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Li X(L, Alahmari A, Schivinski B. Place branding: Religion in shaping the three-dimensional essence of a city brand through stakeholder engagement. PLoS One 2024; 19:e0296162. [PMID: 38261567 PMCID: PMC10805314 DOI: 10.1371/journal.pone.0296162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 12/07/2023] [Indexed: 01/25/2024] Open
Abstract
This study explores the role of religion in engaging stakeholders in branding a place on social media and unmasks what implications this has for (re)constructing the three-dimensional meanings of a place brand. Using the content analysis method to examine the case of Saudi Arabia, it probes how the key stakeholder groups of the government and the residents structure and interact with the narratives of the cities-Jeddah and Riyadh-on Twitter, Facebook, and Instagram. The results show the Islamic religion serves as a powerful tool for motivating the residents to engage in the government-led city branding initiatives at the individual level. However, the strategy of dwelling on religion to mobilize resident engagement at the individual level towards the social level with the aim of growing resources in support of social development should be reassessed within a dynamic social system. Theoretically, the proposed framework of religion city branding expands the scope of stakeholder engagement in place branding research through the integration with the driver of religion, especially unveiling how religious factors shape the personality traits of a place brand. It contributes to the practical sense that religious elements might be deployed by the key stakeholder groups of the government and residents in city branding initiatives, which potentially contributes to their relationship and the engagement of residents in co-creating a place brand with the government. This Saudi-focused study, therefore, possesses significance for place branding practices in Middle Eastern countries and beyond.
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Affiliation(s)
- Xiufang (Leah) Li
- School of Media and Communication, RMIT University, Melbourne, Australia
| | - Abdullah Alahmari
- School of Media and Communication, RMIT University, Melbourne, Australia
- Department of Media and Communication science, Islamic University of Madinah, Madinah, Saudi Arabia
| | - Bruno Schivinski
- School of Media and Communication, RMIT University, Melbourne, Australia
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2
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Heaton D, Nichele E, Clos J, Fischer JE. "The algorithm will screw you": Blame, social actors and the 2020 A Level results algorithm on Twitter. PLoS One 2023; 18:e0288662. [PMID: 37494323 PMCID: PMC10370707 DOI: 10.1371/journal.pone.0288662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 06/30/2023] [Indexed: 07/28/2023] Open
Abstract
In August 2020, the UK government and regulation body Ofqual replaced school examinations with automatically computed A Level grades in England and Wales. This algorithm factored in school attainment in each subject over the previous three years. Government officials initially stated that the algorithm was used to combat grade inflation. After public outcry, teacher assessment grades used instead. Views concerning who was to blame for this scandal were expressed on the social media website Twitter. While previous work used NLP-based opinion mining computational linguistic tools to analyse this discourse, shortcomings included accuracy issues, difficulties in interpretation and limited conclusions on who authors blamed. Thus, we chose to complement this research by analysing 18,239 tweets relating to the A Level algorithm using Corpus Linguistics (CL) and Critical Discourse Analysis (CDA), underpinned by social actor representation. We examined how blame was attributed to different entities who were presented as social actors or having social agency. Through analysing transitivity in this discourse, we found the algorithm itself, the UK government and Ofqual were all implicated as potentially responsible as social actors through active agency, agency metaphor possession and instances of passive constructions. According to our results, students were found to have limited blame through the same analysis. We discuss how this builds upon existing research where the algorithm is implicated and how such a wide range of constructions obscure blame. Methodologically, we demonstrated that CL and CDA complement existing NLP-based computational linguistic tools in researching the 2020 A Level algorithm; however, there is further scope for how these approaches can be used in an iterative manner.
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Affiliation(s)
- Dan Heaton
- School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom
| | - Elena Nichele
- School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom
- Lincoln International Business School, University of Lincoln, Lincoln, Lincolnshire, United Kingdom
| | - Jeremie Clos
- School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom
| | - Joel E Fischer
- School of Computer Science, University of Nottingham, Nottingham, Nottinghamshire, United Kingdom
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3
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Eaton MC, Probst YC, Smith MA. Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis. JMIR INFODEMIOLOGY 2023; 3:e38245. [PMID: 37159259 DOI: 10.2196/38245] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 12/13/2022] [Accepted: 01/10/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Social media has transformed the way health messages are communicated. This has created new challenges and ethical considerations while providing a platform to share nutrition information for communities to connect and for information to spread. However, research exploring the web-based diet communities of popular diets is limited. OBJECTIVE This study aims to characterize the web-based discourse of popular diets, describe information dissemination, identify influential voices, and explore interactions between community networks and themes of mental health. METHODS This exploratory study used Twitter social media posts for an online social network analysis. Popular diet keywords were systematically developed, and data were collected and analyzed using the NodeXL metrics tool (Social Media Research Foundation) to determine the key network metrics (vertices, edges, cluster algorithms, graph visualization, centrality measures, text analysis, and time-series analytics). RESULTS The vegan and ketogenic diets had the largest networks, whereas the zone diet had the smallest network. In total, 31.2% (54/173) of the top users endorsed the corresponding diet, and 11% (19/173) claimed a health or science education, which included 1.2% (2/173) of dietitians. Complete fragmentation and hub and spoke messaging were the dominant network structures. In total, 69% (11/16) of the networks interacted, where the ketogenic diet was mentioned most, with depression and anxiety and eating disorder words most prominent in the "zone diet" network and the least prominent in the "soy-free," "vegan," "dairy-free," and "gluten-free" diet networks. CONCLUSIONS Social media activity reflects diet trends and provides a platform for nutrition information to spread through resharing. A longitudinal exploration of popular diet networks is needed to further understand the impact social media can have on dietary choices. Social media training is vital, and nutrition professionals must work together as a community to actively reshare evidence-based posts on the web.
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Affiliation(s)
- Melissa C Eaton
- School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia
| | - Yasmine C Probst
- School of Medical, Indigenous and Health Sciences, University of Wollongong, Wollongong, Australia
| | - Marc A Smith
- Social Media Research Foundation, Redwood City, CA, United States
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4
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Bhatia S, Jason LA. Using Data Mining and Time Series to Investigate ME and CFS Naming Preferences. JOURNAL OF DISABILITY POLICY STUDIES 2023. [DOI: 10.1177/10442073231154027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
There have been numerous iterations of naming convention specified for Myalgic Encephalomyelitis (ME) and Chronic Fatigue Syndrome (CFS). As health care turns to “big data” analytics to gain insights, the Google Trends database was mined to ascertain worldwide trends of public interest in several ME- and CFS-related search categories between 2004 and 2019. Time series analysis revealed that though “Chronic Fatigue Syndrome” remains the predominant search category in the ME and CFS field, the interest index declined at a rate of 2.77 per month during the 15-year study period. In the same time period, the interest index in “ME/CFS Hybrid” terms increased at a rate of 3.20 per month. Potential causal mechanisms for these trends and implications for patient sentiment analysis are discussed.
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Iparraguirre-Villanueva O, Alvarez-Risco A, Herrera Salazar JL, Beltozar-Clemente S, Zapata-Paulini J, Yáñez JA, Cabanillas-Carbonell M. The Public Health Contribution of Sentiment Analysis of Monkeypox Tweets to Detect Polarities Using the CNN-LSTM Model. Vaccines (Basel) 2023; 11:vaccines11020312. [PMID: 36851190 PMCID: PMC9966732 DOI: 10.3390/vaccines11020312] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
Monkeypox is a rare disease caused by the monkeypox virus. This disease was considered eradicated in 1980 and was believed to affect rodents and not humans. However, recent years have seen a massive outbreak of monkeypox in humans, setting off worldwide alerts from health agencies. As of September 2022, the number of confirmed cases in Peru had reached 1964. Although most monkeypox patients have been discharged, we cannot neglect the monitoring of the population with respect to the monkeypox virus. Lately, the population has started to express their feelings and opinions through social media, specifically Twitter, as it is the most used social medium and is an ideal space to gather what people think about the monkeypox virus. The information imparted through this medium can be in different formats, such as text, videos, images, audio, etc. The objective of this work is to analyze the positive, negative, and neutral feelings of people who publish their opinions on Twitter with the hashtag #Monkeypox. To find out what people think about this disease, a hybrid-based model architecture built on CNN and LSTM was used to determine the prediction accuracy. The prediction result obtained from the total monkeypox data was 83% accurate. Other performance metrics were also used to evaluate the model, such as specificity, recall level, and F1 score, representing 99%, 85%, and 88%, respectively. The results also showed the polarity of feelings through the CNN-LSTM confusion matrix, where 45.42% of people expressed neither positive nor negative opinions, while 19.45% expressed negative and fearful feelings about this infectious disease. The results of this work contribute to raising public awareness about the monkeypox virus.
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Affiliation(s)
| | - Aldo Alvarez-Risco
- Carrera de Negocios Internacionales Facultad de Ciencias Empresariales y Económicas, Universidad de Lima, Lima 15023, Peru
| | - Jose Luis Herrera Salazar
- Facultad de Ingeniería, Ciencias y Administración, Universidad Autónoma de Ica, Chincha Alta 11701, Peru
| | | | | | - Jaime A. Yáñez
- Vicerrectorado de Investigación, Universidad Norbert Wiener, Lima 15046, Peru
- Correspondence: (J.A.Y.); (M.C.-C.)
| | - Michael Cabanillas-Carbonell
- Escuela Técnica Superior de Ingenieros de Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
- Correspondence: (J.A.Y.); (M.C.-C.)
