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

"Can Anyone Tell Me…". Online Health Communities in Diabetes Self-Management in Poland and Italy

This paper contributes to the debate about the role of Online Health Communities (OHCs) in the healthcare system by concentrating on the kind of information sought and shared by their members. The paper focuses on OHCs for diabetes and discusses the main findings of a qualitative study conducted in Italy and Poland. The Uses and Gratifications approach informed the study, while content analysis was used to perform the analysis. The findings show that OHCs' role goes beyond information and emotional support, which relies on expertise by experience. Indeed, the lack of basic knowledge constituting the essential diabetes literacy for self-management was partially compensated by peer exchange in the OHCs. This raises at least two problems: quality and reliability of the information shared online, and consequences in terms of the equity that a healthcare system provides.

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.

Data-driven strategies in operation management: mining user-generated content in Twitter

In recent years, the business ecosystem has focused on understanding new ways of automating, collecting, and analyzing data in order to improve products and business models. These actions allow operations management to improve prediction, value creation, optimization, and automatization. In this study, we develop a novel methodology based on data-mining techniques and apply it to identify insights regarding the characteristics of new business models in operations management. The data analyzed in the present study are user-generated content from Twitter. The results are validated using the methods based on Computer-Aided Text Analysis. Specifically, a sentimental analysis with TextBlob on which experiments are performed using vector classifier, multinomial naïve Bayes, logistic regression, and random forest classifier is used. Then, a Latent Dirichlet Allocation is applied to separate the sample into topics based on sentiments to calculate keyness and p-value. Finally, these results are analyzed with a textual analysis developed in Python. Based on the results, we identify 8 topics, of which 5 are positive (Automation, Data, Forecasting, Mobile accessibility and Employee experiences), 1 topic is negative (Intelligence Security), and 2 topics are neutral (Operational CRM, Digital teams). The paper concludes with a discussion of the main characteristics of the business models in the OM sector that use DDI. In addition, we formulate 26 research questions to be explored in future studies.

#DoctorsSpeakUp: Exploration of Hashtag Hijacking by Anti-Vaccine Advocates and the Influence of Scientific Counterpublics on Twitter

The #DoctorsSpeakUp hashtag, which was designed by physicians to collectively promote vaccines on Twitter in Spring of 2020, was hijacked by anti-vaccine advocates. Through the lens of counterpublic sphere theory, thematic analysis revealed that the hashtag hijacking by a scientific counterpublic was successful, and the majority of #DoctorsSpeakUp tweets were oriented against vaccines. Five overarching themes emerged in anti-vaccine hijacked tweets, including: personal experience with vaccine injury, profits over people, lack of liability, perception that doctors are uninformed, and 'We are the Herd.' In contrast, fewer than 17% of tweets originated from pro-vaccine doctors who openly identified themselves as such in their tweets or profiles. Thus, using the #DoctorsSpeakUp hashtag, anti-vaccine advocates dominated the discourse, which speaks to the communication dynamics afforded within the information ecosystem of a social network. Hashtag activism can connect individuals and promote grassroots movements but may also backfire, allowing a vocal minority of individuals to shout the loudest through the digital megaphone.

Sociocultural factors during COVID-19 pandemic: Information consumption on Twitter

The purpose of the research is to describe the sociocultural factors that emerged during the COVID-19 pandemic. Twitter is used as an instrument for data collection. The study is qualitative and uses the netnographic method. To analyze the flow of messages posted on Twitter, the model proposed by Perez-Cepeda and Arias-Bolzmann (2020), which describes sociocultural factors, is taken as a basis. The semantics that people use are a type of functional knowledge that reveals sociocultural factors. Sentiments were analyzed through lexicon-based methods, which are the most suitable. The categorization and classification of the data are performed based on the information that users post on Twitter. The tweets related to COVID-19 describe the sociocultural issues and the level of sentiment around the pandemic. The discussion centers on the COVID-19 pandemic, information consumption, lexicon, sociocultural factors and sentiment analysis. The study was limited to the social media Twitter; another limitation was not to consider the social group of the users who interact with @pandemic_Covid-19, official account of the World Health Organization (WHO). This research contributes to the social sciences, focusing on sociocultural interaction through the use of the social network Twitter. It describes the link between sociocultural factors and the level of sentiment on issues related to the COVID-19 pandemic.

