What do people study when they study Twitter? Classifying Twitter related academic papers

Criticism, Compassion, and Conspiracy Theories: A Thematic Analysis of What Twitter Users Are Saying About COVID-19 in Correctional Settings

We examined Twitter data using thematic analysis to understand public perceptions of the impact of COVID-19 on incarcerated people and reactions to including incarcerated populations in the early phases of the vaccine rollout. Our findings from n = 513 Tweets yielded six themes: Twitter as usual, Advocacy, Deserve to suffer, Vaccine priority debate, Inadequate response, and Misinformation. Stigma-laden statements cut across themes, highlighting the role pathologizing beliefs play in forming opinions about incarcerated people in public health crises. Trust of government response and buy-in to public health communication are positively associated with adherence to guidelines. Although public health decisions are derived from logic and research, our findings indicate that public perception may be driven by personal morals and stigma associated with justice-involved individuals. We recommend that attention be turned toward effective policy messaging, and use of social media, to increase trust and decrease stigma that tends to dominate societal perception.

Sentiment analysis of vegan related tweets using mutual information for feature selection

Nowadays, people get increasingly attached to social media to connect with other people, to study, and to work. The presented article uses Twitter posts to better understand public opinion regarding the vegan (plant-based) diet that has traditionally been portrayed negatively on social media. However, in recent years, studies on health benefits, COVID-19, and global warming have increased the awareness of plant-based diets. The study employs a dataset derived from a collection of vegan-related tweets and uses a sentiment analysis technique for identifying the emotions represented in them. The purpose of sentiment analysis is to determine whether a piece of text (tweet in our case) conveys a negative or positive viewpoint. We use the mutual information approach to perform feature selection in this study. We chose this method because it is suitable for mining the complicated features from vegan tweets and extracting users' feelings and emotions. The results revealed that the vegan diet is becoming more popular and is currently framed more positively than in previous years. However, the emotions of fear were mostly strong throughout the period, which is in sharp contrast to other types of emotions. Our findings place new information in the public domain, which has significant implications. The article provides evidence that the vegan trend is growing and new insights into the key emotions associated with this growth from 2010 to 2022. By gaining a deeper understanding of the public perception of veganism, medical experts can create appropriate health programs and encourage more people to stick to a healthy vegan diet. These results can be used to devise appropriate government action plans to promote healthy veganism and reduce the associated emotion of fear.

Who Did Spanish Politicians Start Following on Twitter? Homophilic Tendencies among the Political Elite

Political communication has undergone transformations since the advent of digital networks, but do these new platforms promote interactivity and a public sphere with a more democratic political debate or do they function as echo chambers of the elites? In this research, we study the accounts that Spanish politicians started following on Twitter from 2017 to 2020, with the aim of understanding whether they reproduce patterns of homophilic tendencies or if they give space to new voices. To do so, we selected a sample from the deputies that were in the Spanish parliament during the four years of the study and through a big data and machine learning software, we identified the accounts they started following as a network and categorized them. We combined manual and computational data analysis methods and used data visualization techniques to look for patterns and trends. The results suggest that the Spanish political elites exhibit homophilic behaviors in terms of account types and geographic proximity and present a gender balance among the accounts. This study also suggests that the behavior of the political elite presented particularities during the electoral period, where we can observe an intensification of the homophilic patterns.

Understanding the application of handwritten text recognition technology in heritage contexts: a systematic review of Transkribus in published research

Handwritten Text Recognition (HTR) technology is now a mature machine learning tool, becoming integrated in the digitisation processes of libraries and archives, speeding up the transcription of primary sources and facilitating full text searching and analysis of historic texts at scale. However, research into how HTR is changing our information environment is scant. This paper presents a systematic literature review regarding how researchers are using one particular HTR platform, Transkribus, to indicate the domains where HTR is applied, the approach taken, and how the technology is understood. 381 papers from 2015 to 2020 were gathered from Google Scholar, Scopus, and Web of Science, then grouped and coded into categories using quantitative and qualitative approaches. Published research that mentions Transkribus is international and rapidly growing. Transkribus features primarily in archival and library science publications, while a long tail of broad and eclectic disciplines, including history, computer science, citizen science, law and education, demonstrate the wider applicability of the tool. The most common paper categories were humanities applications (67%), technological (25%), users (5%) and tutorials (3%). This paper presents the first overarching review of HTR as featured in published research, while also elucidating how HTR is affecting the information environment.