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Heaton D, Clos J, Nichele E, Fischer J. Critical reflections on three popular computational linguistic approaches to examine Twitter discourses. PeerJ Comput Sci 2023; 9:e1211. [PMID: 37346687 PMCID: PMC10280252 DOI: 10.7717/peerj-cs.1211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 12/19/2022] [Indexed: 06/23/2023]
Abstract
Although computational linguistic methods-such as topic modelling, sentiment analysis and emotion detection-can provide social media researchers with insights into online public discourses, it is not inherent as to how these methods should be used, with a lack of transparent instructions on how to apply them in a critical way. There is a growing body of work focusing on the strengths and shortcomings of these methods. Through applying best practices for using these methods within the literature, we focus on setting expectations, presenting trajectories, examining with context and critically reflecting on the diachronic Twitter discourse of two case studies: the longitudinal discourse of the NHS Covid-19 digital contact-tracing app and the snapshot discourse of the Ofqual A Level grade calculation algorithm, both related to the UK. We identified difficulties in interpretation and potential application in all three of the approaches. Other shortcomings, such the detection of negation and sarcasm, were also found. We discuss the need for further transparency of these methods for diachronic social media researchers, including the potential for combining these approaches with qualitative ones-such as corpus linguistics and critical discourse analysis-in a more formal framework.
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Affiliation(s)
- Dan Heaton
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Jeremie Clos
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Elena Nichele
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
| | - Joel Fischer
- School of Computer Science, University of Nottingham, Nottingham, United Kingdom
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7
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Vicente P. Sampling Twitter users for social science research: evidence from a systematic review of the literature. QUALITY & QUANTITY 2023; 57:1-41. [PMID: 36721461 PMCID: PMC9881529 DOI: 10.1007/s11135-023-01615-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/16/2023] [Indexed: 01/29/2023]
Abstract
All social media platforms can be used to conduct social science research, but Twitter is the most popular as it provides its data via several Application Programming Interfaces, which allows qualitative and quantitative research to be conducted with its members. As Twitter is a huge universe, both in number of users and amount of data, sampling is generally required when using it for research purposes. Researchers only recently began to question whether tweet-level sampling-in which the tweet is the sampling unit-should be replaced by user-level sampling-in which the user is the sampling unit. The major rationale for this shift is that tweet-level sampling does not consider the fact that some core discussants on Twitter are much more active tweeters than other less active users, thus causing a sample biased towards the more active users. The knowledge on how to select representative samples of users in the Twitterverse is still insufficient despite its relevance for reliable and valid research outcomes. This paper contributes to this topic by presenting a systematic quantitative literature review of sampling plans designed and executed in the context of social science research in Twitter, including: (1) the definition of the target populations, (2) the sampling frames used to support sample selection, (3) the sampling methods used to obtain samples of Twitter users, (4) how data is collected from Twitter users, (5) the size of the samples, and (6) how research validity is addressed. This review can be a methodological guide for professionals and academics who want to conduct social science research involving Twitter users and the Twitterverse.
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Affiliation(s)
- Paula Vicente
- Business Research Unit (bru_ISCTE), ISCTE-Instituto Universitário de Lisboa, Av. Forças Armadas, 1649-026 Lisboa, Portugal
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8
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Janes EE, Villalovos K, D’Aniello C. #BadTherapist: What TikTok is Saying About Therapy Discontinuation. CONTEMPORARY FAMILY THERAPY 2022. [DOI: 10.1007/s10591-022-09660-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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9
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Massell J, Lieb R, Meyer A, Mayor E. Fluctuations of psychological states on Twitter before and during COVID-19. PLoS One 2022; 17:e0278018. [PMID: 36516149 PMCID: PMC9750014 DOI: 10.1371/journal.pone.0278018] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 11/08/2022] [Indexed: 12/15/2022] Open
Abstract
The COVID-19 pandemic has been repeatedly associated with poor mental health. Previous studies have mostly focused on short time frames such as around the first lockdown periods, and the majority of research is based on self-report questionnaires. Less is known about the fluctuations of psychological states over longer time frames across the pandemic. Twitter timelines of 4,735 users from London and New York were investigated to shed light on potential fluctuations of several psychological states and constructs related to the pandemic. Moving averages are presented for the years 2020 and 2019. Further, mixed negative binomial regression models were fitted to estimate monthly word counts for the time before and during the pandemic. Several psychological states and constructs fluctuated heavily on Twitter during 2020 but not during 2019. Substantial increases in levels of sadness, anxiety, anger, and concerns about home and health were observed around the first lockdown periods in both cities. The levels of most constructs decreased after the initial spike, but negative emotions such as sadness, anxiety, and anger remained elevated throughout 2020 compared to the year prior to the pandemic. Tweets from both cities showed remarkably similar temporal patterns, and there are similarities to reactions found on Twitter following other previous traumatic events.
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Affiliation(s)
- Johannes Massell
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Roselind Lieb
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Andrea Meyer
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland
| | - Eric Mayor
- Division of Clinical Psychology and Epidemiology, Department of Psychology, University of Basel, Basel, Switzerland
- * E-mail:
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10
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Yin R, Wu J, Tian R, Gan F. Topic modeling and sentiment analysis of Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. Front Psychol 2022; 13:1064372. [DOI: 10.3389/fpsyg.2022.1064372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Accepted: 10/18/2022] [Indexed: 11/06/2022] Open
Abstract
The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation (LDA). To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the open-source natural language processing (NLP) technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment (n = 31,661, 34.44%), fear of COVID-19 (n = 22,597, 24.58%), individual characteristic (n = 17,688, 19.24%), government support (n = 15,482, 16.84%), and community effect (n = 4,505, 4.90%). The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic.
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11
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Yin R, Tian R, Wu J, Gan F. Exploring the Factors Associated with Mental Health Attitude in China: A Structural Topic Modeling Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12579. [PMID: 36231878 PMCID: PMC9566640 DOI: 10.3390/ijerph191912579] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 09/28/2022] [Accepted: 09/29/2022] [Indexed: 06/16/2023]
Abstract
Mental health attitude has huge impacts on the improvement of mental health. In response to the ongoing damage the COVID-19 pandemic caused to the mental health of the Chinese people, this study aims to explore the factors associated with mental health attitude in China. To this end, we extract the key topics in mental health-related microblogs on Weibo, the Chinese equivalent of Twitter, using the structural topic modeling (STM) approach. An interaction term of sentiment polarity and time is put into the STM model to track the evolution of public sentiment towards the key topics over time. Through an in-depth analysis of 146,625 Weibo posts, this study captures 12 topics that are, in turn, classified into four factors as stigma (n = 54,559, 37.21%), mental health literacy (n = 32,199, 21.96%), public promotion (n = 30,747, 20.97%), and social support (n = 29,120, 19.86%). The results show that stigma is the primary factor inducing negative mental health attitudes in China as none of the topics related to this factor are considered positive. Mental health literacy, public promotion, and social support are the factors that could enhance positive attitudes towards mental health, since most of the topics related to these factors are identified as positive ones. The provision of tailored strategies for each of these factors could potentially improve the mental health attitudes of the Chinese people.
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Affiliation(s)
- Ruheng Yin
- School of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing 211189, China
| | - Rui Tian
- School of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing 211189, China
| | - Jing Wu
- School of Sociology and Population Studies, Nanjing University of Posts and Telecommunications, Nanjing 211189, China
| | - Feng Gan
- School of Art, Culture and Tourism Industry Think Tank Chinese Art Evaluation Institute, Southeast University, Nanjing 211189, China
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12
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Nguyen HL, Tsolak D, Karmann A, Knauff S, Kühne S. Efficient and Reliable Geocoding of German Twitter Data to Enable Spatial Data Linkage to Official Statistics and Other Data Sources. FRONTIERS IN SOCIOLOGY 2022; 7:910111. [PMID: 35755485 PMCID: PMC9220088 DOI: 10.3389/fsoc.2022.910111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.