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.

The Role of Twitter in the WHO's Fight against the Infodemic

The COVID-19 pandemic has far-reaching consequences in various fields. In addition to its health and economic impact, there are also social, cultural and informational impacts. Regarding the latter, the World Health Organization (WHO) flagged concerns about the infodemic at the beginning of 2020. The main objective of this paper is to explore how the WHO uses its Twitter profile to inform the population on vaccines against the coronavirus, thus preventing or mitigating misleading or false information both in the media and on social networks. This study analyzed 849 vaccine-related tweets posted by the WHO on its Twitter account from 9 November 2020 (when the 73rd World Health Assembly resumed) to 14 March 2021 (three months after the start of vaccination). In order to understand the data collected, these results were compared with the actions carried out by the WHO and with the information and debates throughout this period. The analysis shows that the WHO is decidedly committed to the use of these tools as a means to disseminate messages that provide the population with accurate and scientific information, as well as to combat mis- and disinformation about the SARS-CoV-2 vaccination process.

Data Curation Implications of Qualitative Data Reuse and Big Social Research

Objective: Big social data (such as social media and blogs) and archived qualitative data (such as interview transcripts, field notebooks, and diaries) are similar, but their respective communities of practice are under-connected. This paper explores shared challenges in qualitative data reuse and big social research and identifies implications for data curation. Methods: This paper uses a broad literature search and inductive coding of 300 articles relating to qualitative data reuse and big social research. The literature review produces six key challenges relating to data use and reuse that are present in both qualitative data reuse and big social research—context, data quality, data comparability, informed consent, privacy & confidentiality, and intellectual property & data ownership. Results: This paper explores six key challenges related to data use and reuse for qualitative data and big social research and discusses their implications for data curation practices. Conclusions: Data curators can benefit from understanding these six key challenges and examining data curation implications. Data curation implications from these challenges include strategies for: providing clear documentation; linking and combining datasets; supporting trustworthy repositories; using and advocating for metadata standards; discussing alternative consent strategies with researchers and IRBs; understanding and supporting deidentification challenges; supporting restricted access for data; creating data use agreements; supporting rights management and data licensing; developing and supporting alternative archiving strategies. Considering these data curation implications will help data curators support sounder practices for both qualitative data reuse and big social research.

The Tone Debate: Knowledge, Self, and Social Order

In the replication crisis in psychology, a “tone debate” has developed. It concerns the question of how to conduct scientific debate effectively and ethically. How should scientists give critique without unnecessarily damaging relations? The increasing use of Facebook and Twitter by researchers has made this issue especially pressing, as these social technologies have greatly expanded the possibilities for conversation between academics, but there is little formal control over the debate. In this article, we show that psychologists have tried to solve this issue with various codes of conduct, with an appeal to virtues such as humility, and with practices of self-transformation. We also show that the polemical style of debate, popular in many scientific communities, is itself being questioned by psychologists. Following Shapin and Schaffer’s analysis of the ethics of Robert Boyle’s experimental philosophy in the 17th century, we trace the connections between knowledge, social order, and subjectivity as they are debated and revised by present-day psychologists.

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.

On the complexities of studying sensitive communities online as a researcher–participant

Purpose

This study aims to explore the complexities of methodological, ethical and emotional challenges of studying sensitive and vulnerable communities online from the perspective of simultaneously being a researcher and a research subject. The point of departure for these explorations consists of the author’s past and ongoing studies of the role and use of a closed grief support group on Facebook for bereaved parents – a community of which the author is a member. The aim is not to provide ready solutions for “how to do ethics,” but rather to contribute to the collective and ongoing work initiated by the Association of Internet Researchers (AoIR), among others, and to recognize the necessity of ethical pluralism, cross-cultural awareness and an interdisciplinary approach.

Design/methodology/approach

This is an explorative study, drawing on an (auto)ethnographic case study. The case serves as a point of departure for discussing the complexities of methodological, ethical and emotional challenges of studying sensitive and vulnerable communities online from the perspective of simultaneously being a researcher and a research subject.