Media moments: how media events and business incentives drive twitter engagement within the small business community

Twitter is one of the most popular social networking platforms today with nearly 238 million active daily users. While the platform is used by a myriad of individuals for various purposes, businesses both large and small have begun to adopt Twitter into their business strategy to better connect with consumers. Considering the growing emphasis on social media engagement in the business sector, the present study examines some of the fastest-growing American small businesses from the perspective of media events theory. According to media events theory, certain large-scale events will attract excess viewership and attention from the public, both on traditional and digital platforms. We examine how small businesses leveraged media events of 2020, including COVID-19 and the 2020 US presidential election, so as to increase engagement and foster the growth of their businesses via Twitter. Using 35000 tweets based on media event-related hashtags collected throughout 2020, we investigated Twitter engagement among 100 of the fastest-growing small businesses in the USA. Through the use of network analysis metrics, we illustrate that businesses that tweeted about media events often received greater levels of user engagement and exerted greater influence over their respective networks.

Ethical issues of collecting, storing, and analyzing geo-referenced tweets for mental health research

Spatial approaches to epidemiological research with big social media data provide tremendous opportunities to study the relationship between the socio-ecological context where these data are generated and health indicators of interest. Such research poses a number of ethical challenges, particularly in relation to issues such as privacy, informed consent, data security, and storage. While these issues have received considerable attention by researchers in relation to research for physical health purposes in the past 10 years, there have been few efforts to consider the ethical challenges of conducting mental health research, particularly with geo-referenced social media data. The aim of this article is to identify strengths and limitations of current recommendations to address the specific ethical issues of geo-referenced tweets for mental health research. We contribute to the ongoing debate on the ethical implications of big data research and also provide recommendations to researchers and stakeholders alike on how to tackle them, with a specific focus on the use of geo-referenced data for mental health research purposes. With increasing awareness of data privacy and confidentiality issues (even for non-spatial social media data) it becomes crucial to establish professional standards of conduct so that compliance with ethical standards of conducting research with health-related social media data can be prioritized and easily assessed.

Financial Literacy and Sustainable Consumer Behavior

A more sustainable society and economy also implies more sustainable behavior in terms of the consumption of financial products. A possible change in this behavior can be brought about when there is a change in the demand. In other words, more sustainable consumption of financial products is directly related to financial literacy. However, the latter’s definition, object, and scope are far from being agreed upon internationally. One objective of this work was to explore the different interpretations of financial literacy in academic literature. In this exploration we delved into the evolution of the term, and how, in what context, and with what other concepts the term is used in social networks. Scientometric techniques were used for the analysis and review of the literature. The NLP technique was used to analyze comments on social networks. With this technique, ten feelings that were specially selected were analyzed. Positivity, confidence, and anticipation predominated among them. We conclude that it is important to emphasize that greater attention must be given to financial literacy, from both private and public sectors, so that it can be used to drive more sustainable behavior by individual consumers. Finally, a new definition of financial literacy is proposed.

Breaking community boundary: Comparing academic and social communication preferences regarding global pandemics

The global spread of COVID-19 has caused pandemics to be widely discussed. This is evident in the large number of scientific articles and the amount of user-generated content on social media. This paper aims to compare academic communication and social communication about the pandemic from the perspective of communication preference differences. It aims to provide information for the ongoing research on global pandemics, thereby eliminating knowledge barriers and information inequalities between the academic and the social communities. First, we collected the full text and the metadata of pandemic-related articles and Twitter data mentioning the articles. Second, we extracted and analyzed the topics and sentiment tendencies of the articles and related tweets. Finally, we conducted pandemic-related differential analysis on the academic community and the social community. We mined the resulting data to generate pandemic communication preferences (e.g., information needs, attitude tendencies) of researchers and the public, respectively. The research results from 50,338 articles and 927,266 corresponding tweets mentioning the articles revealed communication differences about global pandemics between the academic and the social communities regarding the consistency of research recognition and the preferences for particular research topics. The analysis of large-scale pandemic-related tweets also confirmed the communication preference differences between the two communities.