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13
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Golder S, Stevens R, O'Connor K, James R, Gonzalez-Hernandez G. Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review. J Med Internet Res 2022; 24:e35788. [PMID: 35486433 PMCID: PMC9107046 DOI: 10.2196/35788] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/08/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background A growing amount of health research uses social media data. Those critical of social media research often cite that it may be unrepresentative of the population; however, the suitability of social media data in digital epidemiology is more nuanced. Identifying the demographics of social media users can help establish representativeness. Objective This study aims to identify the different approaches or combination of approaches to extract race or ethnicity from social media and report on the challenges of using these methods. Methods We present a scoping review to identify methods used to extract the race or ethnicity of Twitter users from Twitter data sets. We searched 17 electronic databases from the date of inception to May 15, 2021, and carried out reference checking and hand searching to identify relevant studies. Sifting of each record was performed independently by at least two researchers, with any disagreement discussed. Studies were required to extract the race or ethnicity of Twitter users using either manual or computational methods or a combination of both. Results Of the 1249 records sifted, we identified 67 (5.36%) that met our inclusion criteria. Most studies (51/67, 76%) have focused on US-based users and English language tweets (52/67, 78%). A range of data was used, including Twitter profile metadata, such as names, pictures, information from bios (including self-declarations), or location or content of the tweets. A range of methodologies was used, including manual inference, linkage to census data, commercial software, language or dialect recognition, or machine learning or natural language processing. However, not all studies have evaluated these methods. Those that evaluated these methods found accuracy to vary from 45% to 93% with significantly lower accuracy in identifying categories of people of color. The inference of race or ethnicity raises important ethical questions, which can be exacerbated by the data and methods used. The comparative accuracies of the different methods are also largely unknown. Conclusions There is no standard accepted approach or current guidelines for extracting or inferring the race or ethnicity of Twitter users. Social media researchers must carefully interpret race or ethnicity and not overpromise what can be achieved, as even manual screening is a subjective, imperfect method. Future research should establish the accuracy of methods to inform evidence-based best practice guidelines for social media researchers and be guided by concerns of equity and social justice.
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Affiliation(s)
- Su Golder
- Department of Health Sciences, University of York, York, United Kingdom
| | - Robin Stevens
- School of Communication and Journalism, University of Southern California, Los Angeles, CA, United States
| | - Karen O'Connor
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Richard James
- School of Nursing Liaison and Clinical Outreach Coordinator, University of Pennsylvania, Philadelphia, PA, United States
| | - Graciela Gonzalez-Hernandez
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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14
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Liu X, Kar B, Montiel Ishino FA, Onega T, Williams F. Racially/ethnically stratified COVID-19 tweets are associated with COVID-19 cases and deaths. JMIR Form Res 2022; 6:e30371. [PMID: 35537056 PMCID: PMC9153911 DOI: 10.2196/30371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 12/29/2021] [Accepted: 04/14/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The Coronavirus Disease 2019 (COVID-19) pandemic exacerbated existing racial/ethnic health disparities in the United States (U.S.). Monitoring nationwide Twitter conversations about COVID-19 and race/ethnicity could shed light on the impact of the pandemic on the racial/ethnic minorities and help address health disparities. OBJECTIVE This paper aims to examine the association between COVID-19 tweet volume and COVID-19 cases and deaths, stratified by race/ethnicity, in the early onset of the pandemic. METHODS This cross-sectional study used geo-tagged COVID-19 tweets from within the U.S. posted in April 2020 on Twitter to examine the association between tweet volume, COVID-19 surveillance data (total cases and deaths in April), and population size. The studied time frame was limited to April 2020 because April was the earliest month when COVID-19 surveillance data on racial/ethnic groups was collected. Racially/ethnically stratified tweets were extracted using racial/ethnic group-related keywords (Asian, Black, Latino, and White) from COVID-19 tweets. Racially/ethnically stratified tweets, COVID-19 cases, and deaths were mapped to reveal their spatial distribution patterns. The ordinary least squares (OLS) regression model was applied to each stratified dataset. RESULTS The racially/ethnically stratified tweet volume was associated with surveillance data. Specifically, the increase of one Asian tweet was correlated to 288 Asian cases (p<0.05) and 93.4 Asian deaths (p<0.05); the increase of one Black tweet was linked to 47.6 Black deaths (p<0.05); the increase of one Latino tweets was linked to 719 Latino deaths (p<0.05); and the increase of one White tweet was linked to 60.2 White deaths (p<0.05). CONCLUSIONS Using racially/ethnically stratified Twitter data as a surveillance indicator could inform epidemiologic trends to help estimate future surges of COVID-19 cases and potential future outbreaks of a pandemic among racial/ethnic groups.
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Affiliation(s)
- Xiaohui Liu
- National Institutes of Health, National Institute on Minority Health and Health Disparities, 6707 Democracy Boulevard, Suite 800, Bethesda, US.,Huntsman Cancer Institute, University of Utah, Salt Lake City, US
| | | | - Francisco Alejandro Montiel Ishino
- National Institutes of Health, National Institute on Minority Health and Health Disparities, 6707 Democracy Boulevard, Suite 800, Bethesda, US
| | - Tracy Onega
- Huntsman Cancer Institute, University of Utah, Salt Lake City, US
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Social Media Big Data Analysis: Towards Enhancing Competitiveness of Firms in a Post-Pandemic World. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:6967158. [PMID: 35281539 PMCID: PMC8913073 DOI: 10.1155/2022/6967158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/05/2022] [Accepted: 02/07/2022] [Indexed: 11/23/2022]
Abstract
In this paper, we proposed an advanced business intelligence framework for firms in a post-pandemic phase to increase their performance and productivity. The proposed framework utilizes some of the most significant tools in this era, such as social media and big data analysis for business intelligence systems. In addition, we survey the most outstanding related papers to this study. Open challenges based on this framework are described as well, and a proposed methodology to minimize these challenges is given. Finally, the conclusion and further research points that are worth studying are discussed.
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16
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Kamila S, Hasanuzzaman M, Ekbal A, Bhattacharyya P. Investigating the impact of emotion on temporal orientation in a deep multitask setting. Sci Rep 2022; 12:493. [PMID: 35017584 PMCID: PMC8752665 DOI: 10.1038/s41598-021-04331-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Accepted: 11/09/2021] [Indexed: 11/09/2022] Open
Abstract
Temporal orientation is an important aspect of human cognition which shows how an individual emphasizes past, present, and future. Theoretical research in psychology shows that one’s emotional state can influence his/her temporal orientation. We hypothesize that measuring human temporal orientation can benefit from concurrent learning of emotion. To test this hypothesis, we propose a deep learning-based multi-task framework where we concurrently learn a unified model for temporal orientation (our primary task) and emotion analysis (secondary task) using tweets. Our multi-task framework takes users’ tweets as input and produces three temporal orientation labels (past, present or future) and four emotion labels (joy, sadness, anger, or fear) with intensity values as outputs. The classified tweets are then grouped for each user to obtain the user-level temporal orientation and emotion. Finally, we investigate the associations between the users’ temporal orientation and their emotional state. Our analysis reveals that joy and anger are correlated to future orientation while sadness and fear are correlated to the past orientation.
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Affiliation(s)
- Sabyasachi Kamila
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India.
| | - Mohammad Hasanuzzaman
- Department of Computer Science, Munster Technological University (Cork Campus), Cork, Ireland
| | - Asif Ekbal
- Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, India.
| | - Pushpak Bhattacharyya
- Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Mumbai, Maharashtra, India
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17
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Bullying-Related Tweets: a Qualitative Examination of Perpetrators, Targets, and Helpers. INTERNATIONAL JOURNAL OF BULLYING PREVENTION : AN OFFICIAL PUBLICATION OF THE INTERNATIONAL BULLYING PREVENTION ASSOCIATION 2022; 4:6-22. [PMID: 34124584 PMCID: PMC8180542 DOI: 10.1007/s42380-021-00098-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/25/2021] [Indexed: 02/08/2023]
Abstract
Bullying literature notes that aside from the dyadic relationship of target and perpetrator, there are other participant roles in the bullying process including those that reinforce the perpetrator and those that stand up for the target. Most examinations of bullying roles have relied on self-reported data, which suffer from key limitations such as response and recall bias. Twitter data provides a way to overcome these limitations and extend our current understanding of bullying roles. The current study provides one of the first qualitative examinations of tweets to analyze the disclosure and sharing of bullying-related online and offline episodes. Through a qualitative content analysis, the study examines 780 tweets to analyze the descriptions and characteristics of three participant roles: the perpetrator, target, and helper. The results provide multidimensional insights into the context and relationships between bullying roles. The results reveal that each of the bullying role players tweet to share varying perspectives and the discussions transcend beyond just online exchanges. The results also confirm that Twitter is used not only as a channel for bullying but also as a tool for connection between the different role players. Implications of how Twitter can be leveraged to promote anti-bullying initiatives to educate and inform users about bullying, while also helping build resilience and emotional regulation, are discussed. Additionally, the study also has implications for artificial intelligence and can help to build improved classifiers to detect bullying-related discourse and content online.
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18
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Yazdiha H, Boen C. "It's a stomachache filled with stress": Tracing the Uneven Spillover Effects of Racialized Police Violence Using Twitter Data. CURRENTS 2022; 2:81-87. [PMID: 35647582 PMCID: PMC9133729 DOI: 10.3998/ncidcurrents.1780] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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19
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Canoy NA, Robles AMQ, Roxas GKT. Bodies-in-waiting as infrastructure: Assembling the Philippine Government's disciplinary quarantine response to COVID-19. Soc Sci Med 2021; 294:114695. [PMID: 34999530 PMCID: PMC8718842 DOI: 10.1016/j.socscimed.2021.114695] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/30/2021] [Accepted: 12/28/2021] [Indexed: 11/17/2022]
Abstract
The purpose of this article is to advance the concept of bodies-in-waiting as an everyday infrastructure to explain the shifting nature of ‘pandemic cities’ in response to the changing dynamics of infection control in urban spaces. While previous literatures have been ‘sanitized’ to emphasize the importance of managing optimal physiological health and safety, we would like to argue that keener attention is needed to rethink the constitutive role of bodies in co-producing a city's sociopolitical ecologies at this time of crisis. The main body is divided into three sections. The first section introduces the political dimensions of pandemic response by various governments with an emphasis to experiences of middle to low income countries. Our intention is to show how these studies bring into light the role of local politics of pandemic response within countries, and that actual governance mechanisms in cities are shaped and contested by shifting power blocs and emergent affinities. The second section forwards an embodied urban political approach that conceptualizes bodies-in-waiting as infrastructure. In this view, bodies-in-waiting is produced and reproduced by complex social-material flows and transformation rooted in variegated matrices of power through which urban spaces are (re)assembled. The last section demonstrates a sample case that shows how bodies-in-waiting as infrastructure are understood using Twitter-sourced data associated with the Philippine government's disciplinary quarantine measures which started March 12, 2020 in the NCR. At its core, bodies-in-waiting as infrastructures populate a politically affirmative urban imaginary of bodies living on despite the existence of an accelerated and mutating virus in slower moving cities.