Findings

Being a researcher and a research subject rolled into one, as it were, presents both opportunities and challenges. To conduct responsible research from both these perspectives pose high demands on the researchers’ ethical as well as emotional capacities and responsibilities. Hopes and expectancies of the community under study might put the researcher into a dilemma, ethical aspects of anonymity and informed consent might have to be reconsidered as well as emotional challenges of engaging in and with sensitive research, all of which makes for a complex balancing act. Ethics and methods are inextricably intertwined, so are the emotional challenges of conducting sensitive research intermingled. Studying vulnerable individuals and closed communities online highlights the necessity for case and context sensitive research and for flexibility, adaptivity and mindfulness of the researcher. It also highlights the importance of discussing and questioning theoretical, methodological and ethical developments for studying everyday life practices online.

Originality/value

The challenges encountered in this case study contribute to the experientially grounded approach to research ethics emphasized in AoIR’s ethics guidelines. This case offers an opportunity to explore and discuss complex issues arising from the researcher’s insider position in a closed group devoted to the sensitive topic of supporting bereaved parents. Further, it highlights the necessity for research to be case and context sensitive as well as for the researcher and the research design to be flexible and adaptive. Research on vulnerable communities also heightens the demands of ethical responsibility of the researcher and the research process.

Sender vs. recipient-orientated information systems revisited

Purpose

Belkin and Robertson (1976a) reflected on the ethical implications of theoretical research in information science and warned that there was potential for abuse of knowledge gained by undertaking such research and applying it to information systems. In particular, they identified the domains of advertising and political propaganda that posed particular problems. The purpose of this literature review is to revisit these ideas in the light of recent events in global information systems that demonstrate that their fears were justified.

Design/methodology/approach

The authors revisit the theory in information science that Belkin and Robertson used to build their argument, together with the discussion on ethics that resulted from this work in the late 1970s and early 1980s. The authors then review recent literature in the field of information systems, specifically information retrieval, social media and recommendation systems that highlight the problems identified by Belkin and Robertson.

Findings

Information science theories have been used in conjunction with empirical evidence gathered from user interactions that have been detrimental to both individuals and society. It is argued in the paper that the information science and systems communities should find ways to return control to the user wherever possible, and the ways to achieve this are considered.

Research limitations/implications

The ethical issues identified require a multidisciplinary approach with research in information science, computer science, information systems, business, sociology, psychology, journalism, government and politics, etc. required. This is too large a scope to deal with in a literature review, and we focus only on the design and implementation of information systems (Zimmer, 2008a) through an information science and information systems perspective.

Practical implications

The authors argue that information systems such as search technologies, social media applications and recommendation systems should be designed with the recipient of the information in mind (Paisley and Parker, 1965), not the sender of that information.

Social implications

Information systems designed ethically and with users in mind will go some way to addressing the ill effects typified by the problems for individuals and society evident in global information systems.

Originality/value

The authors synthesize the evidence from the literature to provide potential technological solutions to the ethical issues identified, with a set of recommendations to information systems designers and implementers.

Disrupting State Spaces: Asylum Seekers in Australia’s Offshore Detention Centres

The Australian government has spent over a billion dollars a year on managing offshore detention (Budget 2018–2019). Central to this offshore management was the transference and mandatory detention of asylum seekers in facilities that sit outside Australia’s national sovereignty, in particular on Manus Island (Papua New Guinea) and Nauru. As a state-sanctioned spatial aberration meant to deter asylum seekers arriving by boat, offshore detention has resulted in a raft of legal and policy actions that are reshaping the modern state-centric understanding of the national space. It has raised questions of sovereignty, of moral, ethical and legal obligations, of national security and humanitarian responsibilities, and of nationalism and belonging. Using a sample of Twitter users on Manus during the closure of the Manus Island detention centre in October–November 2017, this paper examines how asylum seekers and refugees have negotiated and defined the offshore detention space and how through the use of social media they have created a profound disruption to the state discourse on offshore detention. The research is based on the premise that asylum seekers’ use social media in a number of disruptive ways, including normalising the presence of asylum seekers in the larger global phenomena of migration, humanising asylum seekers in the face of global discourses of dehumanisation, ensuring visibility by confirming the conditions of detention, highlighting Australia’s human rights violations and obligations, and challenging the government discourse on asylum seekers and offshore detention. Social media is both a tool and a vehicle by which asylum seekers on Manus Island could effect that disruption.