Link-based approach to study scientific software usage: the case of VOSviewer

Scientific software is a fundamental player in modern science, participating in all stages of scientific knowledge production. Software occasionally supports the development of trivial tasks, while at other instances it determines procedures, methods, protocols, results, or conclusions related with the scientific work. The growing relevance of scientific software as a research product with value of its own has triggered the development of quantitative science studies of scientific software. The main objective of this study is to illustrate a link-based webometric approach to characterize the online mentions to scientific software across different analytical frameworks. To do this, the bibliometric software VOSviewer is used as a case study. Considering VOSviewer’s official website as a baseline, online mentions to this website were counted in three different analytical frameworks: academic literature via Google Scholar (988 mentioning publications), webpages via Majestic (1,330 mentioning websites), and tweets via Twitter (267 mentioning tweets). Google scholar mentions shows how VOSviewer is used as a research resource, whilst mentions in webpages and tweets show the interest on VOSviewer’s website from an informational and a conversational point of view. Results evidence that URL mentions can be used to gather all sorts of online impacts related to non-traditional research objects, like software, thus expanding the analytical scientometric toolset by incorporating a novel digital dimension.

Implications of social media discourse for managing national parks in South Africa

Increasingly, public discourse occurs on social media including about national parks, but how much of it is relevant to park management and what topics/events cause debate? Using a case study approach, we conducted a content and sentiment analysis of tweets about seven South African national parks (Table Mountain, Karoo, Addo Elephant, Pilanesberg, West Coast, Tsitsikamma and Golden Gate Highlands) posted over 14 months on Twitter to assess what topics were discussed for which parks, by whom, when, what emotions were expressed and how such information could be useful for management. Most of the discourse (70% of 10,292 tweets) related to management, including tweets about biodiversity (34%), tourism (27%), natural attractions (17%) as well as crime and safety (10%). Most Tweeters were South African (60%) with other Tweeters from other countries in Africa (4%), the USA (12%) or UK (7%). Although the 2% of accounts from news and conservation organisations had massive followings, most tweets were from individuals, were mainly positive and included emotions such as anticipation and trust. Peaks in tweets often related to specific events in the parks reported in the news. Our study revealed that despite issues with Twitter, such as geographic bias, shortcomings with search terms and short texts, park authorities could consider monitoring Twitter to better understand the interests and opinions of local and international communities. Managers could also use Twitter to monitor reactions to specific events in parks, disseminating information to stakeholders and responding to contentious issues.

Twitter-based analysis reveals differential COVID-19 concerns across areas with socioeconomic disparities

OBJECTIVE

We sought to understand spatial-temporal factors and socioeconomic disparities that shaped U.S. residents' response to COVID-19 as it emerged.

METHODS

We mined coronavirus-related tweets from January 23rd to March 25th, 2020. We classified tweets by the socioeconomic status of the county from which they originated with the Area Deprivation Index (ADI). We applied topic modeling to identify and monitor topics of concern over time. We investigated how topics varied by ADI and between hotspots and non-hotspots.

RESULTS

We identified 45 topics in 269,556 unique tweets. Topics shifted from early-outbreak-related content in January, to the presidential election and governmental response in February, to lifestyle impacts in March. High-resourced areas (low ADI) were concerned with stocks and social distancing, while under-resourced areas shared negative expression and discussion of the CARES Act relief package. These differences were consistent within hotspots, with increased discussion regarding employment in high ADI hotspots.

DISCUSSION

Topic modeling captures major concerns on Twitter in the early months of COVID-19. Our study extends previous Twitter-based research as it assesses how topics differ based on a marker of socioeconomic status. Comparisons between low and high-resourced areas indicate more focus on personal economic hardship in less-resourced communities and less focus on general public health messaging.

CONCLUSION

Real-time social media analysis of community-based pandemic responses can uncover differential conversations correlating to local impact and income, education, and housing disparities. In future public health crises, such insights can inform messaging campaigns, which should partly focus on the interests of those most disproportionately impacted.

Tweets by People With Arthritis During the COVID-19 Pandemic: Content and Sentiment Analysis

BACKGROUND

Emerging evidence suggests that people with arthritis are reporting increased physical pain and psychological distress during the COVID-19 pandemic. At the same time, Twitter's daily usage has surged by 23% throughout the pandemic period, presenting a unique opportunity to assess the content and sentiment of tweets. Individuals with arthritis use Twitter to communicate with peers, and to receive up-to-date information from health professionals and services about novel therapies and management techniques.