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Affiliation(s)
- Nico A Canoy
- Department of Psychology, Ateneo de Manila University, Quezon City, Philippines.
| | - Augil Marie Q Robles
- Division of Social Sciences, University of the Philippines Visayas, Miagao, Philippines
| | - Gilana Kim T Roxas
- Department of Psychology, Ateneo de Manila University, Quezon City, Philippines
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20
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Public Perceptions around mHealth Applications during COVID-19 Pandemic: A Network and Sentiment Analysis of Tweets in Saudi Arabia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182413388. [PMID: 34948997 PMCID: PMC8708161 DOI: 10.3390/ijerph182413388] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 12/07/2021] [Accepted: 12/14/2021] [Indexed: 01/12/2023]
Abstract
A series of mitigation efforts were implemented in response to the COVID-19 pandemic in Saudi Arabia, including the development of mobile health applications (mHealth apps) for the public. Assessing the acceptability of mHealth apps among the public is crucial. This study aimed to use Twitter to understand public perceptions around the use of six Saudi mHealth apps used during COVID-19: “Sehha”, “Mawid”, “Sehhaty”, “Tetamman”, “Tawakkalna”, and “Tabaud”. We used two methodological approaches: network and sentiment analysis. We retrieved Twitter data using specific mHealth apps-related keywords. After including relevant tweets, our final mHealth app networks consisted of a total of 4995 Twitter users and 8666 conversational relationships. The largest networks in size (i.e., the number of users) and volume (i.e., the conversational relationships) among all were “Tawakkalna” followed by “Tabaud”, and their conversations were led by diverse governmental accounts. In contrast, the four remaining mHealth networks were mainly led by the health sector and media. Our sentiment analysis approach included five classes and showed that most conversations were neutral, which included facts or information pieces and general inquires. For the automated sentiment classifier, we used Support Vector Machine with AraVec embeddings as it outperformed the other tested classifiers. The sentiment classifier showed an accuracy, precision, recall, and F1-score of 85%. Future studies can use social media and real-time analytics to improve mHealth apps’ services and user experience, especially during health crises.
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21
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Zhou S, Li Y, Chi G, Yin J, Oravecz Z, Bodovski Y, Friedman NP, Vrieze SI, Chow SM. GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. JOURNAL OF BEHAVIORAL DATA SCIENCE 2021; 1:127-155. [PMID: 35281484 PMCID: PMC8915920 DOI: 10.35566/jbds/v1n2/p5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.
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Affiliation(s)
- Shuai Zhou
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yanling Li
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Guangqing Chi
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Junjun Yin
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Zita Oravecz
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yosef Bodovski
- The Pennsylvania State University, University Park, PA 16801, USA
| | | | | | - Sy-Miin Chow
- The Pennsylvania State University, University Park, PA 16801, USA
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22
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Zhou S, Li Y, Chi G, Yin J, Oravecz Z, Bodovski Y, Friedman NP, Vrieze SI, Chow SM. GPS2space: An Open-source Python Library for Spatial Measure Extraction from GPS Data. JOURNAL OF BEHAVIORAL DATA SCIENCE 2021. [PMID: 35281484 DOI: 10.5281/zenodo.4672651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Global Positioning System (GPS) data have become one of the routine data streams collected by wearable devices, cell phones, and social media platforms in this digital age. Such data provide research opportunities in that they may provide contextual information to elucidate where, when, and why individuals engage in and sustain particular behavioral patterns. However, raw GPS data consisting of densely sampled time series of latitude and longitude coordinate pairs do not readily convey meaningful information concerning intra-individual dynamics and inter-individual differences; substantial data processing is required. Raw GPS data need to be integrated into a Geographic Information System (GIS) and analyzed, from which the mobility and activity patterns of individuals can be derived, a process that is unfamiliar to many behavioral scientists. In this tutorial article, we introduced GPS2space, a free and open-source Python library that we developed to facilitate the processing of GPS data, integration with GIS to derive distances from landmarks of interest, as well as extraction of two spatial features: activity space of individuals and shared space between individuals, such as members of the same family. We demonstrated functions available in the library using data from the Colorado Online Twin Study to explore seasonal and age-related changes in individuals' activity space and twin siblings' shared space, as well as gender, zygosity and baseline age-related differences in their initial levels and/or changes over time. We concluded with discussions of other potential usages, caveats, and future developments of GPS2space.
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Affiliation(s)
- Shuai Zhou
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yanling Li
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Guangqing Chi
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Junjun Yin
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Zita Oravecz
- The Pennsylvania State University, University Park, PA 16801, USA
| | - Yosef Bodovski
- The Pennsylvania State University, University Park, PA 16801, USA
| | | | | | - Sy-Miin Chow
- The Pennsylvania State University, University Park, PA 16801, USA
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23
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Peretz H, Fried Y, Parry E. Generations in context: The development of a new approach using Twitter and a survey. JOURNAL OF OCCUPATIONAL AND ORGANIZATIONAL PSYCHOLOGY 2021. [DOI: 10.1111/joop.12376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Hilla Peretz
- Ort Braude Academic College of Engineering Karmiel Israel
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24
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Artificial neural networks applied for predicting and explaining the education level of Twitter users. SOCIAL NETWORK ANALYSIS AND MINING 2021; 11:112. [PMID: 34745380 PMCID: PMC8558764 DOI: 10.1007/s13278-021-00832-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 10/07/2021] [Accepted: 10/17/2021] [Indexed: 11/21/2022]
Abstract
This paper provides a novel procedure to estimate the education level of social network (SN) users by leveraging artificial neural networks (ANN). Additionally, it provides a robust methodology to extract explanatory insights from ANN models. It also contributes to the study of socio-demographic phenomena by utilizing less explored data sources, such as social media. It proposes Twitter data as an alternative data source for in-depth social studies, and ANN for complex patterns recognition. Moreover, cutting edge technology, such as face recognition, on social media data are applied to explain the social characteristics of country-specific users. We use nine variables and three hidden layers of neurons to identify high-skilled users. The resulted model describes well the level of education by correctly estimating it with an accuracy of 95% on the training set and an accuracy of 92% on a testing set. Approximately 30% of the analyzed users are highly skilled and this share does not differ among the two genders. However, it tends to be lower among users younger than 30 years old.
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25
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Yao Z, Yang J, Liu J, Keith M, Guan C. Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19. CITIES (LONDON, ENGLAND) 2021; 116:103273. [PMID: 36540864 PMCID: PMC9756302 DOI: 10.1016/j.cities.2021.103273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 03/08/2021] [Accepted: 05/20/2021] [Indexed: 05/07/2023]
Abstract
COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people's sentiments during this pandemic. By conducting sentiment analysis on Tweets using advanced machine learning techniques, this study aims to investigate how public sentiments respond to the pandemic from March 2 to May 21, 2020 in New York City, Los Angeles, London, and another six global mega-cities. Results showed that across cities, negative and positive Tweet sentiment clustered around mid-March and early May, respectively. Furthermore, positive sentiments of Tweets from New York City and London were positively correlated with stricter quarantine measures, although this correlation was not significant in Los Angeles. Meanwhile, Tweet sentiments of all three cities did not exhibit a strong correlation with new cases and hospitalization. Last but not least, we provide a qualitative analysis of the reasons behind differences in correlations shown above, along with a discussion of the polarizing effect of public policies on Tweet sentiments. Thus, the results of this study imply that Tweet sentiment is more sensitive to quarantine orders than reported statistics of COVID-19, especially in populous megacities where public transportation is heavily relied upon, which calls for prompt and effective quarantine measures during contagious disease outbreaks.