"Care is not care if it isn't person-centred": A content analysis of how Person-Centred Care is expressed on Twitter

Background

Person‐Centred Care (PCC) has been the subject of growing interest in recent decades. Even though there is no conceptual consensus regarding PCC, many health‐care institutions have implemented elements into their care.

Objective

This study aimed to investigate the PCC topics presented by different stakeholder groups on Twitter and to explore the perceptions of PCC within the content of the tweets.

Method

Tweets with mentions of PCC in various translations were collected through a Twitter Application Programming Interface in October 2019. The tweets were analysed using quantitative and qualitative content analysis.

Results

Five stakeholder groups and ten topics were identified within 1540 tweets. The results showed that the PCC content focused on providing information and opinions rather than expressing experiences of PCC in practice. Qualitative content analysis of 428 selected tweets revealed content on a vision that all care should be person‐centred but that the realization of that vision was more complicated.

Conclusions

Twitter has shown to be a quick and non‐intrusive data collection tool for uncovering stakeholders' expressions concerning PCC. The PCC content revealed that stakeholders feel a need to 'educate' others about their perception of PCC when experiences and real‐life applications are missing. More action should be taken for the implementation of PCC rather than circulating PCC vision without operationalization in care.

Public Contribution

The public provided the data through their posts on Twitter, and it is their perception of PCC that is studied here.

Perspective: Acknowledging Data Work in the Social Media Research Lifecycle

This perspective article suggests considering the everyday research data management work required to accomplish social media research along different phases in a data lifecycle to inform the ongoing discussion of social media research data's quality and validity. Our perspective is informed by practical experience of archiving social media data, by results from a series of qualitative interviews with social media researchers, as well as by recent literature in the field. We emphasize how social media researchers are entangled in complexities between social media platform providers, social media users, other actors, as well as legal and ethical frameworks, that all affect their everyday research practices. Research design decisions are made iteratively at different stages, involving many decisions that may potentially impact the quality of research. We show that these decisions are often hidden, but that making them visible allows us to better understand what drives social media research into specific directions. Consequently, we argue that untangling and documenting choices during the research lifecycle, especially when researchers pursue specific approaches and may have actively decided against others (often due to external factors) is necessary and will help to spot and address structural challenges in the social media research ecosystem that go beyond critiques of individual opportunistic approaches to easily accessible data.

Video kills the sentiment-Exploring fans' reception of the video assistant referee in the English premier league using Twitter data

Evaluative research of technological officiating aids in sports predominantly focuses on the respective technology and the impact on decision accuracy, whereas the impact on stakeholders is neglected. Therefore, the aim of this study was to investigate the immediate impact of the recently introduced Video Assistant Referee, often referred to as VAR, on the sentiment of fans of the English Premier League. We analyzed the content of 643,251 tweets from 129 games, including 94 VAR incidents, using a new variation of a gradient boosting approach to train two tree-based classifiers for text corpora: one classifier to identify tweets related to the VAR and another one to rate a tweet’s sentiment. The results of 10-fold cross-validations showed that our approach, for which we only took a small share of all features to grow each tree, performed better than common approaches (naïve Bayes, support vector machines, random forest and traditional gradient tree boosting) used by other studies for both classification problems. Regarding the impact of the VAR on fans, we found that the average sentiment of tweets related to this technological officiating aid was significantly lower compared to other tweets (-0.64 vs. 0.08; t = 45.5, p < .001). Further, by tracking the mean sentiment of all tweets chronologically for each game, we could display that there is a significant drop of sentiment for tweets posted in the periods after an incident compared to the periods before. A plunge that persisted for 20 minutes on average. Summed up, our results provide evidence that the VAR effects predominantly expressions of negative sentiment on Twitter. This is in line with the results found in previous, questionnaire-based, studies for other technological officiating aids and also consistent with the psychological principle of loss aversion.

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.