OBJECTIVE

The aim of this research was to identify proxy topics of importance for individuals with arthritis during the COVID-19 pandemic, and to explore the emotional context of tweets by people with arthritis during the early phase of the pandemic.

METHODS

From March 20 to April 20, 2020, publicly available tweets posted in English and with hashtag combinations related to arthritis and COVID-19 were extracted retrospectively from Twitter. Content analysis was used to identify common themes within tweets, and sentiment analysis was used to examine positive and negative emotions in themes to understand the COVID-19 experiences of people with arthritis.

RESULTS

In total, 149 tweets were analyzed. The majority of tweeters were female and were from the United States. Tweeters reported a range of arthritis conditions, including rheumatoid arthritis, systemic lupus erythematosus, and psoriatic arthritis. Seven themes were identified: health care experiences, personal stories, links to relevant blogs, discussion of arthritis-related symptoms, advice sharing, messages of positivity, and stay-at-home messaging. Sentiment analysis demonstrated marked anxiety around medication shortages, increased physical symptom burden, and strong desire for trustworthy information and emotional connection.

CONCLUSIONS

Tweets by people with arthritis highlight the multitude of concurrent concerns during the COVID-19 pandemic. Understanding these concerns, which include heightened physical and psychological symptoms in the context of treatment misinformation, may assist clinicians to provide person-centered care during this time of great health uncertainty.

'Research sharing' using social media: online conferencing and the experience of #BSHSGlobalHist

In February 2020, the British Society for the History of Science hosted its first entirely digital conference via Twitter, with the dual goals of improving outreach and engagement with international historians of science, and exploring methods of reducing the carbon footprint of academic activities. In this article we discuss how we planned and organized this conference, and provide a summary of our experience of the conference itself. We also describe in greater detail the motivations behind its organization, and explore the good and bad dimensions of this relatively new kind of conferencing. As the climate crisis becomes more acute and, in turn, the pressure to reduce the carbon footprint of academic activities increases, we argue that digital conferences of this style will necessarily become more central to how academia operates. By sharing our own experiences of running such a conference, we seek to contribute to a rapidly growing body of knowledge on the subject that might be drawn on to improve our practices going forward. We also share some of our own ideas about how best to approach digital conference organization which helped us to make the most of this particular event.

Clasificación de la investigación académica en Metaliteracy

La Metaliteracy presenta, dentro de las multialfabetizaciones, un modelo conceptual transversal e integrador. Su enfoque innovador invita a identificar sus publicaciones y categorizarlas para entender sus métodos en su estudio. Se identificó la investigación sobre Metaliteracy mediante revisión de la literatura, identificando los resultados por autoría, fecha, filiación institucional de autores y principales revistas de publicación. Sobre su temática, se aplicó una clasificación ad hoc para el tratamiento de las palabras clave y adscripción a las categorías temáticas del Modelo Taxonómico de VOREMETUR, proyecto de investigación marco, cuyos resultados apuntan al diseño de programas en Metaliteracy para información digital. La investigación en Metaliteracy es todavía escasa y reciente, el grado de dispersión de autorías y fuentes es elevado. El desarrollo conceptual como disciplina de la Metaliteracy se vincula a la fundamentación de estudios de caso, sin planteamiento global para una investigación aplicada. Se constata la validez del Modelo Taxonómico VOREMETUR.

Does SEO Matter for Startups? Identifying Insights from UGC Twitter Communities

In the present study, we analyzed User Generated Content (UGC) to measure the importance of Search Engine Optimization (SEO) for startups. For this purpose, we used several clustering algorithms to identify user communities on Twitter. The dataset contained a total of 67,126 tweets. A three-step UGC analysis process was applied to the data. First, a Latent Dirichlet allocation (LDA) was developed to divide the UGC-sample into topics. Next, a sentiment analysis (SA) with machine-learning was applied to divide the sample of topics into negative, positive, and neutral feelings. Finally, a textual analysis (TA) process with data mining techniques was used to extract indicators related to the SEO technique optimization in startups. The results helped us identify UGC communities in Twitter about SEO for startups and the main optimization indicators according to the feelings expressed in tweets. Our results also demonstrated that Black Hack SEO is not the most relevant strategy of positioning of digital marketing for startups and that, although this strategy is used by the startups, it is predominantly negatively perceived by SEO UGC communities.