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Affiliation(s)
- Zhirui Yao
- Arts and Science, New York University Shanghai, China
- Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, China
| | - Junyan Yang
- Department of Urban Planning, Southeast University, China
| | - Jialin Liu
- Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, China
| | - Michael Keith
- PEAK Urban Programme, University of Oxford, United Kingdom
| | - ChengHe Guan
- Arts and Science, New York University Shanghai, China
- Shanghai Key Laboratory of Urban Renewal and Spatial Optimization Technology, Tongji University, China
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26
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Misra P, Gupta J. Impact of COVID 19 on Indian Migrant Workers: Decoding Twitter Data by Text Mining. THE INDIAN JOURNAL OF LABOUR ECONOMICS : THE QUARTERLY JOURNAL OF THE INDIAN SOCIETY OF LABOUR ECONOMICS 2021; 64:731-747. [PMID: 34305343 PMCID: PMC8294302 DOI: 10.1007/s41027-021-00324-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/23/2021] [Indexed: 06/13/2023]
Abstract
The Coronavirus pandemic has induced a huge economic crisis. The norms of social distancing and consequent lockdown to flatten the curve of this infection has brought economic activity across the globe to a standstill. A mass exodus of workers from major urban centres of India to their native villages started. Mental, financial and emotional agony inflicted due to job-loss, lack of job and livelihood opportunities led to this. A massive macroeconomic crisis for the country with serious ramifications has consequently exploded. The present study explores and captures the diffusion and discovery of information about the various facets of reverse migration in India using Twitter mining. Tweets provide extensive opportunities to extract social perceptions and insights relevant to migration of workers. The massive Twitter data were analysed by applying text mining technique and sentiment analysis. The results of the analysis highlight five major themes. The sentiment analysis confirms the confidence and trust in the minds of masses about tiding through this crisis with government support. The study brings out the major macroeconomic ramifications of this reverse migration. The study's findings indicate that a concentrated joint intervention by the State and Central Governments is critical for successfully tiding through this crisis and restoring normalcy. The subsequent policy measures announced by the government are being critically gauged. In addition, the authors have proposed measures to ameliorate this damage on the formal and informal sectors.
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Affiliation(s)
- Pooja Misra
- Birla Institute of Management Technology, Plot No. 5, Knowledge Park – II, Greater Noida, 201 306 India
| | - Jaya Gupta
- Birla Institute of Management Technology, Plot No. 5, Knowledge Park – II, Greater Noida, 201 306 India
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27
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Abstract
During the time of the coronavirus, strict prevention policies, social distancing, and limited contact with others were enforced in Greece. As a result, Twitter and other social media became an important place of interaction, and conversation became online. The aim of this study is to examine Twitter discussions around COVID-19 in Greece. Twitter was chosen because of the critical role it played during the global health crisis. Tweets were recorded over four time periods. NodeXL Pro was used to identify word pairs, create semantic networks, and analyze them. A lexicon-based sentiment analysis was also performed. The main topics of conversation were extracted. “New cases” are heavily discussed throughout, showing fear of transmission of the virus in the community. Mood analysis showed fluctuations in mood over time. Positive emotions weakened and negative emotions increased. Fear is the dominant sentiment. Timely knowledge of people’s sentiment can be valuable for government agencies to develop efficient strategies to better manage the situation and use efficient communication guidelines in Twitter to disseminate accurate, reliable information and control panic.
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28
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Fütterer T, Hoch E, Stürmer K, Lachner A, Fischer C, Scheiter K. [Concerns of teachers during school closings: analyzing communication in the twitter-lehrerzimmer regarding opportunities and challenges of digital teaching]. ZEITSCHRIFT FUR ERZIEHUNGSWISSENSCHAFT : ZFE 2021; 24:443-477. [PMID: 33824621 PMCID: PMC8015751 DOI: 10.1007/s11618-021-01013-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 01/14/2021] [Accepted: 03/12/2021] [Indexed: 06/12/2023]
Abstract
Many schools introduced distance learning as a result of the school closings due to the Corona pandemic in March 2020. Such instruction was often organized digitally without much prior preparation. As a result, an increased exchange between teachers in online communities was to be expected. Analyzing the communication of the online community Twitter-Lehrerzimmer provided insight into topics and allowed to compare topics that were discussed before and during school closings. To identify topics, we applied computational linguistic analysis methods on 128,422 tweets and qualitative content analysis of 270 tweets. The results indicated that topics such as (a)synchronous digital teaching had already been discussed previously but was addressed more often and in more breadth during school closings. The Twitter-Lehrerzimmer was used for mutual support and exchange on urgent challenges such as the availability of high-quality software (compliant with data protection). The results reveal deficits in the German digitalization process from the perspective of teachers using Twitter and show the potential of online communities for information exchange and networking.
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Affiliation(s)
- Tim Fütterer
- Hector-Institut für Empirische Bildungsforschung, Universität Tübingen, Europastraße 6, 72072 Tübingen, Deutschland
| | - Emely Hoch
- Multiple Repräsentationen, Leibniz-Institut für Wissensmedien, Tübingen, Deutschland
| | - Kathleen Stürmer
- Hector-Institut für Empirische Bildungsforschung, Universität Tübingen, Europastraße 6, 72072 Tübingen, Deutschland
| | - Andreas Lachner
- Institut für Erziehungswissenschaft, Universität Tübingen, Tübingen, Deutschland
| | - Christian Fischer
- Hector-Institut für Empirische Bildungsforschung, Universität Tübingen, Europastraße 6, 72072 Tübingen, Deutschland
| | - Katharina Scheiter
- Multiple Repräsentationen, Leibniz-Institut für Wissensmedien, Tübingen, Deutschland
- Fachbereich Psychologie, Universität Tübingen, Tübingen, Deutschland
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29
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Brown ME, Dustman PA, Barthelemy JJ. Twitter impact on a community trauma: An examination of who, what, and why it radiated. JOURNAL OF COMMUNITY PSYCHOLOGY 2021; 49:838-853. [PMID: 32058589 DOI: 10.1002/jcop.22330] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 01/23/2020] [Accepted: 02/03/2020] [Indexed: 06/10/2023]
Abstract
The study examined the radiating impacts of trauma following the officer-involved shooting of Alton Sterling. Twitter data (#AltonSterling) was collected, filtered, and analyzed using textual and spatial methods. Primary coding encompassed the 30-day period immediately following the shooting. In general, tweets were not used to convey either facts or neutral information, rather, personal opinions dominated. The immediate responses were largely grounded in fear and/or violence. One particularly illuminating finding was the absence of messaging and silence from local leadership. Social media can be a tool to either provide consolatory messaging to promote healing and health, or to spread inflammatory exchanges that perpetuate community discord, further fracture communities and groups, and elevate the risk of retraumatization. Local organizations need established protocols for using social media proactively in the aftermath of community trauma; social media can be a powerful tool for enhancing community cohesion, recovery, and resilience.
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Affiliation(s)
- Mary Ellen Brown
- School of Social Work, Arizona State University, Phoenix, Arizona
| | - Patricia A Dustman
- Southwest Interdisciplinary Research Center, Arizona State University, Phoenix, Arizona
| | - Juan J Barthelemy
- Graduate College of Social Work, University of Houston, Houston, Texas
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30
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Using Twitter to track immigration sentiment during early stages of the COVID-19 pandemic. DATA & POLICY 2021. [DOI: 10.1017/dap.2021.38] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
Abstract
Large-scale coordinated efforts have been dedicated to understanding the global health and economic implications of the COVID-19 pandemic. Yet, the rapid spread of discrimination and xenophobia against specific populations has largely been neglected. Understanding public attitudes toward migration is essential to counter discrimination against immigrants and promote social cohesion. Traditional data sources to monitor public opinion are often limited, notably due to slow collection and release activities. New forms of data, particularly from social media, can help overcome these limitations. While some bias exists, social media data are produced at an unprecedented temporal frequency, geographical granularity, are collected globally and accessible in real-time. Drawing on a data set of 30.39 million tweets and natural language processing, this article aims to measure shifts in public sentiment opinion about migration during early stages of the COVID-19 pandemic in Germany, Italy, Spain, the United Kingdom, and the United States. Results show an increase of migration-related Tweets along with COVID-19 cases during national lockdowns in all five countries. Yet, we found no evidence of a significant increase in anti-immigration sentiment, as rises in the volume of negative messages are offset by comparable increases in positive messages. Additionally, we presented evidence of growing social polarization concerning migration, showing high concentrations of strongly positive and strongly negative sentiments.
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31
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Browning CR, Pinchak NP, Calder CA. Human Mobility and Crime: Theoretical Approaches and Novel Data Collection Strategies. ANNUAL REVIEW OF CRIMINOLOGY 2021; 4:99-123. [PMID: 37559706 PMCID: PMC10409625 DOI: 10.1146/annurev-criminol-061020-021551] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/11/2023]
Abstract
This review outlines approaches to explanations of crime that incorporate the concept of human mobility-or the patterns of movement throughout space of individuals or populations in the context of everyday routines-with a focus on novel strategies for the collection of geographically referenced data on mobility patterns. We identify three approaches to understanding mobility-crime linkages: (a) Place and neighborhood approaches characterize local spatial units of analysis of varying size with respect to the intersection in space and time of potential offenders, victims, and guardians; (b) person-centered approaches emphasize the spatial trajectories of individuals and person-place interactions that influence crime risk; and (c) ecological network approaches consider links between persons or collectivities based on shared activity locations, capturing influences of broader systems of interconnection on spatial- and individual-level variation in crime. We review data collection strategies for the measurement of mobility across these approaches, considering both the challenges and promise of mobility-based research for criminology.