Modelling Twitter conversations in #favela towards the conceptualization of the eVoice of the unheard

Purpose

The purpose of this study to shed light on the importance of social media hosted content related to socially-motivated discussions. Moving from the field of communication for development, the research leverages social media as a powerful tool for collecting and analyse peer-to-peer communication towards the conceptualization of eVoices of Unheard. The deep understanding of these conversation can generate recommendations for organizations and governments designing and providing interventions fostering local socio-economic development.

Design/methodology/approach

The study presents a large-scale analysis of social media interactions on the topic “#favela” to generate insights into a social network structure, narrative contents and meaning generated.

Findings

Structurally, the analysed networks are comparable with those presented in current academic literature; automatic text analysis confirmed the promise of the inner value of communication for development opening the floor to conceptualization of the “eVoices of unheard”, which is the collective and conscious use of social media to mediate community discussions about tangible and intangible issues related to socio-economic development.

Originality/value

Framed within the rise of interactive communication for development this research show that social media an support the notion of voice proposed by Couldry (2010) moving from process (i.e. the recording of the voice) towards value (i.e. the possibility of giving an account of one’s life and its conditions to have an impact on human life and resources) thereby understanding intangible issues related with socio-economic development.

Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries

Social data in digital form-including user-generated content, expressed or implicit relations between people, and behavioral traces-are at the core of popular applications and platforms, driving the research agenda of many researchers. The promises of social data are many, including understanding "what the world thinks" about a social issue, brand, celebrity, or other entity, as well as enabling better decision-making in a variety of fields including public policy, healthcare, and economics. Many academics and practitioners have warned against the naïve usage of social data. There are biases and inaccuracies occurring at the source of the data, but also introduced during processing. There are methodological limitations and pitfalls, as well as ethical boundaries and unexpected consequences that are often overlooked. This paper recognizes the rigor with which these issues are addressed by different researchers varies across a wide range. We identify a variety of menaces in the practices around social data use, and organize them in a framework that helps to identify them. "For your own sanity, you have to remember that not all problems can be solved. Not all problems can be solved, but all problems can be illuminated." -Ursula Franklin.

A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters

During disasters, people share their thoughts and emotions on social media and also provide information about the event. Mining the social media messages and updates can be helpful in understanding the emotional state of people during such unforeseen events as they are real-time data. The objective of this review is to explore the feasibility of using social media data for mental health surveillance as well as the techniques used for determining mental health using social media data during disasters. PubMed, PsycINFO, and PsycARTICLES databases were searched from 2009 to November 2018 for primary research studies. After screening and analyzing the records, 18 studies were included in this review. Twitter was the widely researched social media platform for understanding the mental health of people during a disaster. Psychological surveillance was done by identifying the sentiments expressed by people or the emotions they displayed in their social media posts. Classification of sentiments and emotions were done using lexicon-based or machine learning methods. It is not possible to conclude that a particular technique is the best performing one, because the performance of any method depends upon factors such as the disaster size, the volume of data, disaster setting, and the disaster web environment.

Affective responses to uncertain real-world outcomes: Sentiment change on Twitter

We use data from Twitter.com to study the interplay between affect and expectations about uncertain outcomes. In two studies, we obtained tweets about candidates in the 2014 US Senate elections and tweets about National Football League (NFL) teams in the 2014/2015 NFL season. We chose these events because a) their outcomes are highly uncertain and b) they attract a lot of attention and feature heavily in the communication on social media. We also obtained a priori expectations for the events from political forecasting and sport betting websites. Using this quasi-experimental design, we found that unexpected events are associated with more intense affect than expected events. Moreover, the effect of expectations is larger for outcomes that fall below expectations than outcomes that exceed expectations. Our results are consistent with fundamental principles in psychological science, such as reference-dependence in experienced affect. We discuss how naturally occurring online data can be used to test psychological predictions and develop novel psychological insights.

Toward Real-Time Infoveillance of Twitter Health Messages

There is growing interest in conducting public health research using data from social media. In particular, Twitter "infoveillance" has demonstrated utility across health contexts. However, rigorous and reproducible methodologies for using Twitter data in public health are not yet well articulated, particularly those related to content analysis, which is a highly popular approach. In 2014, we gathered an interdisciplinary team of health science researchers, computer scientists, and methodologists to begin implementing an open-source framework for real-time infoveillance of Twitter health messages (RITHM). Through this process, we documented common challenges and novel solutions to inform future work in real-time Twitter data collection and subsequent human coding. The RITHM framework allows researchers and practitioners to use well-planned and reproducible processes in retrieving, storing, filtering, subsampling, and formatting data for health topics of interest. Further considerations for human coding of Twitter data include coder selection and training, data representation, codebook development and refinement, and monitoring coding accuracy and productivity. We illustrate methodological considerations through practical examples from formative work related to hookah tobacco smoking, and we reference essential methods literature related to understanding and using Twitter data.