A Bibliometric Overview of Twitter-Related Studies Indexed in Web of Science

Twitter has been one of the most popular social network sites for academic research; the main objective of this study was to update the current knowledge boundary surrounding Twitter-related investigations and, further, identify the major research topics and analyze their evolution across time. A bibliometric analysis has been applied in this article: we retrieved 19,205 Twitter-related academic articles from Web of Science after several steps of data cleaning and preparation. The R package “Bibliometrix” was mainly used in analyzing this content. Our study has two sections, and performance analysis contains 5 categories (Annual Scientific Production, Most Relevant Sources, Most Productive Authors, Most Cited Publications, Most Relevant Keywords.). The science mapping included country collaboration analysis and thematic analysis. We highlight our thematic analysis by splitting the whole bibliographic dataset into three temporal periods, thus a thematic evolution across time has been presented. This study is one of the most comprehensive bibliometric overview in analyzing Twitter-related studies by far. We proceed to explain how the results will benefit the understanding of current academic research interests on the social media giant.

Understanding adaptive information seeking in the context of microblogging from the cognitive switching perspective

Drawing on adaptive structuration theory and cognitive switching theory, this study develops a research model exploring the effects of cognitive switching stimuli on adaptive information seeking and the moderating effects of information need and personal innovativeness in information technologies. Data collected from microblogging users were used to test the model. The findings suggest that other people’s use, discrepancies, and deliberate initiatives each have significant positive effects on trying new features to seek information. Other people’s use which essentially reflects the nature of learning from observing other people is the most important determinant. Meanwhile, information need and personal innovativeness in IT each positively moderate the effect of other people’s use on trying new features to seek information. This study contributes to theory by examining adaptive information seeking in the context of microblogging which has been largely overlooked by prior literature. The findings and more implications for theory and practice are discussed.

Winning engaged consumers

Purpose

Under the sunlight of social commerce, few concepts have blossomed like value co-creation. But when blurred strategies are implemented, the opportunity to wilt a brand is high. To avoid the miscues and the controversies, an ascendant step is to engage consumers with social commerce sites. The purpose of this paper is to propose three antecedents to engage consumers with social commerce sites, namely, social support, social commerce value and social commerce information sharing, and the effect of brand engagement on the intention of brand co-creation.

Design/methodology/approach

This study used survey data from 234 Iranians with experience using social commerce sites. Variance-based structural equation modeling using the partial least squares path modeling approach was adopted to analyze the structural model.

Findings

The authors found that social support, social commerce value and social commerce information sharing positively foster brand engagement. The study also revealed that brand engagement is a significant predictor of brand co-creation intention.

Originality/value

The study is the first study that considers and explains brand engagement from social support theory, social commerce value theory and social commerce information exchange. Also, the study shows how consumers can be an integral part of a brand. Unlike other studies which were done in industrialized countries, this study was employed in Iran.

Exploring adaptive information sharing from the perspective of cognitive switching

Purpose

Microblogging as one kind of social media application provides an important information sharing platform. Adaptive information sharing is the combination of adaptive information technologies (IT) use behavior and information sharing behavior and subsequently refers to adaptive use of IT oriented to information sharing. The purpose of this paper is to understand adaptive information sharing in the context of microblogging from the perspective of cognitive switching.

Design/methodology/approach

A research model was developed and survey data were collected. The partial least squares structural equation modeling was employed to verify the research model.

Findings

Adaptive information sharing is positively impacted by other people’s use, discrepancies and deliberate initiatives among which other people’s use is the key determinant. Meanwhile, task self-efficacy positively moderates the effect of other people’s use on adaptive information sharing.

Practical implications

Developers of microblogging should as far as possible create learning atmosphere and learning culture. With learning atmosphere and culture, more and more users could keep on learning from observing other people. Consequently, more and more users would be willing to try new features of microblogging to share information.

Originality/value

This study examines adaptive information sharing by extending adaptive IT use behavior from the levels of technology, system and feature to the information level, presenting a new lens for adaptive IT use and information sharing alike.