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Affiliation(s)
| | - Nicolo P Pinchak
- Department of Sociology, The Ohio State University, Columbus, Ohio 43210, USA
| | - Catherine A Calder
- Department of Statistics and Data Sciences, The University of Texas at Austin, Austin, Texas 78712, USA
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Pickering CM, Norman P. Assessing discourses about controversial environmental management issues on social media: Tweeting about wild horses in a national park. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 275:111244. [PMID: 32841789 DOI: 10.1016/j.jenvman.2020.111244] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 08/06/2020] [Accepted: 08/15/2020] [Indexed: 06/11/2023]
Abstract
Public participation is critical for planning and management of protected areas. With people increasingly using social media, including Twitter, to obtain news and express opinions, park agencies should recognize the utility of monitoring and engaging with this public discourse. We used a conservation culturomics approach to analyse Tweets during a period of controversy about the management of large mammals (horses) in a park (Kosciuszko National Park in Australia), including examining who talked about what, when and what emotions were expressed. An automated programming interface was used to collect metadata for Tweets about the Park, with keywords coded while sentiments and emotions were analysed using a standard lexicon of terms. The debate over introduced wild/feral horses in the Park dominated the discourse, accounting for 56% of the 2085 Tweets referring by name to the Park over 275 days. Many Tweets referred to horses (44.8%) and/or used the alternative term, brumbies (15%). They were more likely to be Retweets, be sent by Australians, with a potential reach of over 5 million followers. Peaks in Tweets related to specific events in the news, with Tweets sent by journalists and others in a professional capacity or specific organisations engaged in the debate more likely to be retweeted. Despite considerable polarisation in the broader debate, including in the traditional media and on other social media platforms, the discourse on Twitter focused mainly on the environmental impacts of horses, and ways to reduce their numbers, rather than wanting to keep horses in the Park. There are important issues with the use of Twitter data including challenges in identifying relevant Tweets, biases in who Tweets and what is retweeted, limited text in Tweets, and increasing focus of the rights to privacy online. Twitter, however, remains a relatively fast, timely and often free way to listen into public debate with a large potential audience, is simple to analyse and hence provides valuable insights into public reactions to park management decisions complementing other data sources.
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Affiliation(s)
| | - Patrick Norman
- Environment Futures Research Institute, Griffith University, Gold Coast, 4222, Australia.
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Little RJA, West BT, Boonstra PS, Hu J. Measures of the Degree of Departure from Ignorable Sample Selection. JOURNAL OF SURVEY STATISTICS AND METHODOLOGY 2020; 8:932-964. [PMID: 33381610 PMCID: PMC7750890 DOI: 10.1093/jssam/smz023] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
With the current focus of survey researchers on "big data" that are not selected by probability sampling, measures of the degree of potential sampling bias arising from this nonrandom selection are sorely needed. Existing indices of this degree of departure from probability sampling, like the R-indicator, are based on functions of the propensity of inclusion in the sample, estimated by modeling the inclusion probability as a function of auxiliary variables. These methods are agnostic about the relationship between the inclusion probability and survey outcomes, which is a crucial feature of the problem. We propose a simple index of degree of departure from ignorable sample selection that corrects this deficiency, which we call the standardized measure of unadjusted bias (SMUB). The index is based on normal pattern-mixture models for nonresponse applied to this sample selection problem and is grounded in the model-based framework of nonignorable selection first proposed in the context of nonresponse by Don Rubin in 1976. The index depends on an inestimable parameter that measures the deviation from selection at random, which ranges between the values zero and one. We propose the use of a central value of this parameter, 0.5, for computing a point index, and computing the values of SMUB at zero and one to provide a range of the index in a sensitivity analysis. We also provide a fully Bayesian approach for computing credible intervals for the SMUB, reflecting uncertainty in the values of all of the input parameters. The proposed methods have been implemented in R and are illustrated using real data from the National Survey of Family Growth.
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Affiliation(s)
- Roderick J A Little
- Professor of Biostatistics at the School of Public Health and Research Professor in the Survey Methodology Program (SMP), Survey Research Center (SRC), Institute for Social Research (ISR), University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109-2029, USA
| | - Brady T West
- Research Associate Professor in the Survey Methodology Program (SMP), Survey Research Center (SRC), Institute for Social Research (ISR), University of Michigan, 426 Thompson Street, Ann Arbor, MI 48106-1248, USA
| | - Philip S Boonstra
- Research Assistant Professor of Biostatistics in the School of Public Health, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA
| | - Jingwei Hu
- Survey Research Director at SurveyPlus Ltd., 1079 Nanhai Street, Shuma Building 201A, Shenzhen, Guangdong 518023, China
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Liu X, Kar B, Montiel Ishino FA, Zhang C, Williams F. Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020; 9:532. [PMID: 33511044 PMCID: PMC7839990 DOI: 10.3390/ijgi9090532] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms.
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Affiliation(s)
- Xiaohui Liu
- National Institute on Minority and Health Disparities, National Institutes of Health, Bethesda, MD 20814, USA
| | - Bandana Kar
- National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | | | - Chaoyang Zhang
- School of Computing, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, USA
| | - Faustine Williams
- National Institute on Minority and Health Disparities, National Institutes of Health, Bethesda, MD 20814, USA
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Edelmann A, Wolff T, Montagne D, Bail CA. Computational Social Science and Sociology. ANNUAL REVIEW OF SOCIOLOGY 2020; 46:61-81. [PMID: 34824489 PMCID: PMC8612450 DOI: 10.1146/annurev-soc-121919-054621] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The integration of social science with computer science and engineering fields has produced a new area of study: computational social science. This field applies computational methods to novel sources of digital data such as social media, administrative records, and historical archives to develop theories of human behavior. We review the evolution of this field within sociology via bibliometric analysis and in-depth analysis of the following subfields where this new work is appearing most rapidly: (a) social network analysis and group formation; (b) collective behavior and political sociology; (c) the sociology of knowledge; (d) cultural sociology, social psychology, and emotions; (e) the production of culture; (f) economic sociology and organizations; and (g) demography and population studies. Our review reveals that sociologists are not only at the center of cutting-edge research that addresses longstanding questions about human behavior but also developing new lines of inquiry about digital spaces as well. We conclude by discussing challenging new obstacles in the field, calling for increased attention to sociological theory, and identifying new areas where computational social science might be further integrated into mainstream sociology.
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Affiliation(s)
- Achim Edelmann
- Institute of Sociology, University of Bern, 3012 Bern, Switzerland
- Department of Sociology, London School of Economics and Political Science, London WC2A 2AE, United Kingdom
| | - Tom Wolff
- Department of Sociology, Duke University, Durham, North Carolina 27708, USA
| | - Danielle Montagne
- Department of Sociology, Duke University, Durham, North Carolina 27708, USA
| | - Christopher A Bail
- Department of Sociology, Duke University, Durham, North Carolina 27708, USA
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36
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Fecht D, Cockings S, Hodgson S, Piel FB, Martin D, Waller LA. Advances in mapping population and demographic characteristics at small-area levels. Int J Epidemiol 2020; 49 Suppl 1:i15-i25. [PMID: 32293009 PMCID: PMC7158058 DOI: 10.1093/ije/dyz179] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2019] [Accepted: 08/09/2019] [Indexed: 11/30/2022] Open
Abstract
Temporally and spatially highly resolved information on population characteristics, including demographic profile (e.g. age and sex), ethnicity and socio-economic status (e.g. income, occupation, education), are essential for observational health studies at the small-area level. Time-relevant population data are critical as denominators for health statistics, analytics and epidemiology, to calculate rates or risks of disease. Demographic and socio-economic characteristics are key determinants of health and important confounders in the relationship between environmental contaminants and health. In many countries, census data have long been the source of small-area population denominators and confounder information. A strength of the traditional census model has been its careful design and high level of population coverage, allowing high-quality detailed data to be released for small areas periodically, e.g. every 10 years. The timeliness of data, however, becomes a challenge when temporally and spatially highly accurate annual (or even more frequent) data at high spatial resolution are needed, for example, for health surveillance and epidemiological studies. Additionally, the approach to collecting demographic population information is changing in the era of open and big data and may eventually evolve to using combinations of administrative and other data, supplemented by surveys. We discuss different approaches to address these challenges including (i) the US American Community Survey, a rolling sample of the US population census, (ii) the use of spatial analysis techniques to compile temporally and spatially high-resolution demographic data and (iii) the use of administrative and big data sources as proxies for demographic characteristics.