Academic information on Twitter: A user survey

Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research.

Twitter Users’ Privacy Concerns: What do Their Accounts’ First Names Tell Us?

Purpose

In this paper, we describe how gender recognition on Twitter can be used as an intelligent business tool to determine the privacy concerns among users, and ultimately offer a more personalized service for customers who are more likely to respond positively to targeted advertisements.

Design/methodology/approach

We worked with two different data sets to examine whether Twitter users’ gender, inferred from the first name of the account and the profile description, correlates with the privacy setting of the account. We also used a set of features including the inferred gender of Twitter users to develop classifiers that predict user privacy settings.

Findings

We found that the inferred gender of Twitter users correlates with the account’s privacy setting. Specifically, females tend to be more privacy concerned than males. Users whose gender cannot be inferred from their provided first names tend to be more privacy concerned. In addition, our classification performance suggests that inferred gender can be used as an indicator of the user’s privacy preference.

Research limitations

It is known that not all twitter accounts are real user accounts, and social bots tweet as well. A major limitation of our study is the lack of consideration of social bots in the data. In our study, this implies that at least some percentage of the undefined accounts, that is, accounts that had names non-existent in the name dictionary, are social bots. It will be interesting to explore the privacy setting of social bots in the Twitter space.

Practical implications

Companies are investing large amounts of money in business intelligence tools that allow them to know the preferences of their consumers. Due to the large number of consumers around the world, it is very difficult for companies to have direct communication with each customer to anticipate market changes. For this reason, the social network Twitter has gained relevance as one ideal tool for information extraction. On the other hand, users’ privacy preference needs to be considered when companies consider leveraging their publicly available data. This paper suggests that gender recognition of Twitter users, based on Twitter users’ provided first names and their profile descriptions, can be used to infer the users’ privacy preference.

Originality/value

This study explored a new way of inferring Twitter user’s gender, that is, to recognize the user’s gender based on the provided first name and the user’s profile description. The potential of this information for predicting the user’s privacy preference is explored.

Using Twitter to Explore (un)Healthy Housing: Learning from the #Characterbuildings Campaign in New Zealand

While increasingly used for research, Twitter remains largely untapped as a source of data about housing. We explore the growth of social media and use of Twitter in health and social research, and question why housing researchers have avoided using Twitter to explore housing issues to date. We use the #characterbuildings campaign, initiated by an online media platform in New Zealand in 2014 to illustrate that Twitter can provide insights into housing as a public health and social problem. We find that Twitter users share details of problems with past and present homes on this public platform, and that this readily available data can contribute to the case for improving building quality as a means of promoting public health. Moreover, the way people responded to the request to share details about their housing experiences provides insight into how New Zealanders conceive of housing problems.

Does KFC sell rat? Analysis of tweets in the wake of a rumor outbreak

Purpose

In the wake of a rumor outbreak, individuals exchange three types of messages: rumor messages, counter-rumor messages, and uncertainty-expressing messages. However, the properties of the three types of messages are relatively unknown particularly in the social media context. Hence, the purpose of this paper is to examine these three types of messages posted on social media in the wake of a rumor outbreak.

Design/methodology/approach

Data included tweets posted after the outbreak of a rumor that wrongly accused the fast food chain Kentucky Fried Chicken (KFC) for selling rats instead of chicken. Using a deductive approach, codes were derived via content analysis on the tweets. Volume and exposure of tweets were also examined.

Findings

Counter-rumor tweets (52 percent) outnumbered rumors tweets (32 percent) and uncertainty-expressing tweets (16 percent). Emotions and personal involvement were abundant in rumor tweets. Expressions of credence and references to URLs were high in counter-rumor tweets. Social ties were found widely in uncertainty-expressing tweets. The high volume and exposure of counter-rumor tweets compared with those of either rumor tweets or uncertainty-expressing tweets highlight the potential of counter-rumors to mitigate rumors.