A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that is by assuming that after a calamitous event, in the survivors prevails the feeling of helping those who suffer. The extraction, from the Twitter repository, of the few tweets relevant to the event of interest has a long processing time. With the aggravating circumstance in the phase that follows a severe earthquake, the elaboration of tweets clashes with the need to act promptly. Our proposal allows a huge reduction of the processing time. This goal is reached by introducing a service and a mobile app, the latter is an intermediate tool between Twitter and the citizens, suitable to assist them to write structured messages that act as surrogates of tweets. The article describes the architecture of the software service and the steps involved in the retrieval, from the Twitter server, of the messages coming from citizens living in the places hit by the earthquake; moreover, it details the storage of those messages into a geographical database and their processing using SQL.

Digital diabetes: Perspectives for diabetes prevention, management and research

Digital medicine, digital research and artificial intelligence (AI) have the power to transform the field of diabetes with continuous and no-burden remote monitoring of patients' symptoms, physiological data, behaviours, and social and environmental contexts through the use of wearables, sensors and smartphone technologies. Moreover, data generated online and by digital technologies - which the authors suggest be grouped under the term 'digitosome' - constitute, through the quantity and variety of information they represent, a powerful potential for identifying new digital markers and patterns of risk that, ultimately, when combined with clinical data, can improve diabetes management and quality of life, and also prevent diabetes-related complications. Moving from a world in which patients are characterized by only a few recent measurements of fasting glucose levels and glycated haemoglobin to a world where patients, healthcare professionals and research scientists can consider various key parameters at thousands of time points simultaneously will profoundly change the way diabetes is prevented, managed and characterized in patients living with diabetes, as well as how it is scientifically researched. Indeed, the present review looks at how the digitization of diabetes can impact all fields of diabetes - its prevention, management, technology and research - and how it can complement, but not replace, what is usually done in traditional clinical settings. Such a profound shift is a genuine game changer that should be embraced by all, as it can provide solid research results transferable to patients, improve general health literacy, and provide tools to facilitate the everyday decision-making process by both healthcare professionals and patients living with diabetes.

Instagram, Flickr, or Twitter: Assessing the usability of social media data for visitor monitoring in protected areas

Social media data is increasingly used as a proxy for human activity in different environments, including protected areas, where collecting visitor information is often laborious and expensive, but important for management and marketing. Here, we compared data from Instagram, Twitter and Flickr, and assessed systematically how park popularity and temporal visitor counts derived from social media data perform against high-precision visitor statistics in 56 national parks in Finland and South Africa in 2014. We show that social media activity is highly associated with park popularity, and social media-based monthly visitation patterns match relatively well with the official visitor counts. However, there were considerable differences between platforms as Instagram clearly outperformed Twitter and Flickr. Furthermore, we show that social media data tend to perform better in more visited parks, and should always be used with caution. Based on stakeholder discussions we identified potential reasons why social media data and visitor statistics might not match: the geography and profile of the park, the visitor profile, and sudden events. Overall the results are encouraging in broader terms: Over 60% of the national parks globally have Twitter or Instagram activity, which could potentially inform global nature conservation.

Understanding academic attitudes towards the ethical challenges posed by social media research

In this paper, we outline an online survey-based study seeking to understand academic attitudes towards social media research ethics (SMRE). As the exploratory phase of a wider research project, findings are discussed in relation to the responses of 30 participants, spanning multiple faculties and locations at one international university. The paper presents an empirical measure of attitudes towards social media research ethics, reflecting core issues outlined throughout the nascent Internet-mediated research (IMR) literature, in addition to survey questions relating to familiarity with SMRE guidance, and experience of reviewing SMRE proposals from students and/or as part of the university's research ethics committees (RECs). Findings indicate notable variance in academic attitudes towards the ethical challenges of social media research, reflecting the complexity of decision-making within this context and further emphasising the need to understand influencing factors. Future directions are discussed in relation to the tentative findings presented by the current study.

Network structure and community evolution on Twitter: human behavior change in response to the 2011 Japanese earthquake and tsunami

To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

Twitter and TV events: an exploration of how to use social media for student-led research

Purpose

– The purpose of this paper is twofold. First, it investigates the relationship between television, its audiences and Twitter around the creation of social TV events. Here it contributes to knowledge by charting usage in relation to different types of programmes and by comparing Twitter to Facebook data. Second, it evaluates the way in which student-led research can be used to conduct audience studies with the help of Twitter.