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Affiliation(s)
- Daniela Fecht
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - Samantha Cockings
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Susan Hodgson
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - Frédéric B Piel
- UK Small Area Health Statistics Unit, MRC-PHE Centre for Environment and Health, Imperial College London, St Mary’s Campus, London, UK
| | - David Martin
- School of Geography and Environmental Science, University of Southampton, Southampton, UK
| | - Lance A Waller
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
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37
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Myintzaw P, Moran F, Jaiswal AK. Campylobacteriosis, consumer's risk perception, and knowledge associated with domestic poultry handling in Ireland. J Food Saf 2020. [DOI: 10.1111/jfs.12799] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Peter Myintzaw
- School of Food Science and Environmental HealthCollege of Sciences and Health, Technological University Dublin—City Campus Dublin Ireland
| | - Fintan Moran
- School of Food Science and Environmental HealthCollege of Sciences and Health, Technological University Dublin—City Campus Dublin Ireland
| | - Amit K. Jaiswal
- School of Food Science and Environmental HealthCollege of Sciences and Health, Technological University Dublin—City Campus Dublin Ireland
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38
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Stone SM. Whose Play Scripts Are Being Published? A Diversity Audit of One Library’s Collection in Conversation with the Broader Play Publishing World. COLLECTION MANAGEMENT 2020. [DOI: 10.1080/01462679.2020.1715314] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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39
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Giuffrida L, Lokys H, Klemm O. Assessing the effect of weather on human outdoor perception using Twitter. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2020; 64:205-216. [PMID: 29992355 DOI: 10.1007/s00484-018-1574-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 01/22/2018] [Accepted: 06/18/2018] [Indexed: 06/08/2023]
Abstract
Human comfort in outdoor spaces (HCOS) is linked to people's psychological responses to environmental variables. Previous studies have established comfort ranges for these variables through interviews and questionnaires, reaching only limited populations. However, larger amounts of data would not only generate more robust results in local studies, but it would also allow for the possibility of creating an approach that could be applied to a wider range of weather conditions and different climates. Therefore, this study describes a new methodology to assess people's perception of weather based on human responses to weather conditions extracted from tweets, with the purpose of establishing comfort ranges for environmental variables. Tweets containing weather-associated keywords were collected, stored, and then linked to real-time meteorological data acquired nearby the locations in which the tweets were posted. Afterwards, people's perception of weather was extracted from the tweets using a classifier trained specifically on weather data that identified irrelevant, neutral, positive, and negative tweets. The obtained tweets and their related atmospheric data were analyzed to establish comfort ranges. The tweets' responses to effective temperature were very similar to those obtained in previous studies, although the peak of comfort is shifted towards the cold stress. Similarly, the tweets' responses to the thermohygrometric index were alike to previous results, but the peak of comfort is shifted towards the heat stress. Regarding the single weather variables under study, the obtained comfort ranges are similar to the ones found in previous research; in particular, the temperature comfort range matches perfectly at 20-22 °C. Therefore, it was concluded that tweets can be used to assess HCOS; not only are the results of this methodology comparable to results obtained in previous studies, but the procedure itself also shows new features and unexpected future applications.
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Affiliation(s)
- Laura Giuffrida
- Institute of Landscape Ecology - Climatology Group, Westfälische Wilhelms-Universität Münster, Heisenbergstr. 2, 48149, Münster, Germany.
| | - Hanna Lokys
- Institute of Landscape Ecology - Climatology Group, Westfälische Wilhelms-Universität Münster, Heisenbergstr. 2, 48149, Münster, Germany
| | - Otto Klemm
- Institute of Landscape Ecology - Climatology Group, Westfälische Wilhelms-Universität Münster, Heisenbergstr. 2, 48149, Münster, Germany
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40
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Andridge RR, West BT, Little RJA, Boonstra PS, Alvarado-Leiton F. Indices of non-ignorable selection bias for proportions estimated from non-probability samples. J R Stat Soc Ser C Appl Stat 2019; 68:1465-1483. [PMID: 33304001 PMCID: PMC7724611 DOI: 10.1111/rssc.12371] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.
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41
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Greshake Tzovaras B, Angrist M, Arvai K, Dulaney M, Estrada-Galiñanes V, Gunderson B, Head T, Lewis D, Nov O, Shaer O, Tzovara A, Bobe J, Price Ball M. Open Humans: A platform for participant-centered research and personal data exploration. Gigascience 2019; 8:giz076. [PMID: 31241153 PMCID: PMC6593360 DOI: 10.1093/gigascience/giz076] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Revised: 05/02/2019] [Accepted: 06/03/2019] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Many aspects of our lives are now digitized and connected to the internet. As a result, individuals are now creating and collecting more personal data than ever before. This offers an unprecedented chance for human-participant research ranging from the social sciences to precision medicine. With this potential wealth of data comes practical problems (e.g., how to merge data streams from various sources), as well as ethical problems (e.g., how best to balance risks and benefits when enabling personal data sharing by individuals). RESULTS To begin to address these problems in real time, we present Open Humans, a community-based platform that enables personal data collections across data streams, giving individuals more personal data access and control of sharing authorizations, and enabling academic research as well as patient-led projects. We showcase data streams that Open Humans combines (e.g., personal genetic data, wearable activity monitors, GPS location records, and continuous glucose monitor data), along with use cases of how the data facilitate various projects. CONCLUSIONS Open Humans highlights how a community-centric ecosystem can be used to aggregate personal data from various sources, as well as how these data can be used by academic and citizen scientists through practical, iterative approaches to sharing that strive to balance considerations with participant autonomy, inclusion, and privacy.
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Affiliation(s)
- Bastian Greshake Tzovaras
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
- Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
| | - Misha Angrist
- Social Science Research Institute, Duke University, 140 Science Drive, Durham, NC 27708, USA
| | | | - Mairi Dulaney
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
| | - Vero Estrada-Galiñanes
- QoL Lab, Department of ComputerScience, University of Copenhagen, Sigurdsgade 41, DK-2200 Copenhagen, Denmark
- IDE, University of Stavanger, Kjell Arholmsgate 41, 4036 Stavanger, Norway
| | | | - Tim Head
- Wild Tree Tech, Froehlichstrasse 42 5200 Brugg Switzerland
| | | | - Oded Nov
- Tandon School of Engineering, New York University, 6 MetroTech Center, Brooklyn, NY 11201, USA
| | - Orit Shaer
- Wellesley College, 106 Central Street – Wellesley, MA 02481, USA
| | - Athina Tzovara
- Helen Wills Neuroscience Institute, University of California, Berkeley 174 Li Ka Shing Center, Berkeley, CA 94720, USA
- Institute of Computer Science, University of Bern, Neubrückstrasse 10, 3012 Bern, Switzerland
| | - Jason Bobe
- Institute for Next Generation Healthcare, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place New York, NY 10029-5674, USA
| | - Mad Price Ball
- Open Humans Foundation, 500 Westover Dr #10553, Sanford, NC, 27330, USA
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Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics J 2019; 26:765-786. [PMID: 30969146 DOI: 10.1177/1460458219839621] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Sexual and gender minorities face extreme challenges that breed stigma with alarming consequences damaging their mental health. Nevertheless, sexual and gender minority people and their mental health needs remain little understood. Because of stigma, sexual and gender minorities are often unwilling to self-identify themselves as sexual and gender minorities when asked. However, social media have become popular platforms for health-related researches. We first explored methods to find sexual and gender minorities through their self-identifying tweets, and further classified them into 11 sexual and gender minority subgroups. We then analyzed mental health signals extracted from these sexual and gender minorities' Twitter timelines using a lexicon-based analysis method. We found that (1) sexual and gender minorities expressed more negative feelings, (2) the difference between sexual and gender minority and non-sexual and gender minority people is shrinking after 2015, (3) there are differences among sexual and gender minorities lived in different geographic regions, (4) sexual and gender minorities lived in states with sexual and gender minority-related protection laws and policies expressed more positive emotions, and (5) sexual and gender minorities expressed different levels of mental health signals across different sexual and gender minority subgroups.
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43
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Sparks CS, Joyner K. Population Research Briefs in Population Research and Policy Review. POPULATION RESEARCH AND POLICY REVIEW 2019. [DOI: 10.1007/s11113-019-09522-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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44
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Cesare N, Lee H, McCormick T, Spiro E, Zagheni E. Promises and Pitfalls of Using Digital Traces for Demographic Research. Demography 2019; 55:1979-1999. [PMID: 30276667 DOI: 10.1007/s13524-018-0715-2] [Citation(s) in RCA: 49] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
The digital traces that we leave online are increasingly fruitful sources of data for social scientists, including those interested in demographic research. The collection and use of digital data also presents numerous statistical, computational, and ethical challenges, motivating the development of new research approaches to address these burgeoning issues. In this article, we argue that researchers with formal training in demography-those who have a history of developing innovative approaches to using challenging data-are well positioned to contribute to this area of work. We discuss the benefits and challenges of using digital trace data for social and demographic research, and we review examples of current demographic literature that creatively use digital trace data to study processes related to fertility, mortality, and migration. Focusing on Facebook data for advertisers-a novel "digital census" that has largely been untapped by demographers-we provide illustrative and empirical examples of how demographic researchers can manage issues such as bias and representation when using digital trace data. We conclude by offering our perspective on the road ahead regarding demography and its role in the data revolution.