Originality/value

This research ventures into a relatively unexplored territory by concurrently examining rumor messages, counter-rumor messages and uncertainty-expressing messages in the wake of a rumor outbreak. It reveals that counter-rumor messages have the potential to mitigate rumors on social media.

Machine Learning, Sentiment Analysis, and Tweets: An Examination of Alzheimer's Disease Stigma on Twitter

Objectives

Social scientists need practical methods for harnessing large, publicly available datasets that inform the social context of aging. We describe our development of a semi-automated text coding method and use a content analysis of Alzheimer's disease (AD) and dementia portrayal on Twitter to demonstrate its use. The approach improves feasibility of examining large publicly available datasets.

Method

Machine learning techniques modeled stigmatization expressed in 31,150 AD-related tweets collected via Twitter's search API based on 9 AD-related keywords. Two researchers manually coded 311 random tweets on 6 dimensions. This input from 1% of the dataset was used to train a classifier against the tweet text and code the remaining 99% of the dataset.

Results

Our automated process identified that 21.13% of the AD-related tweets used AD-related keywords to perpetuate public stigma, which could impact stereotypes and negative expectations for individuals with the disease and increase "excess disability".

Discussion

This technique could be applied to questions in social gerontology related to how social media outlets reflect and shape attitudes bearing on other developmental outcomes. Recommendations for the collection and analysis of large Twitter datasets are discussed.

The unbearable emptiness of tweeting-About journal articles

Enthusiasm for using Twitter as a source of data in the social sciences extends to measuring the impact of research with Twitter data being a key component in the new altmetrics approach. In this paper, we examine tweets containing links to research articles in the field of dentistry to assess the extent to which tweeting about scientific papers signifies engagement with, attention to, or consumption of scientific literature. The main goal is to better comprehend the role Twitter plays in scholarly communication and the potential value of tweet counts as traces of broader engagement with scientific literature. In particular, the pattern of tweeting to the top ten most tweeted scientific dental articles and of tweeting by accounts is examined. The ideal that tweeting about scholarly articles represents curating and informing about state-of-the-art appears not to be realized in practice. We see much presumably human tweeting almost entirely mechanical and devoid of original thought, no evidence of conversation, tweets generated by monomania, duplicate tweeting from many accounts under centralized professional management and tweets generated by bots. Some accounts exemplify the ideal, but they represent less than 10% of tweets. Therefore, any conclusions drawn from twitter data is swamped by the mechanical nature of the bulk of tweeting behavior. In light of these results, we discuss the compatibility of Twitter with the research enterprise as well as some of the financial incentives behind these patterns.

Attitudes Toward the Ethics of Research Using Social Media: A Systematic Review

BACKGROUND

Although primarily used for social networking and often used for social support and dissemination, data on social media platforms are increasingly being used to facilitate research. However, the ethical challenges in conducting social media research remain of great concern. Although much debated in the literature, it is the views of the public that are most pertinent to inform future practice.

OBJECTIVE

The aim of our study was to ascertain attitudes on the ethical considerations of using social media as a data source for research as expressed by social media users and researchers.

METHODS

A systematic review was conducted, wherein 16 databases and 2 Internet search engines were searched in addition to handsearching, reference checking, citation searching, and contacting authors and experts. Studies that conducted any qualitative methods to collect data on attitudes on the ethical implications of research using social media were included. Quality assessment was conducted using the quality of reporting tool (QuaRT) and findings analyzed using inductive thematic synthesis.

RESULTS

In total, 17 studies met the inclusion criteria. Attitudes varied from overly positive with people expressing the views about the essential nature of such research for the public good, to very concerned with views that social media research should not happen. Underlying reasons for this variation related to issues such as the purpose and quality of the research, the researcher affiliation, and the potential harms. The methods used to conduct the research were also important. Many respondents were positive about social media research while adding caveats such as the need for informed consent or use restricted to public platforms only.