Design/methodology/approach

– The research applies a quantitative approach, measuring the volume of Twitter messages before, during and after two different types of television programmes, i.e. Reality TV (The X Factor and The Only Way is Essex) and sports broadcasts (football and Formula One). Brief comparisons are also drawn with data collected from Facebook. The pedagogical evaluation of the research is based on self-reflection by the author/tutor.

Findings

– The research established similar trends and patterns of viewer engagement for both types of television programming, with key activity during and towards the end of a broadcast which points to viewers using Twitter, or Facebook, while watching the event. The findings are compared to previous studies on television programmes and Twitter use. The study also identified that student research using Twitter can lead to a valuable learning experience as it allows students to use their own knowledge of social media to inform the research process.

Originality/value

– This research makes a contribution to the small yet growing body of studies examining Twitter activity in relation to TV events. It also contributes to knowledge on the educational use of social media by providing an account of how Twitter can be applied as a research tool by students.

A topology of Twitter research: disciplines, methods, and ethics

Purpose

– The purpose of this paper is to engage in a systematic analysis of academic research that relies on the collection and use of Twitter data, creating topology of Twitter research that details the disciplines and methods of analysis, amount of tweets and users under analysis, the methods used to collect Twitter data, and accounts of ethical considerations related to these projects.

Design/methodology/approach

– Content analysis of 382 academic publications from 2006 to 2012 that used Twitter as their primary platform for data collection and analysis.

Findings

– The analysis of over 380 scholarly publications utilizing Twitter data reveals noteworthy trends related to the growth of Twitter-based research overall, the disciplines engaged in such research, the methods of acquiring Twitter data for analysis, and emerging ethical considerations of such research.

Research limitations/implications

– The findings provide a benchmark analysis that must be updated with the continued growth of Twitter-based research.

Originality/value

– The research is the first full-text systematic analysis of Twitter-based research projects, focussing on the growth in discipline and methods as well as its ethical implications. It is of value for the broader research community currently engaged in social media-based research, and will prompt reflexive evaluation of what research is occurring, how it is occurring, what is being done with Twitter data, and how researchers are addressing the ethics of Twitter-based research.

How Twitter Is Studied in the Medical Professions: A Classification of Twitter Papers Indexed in PubMed

Background

Since their inception, Twitter and related microblogging systems have provided a rich source of information for researchers and have attracted interest in their affordances and use. Since 2009 PubMed has included 123 journal articles on medicine and Twitter, but no overview exists as to how the field uses Twitter in research.

Objective

This paper aims to identify published work relating to Twitter within the fields indexed by PubMed, and then to classify it. This classification will provide a framework in which future researchers will be able to position their work, and to provide an understanding of the current reach of research using Twitter in medical disciplines.

Methods

Papers on Twitter and related topics were identified and reviewed. The papers were then qualitatively classified based on the paper’s title and abstract to determine their focus. The work that was Twitter focused was studied in detail to determine what data, if any, it was based on, and from this a categorization of the data set size used in the studies was developed. Using open coded content analysis additional important categories were also identified, relating to the primary methodology, domain, and aspect.

Results

As of 2012, PubMed comprises more than 21 million citations from biomedical literature, and from these a corpus of 134 potentially Twitter related papers were identified, eleven of which were subsequently found not to be relevant. There were no papers prior to 2009 relating to microblogging, a term first used in 2006. Of the remaining 123 papers which mentioned Twitter, thirty were focused on Twitter (the others referring to it tangentially). The early Twitter focused papers introduced the topic and highlighted the potential, not carrying out any form of data analysis. The majority of published papers used analytic techniques to sort through thousands, if not millions, of individual tweets, often depending on automated tools to do so. Our analysis demonstrates that researchers are starting to use knowledge discovery methods and data mining techniques to understand vast quantities of tweets: the study of Twitter is becoming quantitative research.

Conclusions

This work is to the best of our knowledge the first overview study of medical related research based on Twitter and related microblogging. We have used 5 dimensions to categorize published medical related research on Twitter. This classification provides a framework within which researchers studying development and use of Twitter within medical related research, and those undertaking comparative studies of research, relating to Twitter in the area of medicine and beyond, can position and ground their work.