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Affiliation(s)
- Nina Cesare
- Department of Global Health, Boston University, Boston, MA, USA.
| | - Hedwig Lee
- Department of Sociology, Washington University, St. Louis, MO, USA
| | - Tyler McCormick
- Department of Sociology, University of Washington, Seattle, WA, USA.,Department of Statistics, University of Washington, Seattle, WA, USA
| | - Emma Spiro
- Department of Sociology, Washington University, St. Louis, MO, USA.,Information School, University of Washington, Seattle, WA, USA
| | - Emilio Zagheni
- Max Planck Institute for Demographic Research, Rostock, Germany
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45
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Hswen Y, Naslund JA, Brownstein JS, Hawkins JB. Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study. JMIR Ment Health 2018; 5:e11483. [PMID: 30545811 PMCID: PMC6315229 DOI: 10.2196/11483] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2018] [Revised: 09/13/2018] [Accepted: 09/14/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND People with schizophrenia experience elevated risk of suicide. Mental health symptoms, including depression and anxiety, contribute to increased risk of suicide. Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts. OBJECTIVE This exploratory study examined the feasibility of monitoring online discussions about suicide among Twitter users who self-identify as having schizophrenia. METHODS Posts containing the terms suicide or suicidal were collected from a sample of Twitter users who self-identify as having schizophrenia (N=203) and a random sample of control users (N=173) over a 200-day period. Frequency and timing of posts about suicide were compared between groups. The associations between posting about suicide and common mental health symptoms were examined. RESULTS Twitter users who self-identify as having schizophrenia posted more tweets about suicide (mean 7.10, SD 15.98) compared to control users (mean 1.89, SD 4.79; t374=-4.13, P<.001). Twitter users who self-identify as having schizophrenia showed greater odds of tweeting about suicide compared to control users (odds ratio 2.15, 95% CI 1.42-3.28). Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001). CONCLUSIONS Twitter users who self-identify as having schizophrenia appear to commonly discuss suicide on social media, which is associated with greater discussion about other mental health symptoms. These findings should be interpreted cautiously, as it is not possible to determine whether online discussions about suicide correlate with suicide risk. However, these patterns of online discussion may be indicative of elevated risk of suicide observed in this patient group. There may be opportunities to leverage social media for supporting suicide prevention among individuals with schizophrenia.
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Affiliation(s)
- Yulin Hswen
- Department of Social and Behavioral Sciences, Harvard TH Chan School of Public Health, Harvard University, Boston, MA, United States.,Informatics Program, Boston Children's Hospital, Boston, MA, United States
| | - John A Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, United States
| | - John S Brownstein
- Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
| | - Jared B Hawkins
- Informatics Program, Boston Children's Hospital, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States
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46
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Leypunskiy E, Kıcıman E, Shah M, Walch OJ, Rzhetsky A, Dinner AR, Rust MJ. Geographically Resolved Rhythms in Twitter Use Reveal Social Pressures on Daily Activity Patterns. Curr Biol 2018; 28:3763-3775.e5. [PMID: 30449672 PMCID: PMC6590897 DOI: 10.1016/j.cub.2018.10.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/22/2018] [Accepted: 10/04/2018] [Indexed: 12/31/2022]
Abstract
Daily rhythms in human physiology and behavior are driven by the interplay of circadian rhythms, environmental cycles, and social schedules. Much research has focused on the mechanism and function of circadian rhythms in constant conditions or in idealized light-dark environments. There have been comparatively few studies into how social pressures, such as work and school schedules, affect human activity rhythms day to day and season to season. To address this issue, we analyzed activity on Twitter in >1,500 US counties throughout the 2012-2013 calendar years in 15-min intervals using geographically tagged tweets representing ≈0.1% of the total population each day. We find that sustained periods of low Twitter activity are correlated with sufficient sleep as measured by conventional surveys. We show that this nighttime lull in Twitter activity is shifted to later times on weekends relative to weekdays, a phenomenon we term "Twitter social jet lag." The magnitude of this social jet lag varies seasonally and geographically-with the West Coast experiencing less Twitter social jet lag compared to the Central and Eastern US-and is correlated with average commuting schedules and disease risk factors such as obesity. Most counties experience the largest amount of Twitter social jet lag in February and the lowest in June or July. We present evidence that these shifts in weekday activity coincide with relaxed social pressures due to local K-12 school holidays and that the direct seasonal effect of altered day length is comparatively weaker.
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Affiliation(s)
- Eugene Leypunskiy
- Graduate Program in Biophysical Sciences, The University of Chicago, Chicago, IL 60637, USA
| | - Emre Kıcıman
- Information and Data Science Group, Microsoft Research, Redmond, WA, 98052, USA
| | - Mili Shah
- The University of Chicago Laboratory Schools, Chicago, IL 60637, USA
| | - Olivia J Walch
- Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrey Rzhetsky
- Departments of Medicine and Human Genetics, The University of Chicago, Chicago, IL 60637, USA
| | - Aaron R Dinner
- Department of Chemistry and the James Franck Institute, The University of Chicago, Chicago, IL 60637, USA
| | - Michael J Rust
- Department of Molecular Genetics and Cell Biology and Department of Physics, The University of Chicago, Chicago, IL 60637, USA.
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Litt DM, Lewis MA, Spiro ES, Aulck L, Waldron KA, Head-Corliss MK, Swanson A. #drunktwitter: Examining the relations between alcohol-related Twitter content and alcohol willingness and use among underage young adults. Drug Alcohol Depend 2018; 193:75-82. [PMID: 30343237 PMCID: PMC6239902 DOI: 10.1016/j.drugalcdep.2018.08.021] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2018] [Revised: 07/19/2018] [Accepted: 08/14/2018] [Indexed: 11/16/2022]
Abstract
PURPOSE Despite the importance of social networking sites on young adult alcohol use, few studies have examined Twitter as a conduit for sharing drinking behavior. However, this work generally uses random samples of tweets and thus cannot determine the extent to which Tweets correspond with self-reported drinking cognitions or behaviors. The primary aims of the present study were to (1) document basic patterns of alcohol-related Twitter activity in a subsample of young adult drinkers, and (2) examine whether willingness to drink, alcohol use, and negative consequences are associated with alcohol-related tweeting behavior. METHODS 186 young adults age 18-20 completed an online survey and provided Twitter handle information. From these participants, a random sample of 5000 Tweets was coded by a trained team to determine whether tweets were related to alcohol use or not. Ordinary least squares regression analyses were conducted to determine whether the proportion of alcohol-related Tweets is associated with self-reported alcohol use willingness, behaviors, and negative consequences. RESULTS Results indicated that not only are alcohol-related tweets common among young adults, but that the proportion of one's overall tweets that are related to alcohol is significantly associated with willingness to drink, alcohol use, and negative consequences. CONCLUSIONS The results of this study are an important step to understanding how digital behavior (e.g., posting about alcohol on Twitter) is related to an individual's self-reported drinking cognitions, alcohol use, and negative consequences and has implications for the way Twitter data can be used for public health surveillance and interventions.
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Affiliation(s)
- Dana M. Litt
- Department of Health Behavior and Health Systems, University of North Texas Health Science Center, Fort Worth, TX
| | - Melissa A. Lewis
- Department of Health Behavior and Health Systems, University of North Texas Health Science Center, Fort Worth, TX
| | - Emma S. Spiro
- Information School, University of Washington, Seattle, WA
| | - Lovenoor Aulck
- Information School, University of Washington, Seattle, WA
| | - Katja A. Waldron
- Department of Biobehavioral Health, Pennsylvania State University, State College, PA
| | - Maya K. Head-Corliss
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA
| | - Alex Swanson
- Department of Educational, School, and Counseling Psychology, University of Missouri, Columbia, MO
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The Missing Variable in Big Data for Social Sciences: The Decision-Maker. SUSTAINABILITY 2018. [DOI: 10.3390/su10103415] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The value of big data for social sciences and social impact is professed to be high. This potential value is related, however, to the capacity of using extracted information in decision-making. In all of this, one important point has been overlooked: when “humans” retain a role in the decision-making process, the value of information is no longer an objective feature but depends on the knowledge and mindset of end users. A new big data cycle has been proposed in this paper, where the decision-maker is placed at the centre of the process. The proposed cycle is tested through two cases and, as a result of the suggested approach, two operations—filtering and framing—which are routinely carried out independently by scientists and end users in an unconscious manner, become clear and transparent. The result is a new cycle where four dimensions guide the interactions for creating value.
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Hswen Y, Naslund JA, Brownstein JS, Hawkins JB. Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media. Psychiatr Q 2018; 89:569-580. [PMID: 29327218 PMCID: PMC6043409 DOI: 10.1007/s11126-017-9559-y] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Digital technologies hold promise for supporting the detection and management of schizophrenia. This exploratory study aimed to generate an initial understanding of whether patterns of communication about depression and anxiety on popular social media among individuals with schizophrenia are consistent with offline representations of the illness. From January to July 2016, posts on Twitter were collected from a sample of Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected sample of control users (n = 173). Frequency and timing of communication about depression and anxiety were compared between groups. In total, the groups posted n = 1,544,122 tweets and users had similar characteristics. Twitter users with schizophrenia showed significantly greater odds of tweeting about depression compared with control users (OR = 2.69; 95% CI 1.76-4.10), and significantly greater odds of tweeting about anxiety compared with control users (OR = 1.81; 95% CI 1.20-2.73). This study offers preliminary insights that Twitter users with schizophrenia may express elevated symptoms of depression and anxiety in their online posts, which is consistent with clinical characteristics of schizophrenia observed in offline settings. Social media platforms could further our understanding of schizophrenia by informing a digital phenotype and may afford new opportunities to support early illness detection.
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Affiliation(s)
- Yulin Hswen
- Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA. .,Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA.
| | - John A Naslund
- Department of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA
| | - John S Brownstein
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Jared B Hawkins
- Computational Epidemiology Group, Boston Children's Hospital, Boston, MA, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA, USA
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