CONCLUSIONS

Many conflicting issues contribute to the complexity of good ethical practice in social media research. However, this should not deter researchers from conducting social media research. Each Internet research project requires an individual assessment of its own ethical issues. Guidelines on ethical conduct should be based on current evidence and standardized to avoid discrepancies between, and duplication across, different institutions, taking into consideration different jurisdictions.

Understanding Twitter use by major LIS professional organisations in the United States

Although Twitter has been widely adopted by professional organisations, there has been a lack of understanding and research on its utilisation. This article presents a study that looks into how five major library and information science (LIS) professional organisations in the United States use Twitter, including the American Library Association (ALA), Special Libraries Association (SLA), Association for Library and Information Science Education (ALISE), Association for Information Science and Technology (ASIS&T) and the iSchools. Specifically explored are the characteristics of Twitter usage, such as prevalent topics or contents, type of users involved, as well as the user influence based on number of mentions and retweets. The article also presents the network interactions among the LIS associations on Twitter. A systematic Twitter analysis framework of descriptive analytics, content analytics, user analysis and network analytics with relevant metrics used in this study can be applied to other studies of Twitter use.

The 'who' and 'what' of #diabetes on Twitter

Social media are being increasingly used for health promotion, yet the landscape of users, messages and interactions in such fora is poorly understood. Studies of social media and diabetes have focused mostly on patients, or public agencies addressing it, but have not looked broadly at all of the participants or the diversity of content they contribute. We study Twitter conversations about diabetes through the systematic analysis of 2.5 million tweets collected over 8 months and the interactions between their authors. We address three questions. (1) What themes arise in these tweets? (2) Who are the most influential users? (3) Which type of users contribute to which themes? We answer these questions using a mixed-methods approach, integrating techniques from anthropology, network science and information retrieval such as thematic coding, temporal network analysis and community and topic detection. Diabetes-related tweets fall within broad thematic groups: health information, news, social interaction and commercial. At the same time, humorous messages and references to popular culture appear consistently, more than any other type of tweet. We classify authors according to their temporal 'hub' and 'authority' scores. Whereas the hub landscape is diffuse and fluid over time, top authorities are highly persistent across time and comprise bloggers, advocacy groups and NGOs related to diabetes, as well as for-profit entities without specific diabetes expertise. Top authorities fall into seven interest communities as derived from their Twitter follower network. Our findings have implications for public health professionals and policy makers who seek to use social media as an engagement tool and to inform policy design.

Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework

Mobile crowdsensing is a burgeoning concept that allows smart cities to leverage the sensing power and ubiquitous nature of mobile devices in order to capture and map phenomena of common interest. At the core of any successful mobile crowdsensing application is active user participation, without which the system is of no value in sensing the phenomenon of interest. A major challenge militating against widespread use and adoption of mobile crowdsensing applications is the issue of how to identify the most appropriate incentive mechanism for adequately and efficiently motivating participants. This paper reviews literature on incentive mechanisms for mobile crowdsensing and proposes the concept of SPECTRUM as a guide for inferring the most appropriate type of incentive suited to any given crowdsensing task. Furthermore, the paper highlights research challenges and areas where additional studies related to the different factors outlined in the concept of SPECTRUM are needed to improve citizen participation in mobile crowdsensing. It is envisaged that the broad range of factors covered in SPECTRUM will enable smart cities to efficiently engage citizens in large-scale crowdsensing initiatives. More importantly, the paper is expected to trigger empirical investigations into how various factors as outlined in SPECTRUM can influence the type of incentive mechanism that is considered most appropriate for any given mobile crowdsensing initiative.

Complex dynamics in an alcoholism model with the impact of Twitter

A novel alcoholism model which involves impact of Twitter is formulated. It is shown that the model has multiple equilibria. Stability of all the equilibria are obtained in terms of the basic reproductive number R₀. Using the center manifold theory, the occurrence of backward and forward bifurcation for a certain defined range of R₀ are established. Furthermore, the existence of Hopf bifurcation is also established by regarding the transmission coefficient β as the bifurcation parameter. Numerical simulations and sensitivity analysis on a few parameters are also carried out. Our results show that Twitter can serve as a good indicator of alcoholism model and affect the spread of the drinking.

Studying the impact of blended learning that uses the online PBwiki guided by activity theory on LIS students’ knowledge management

Purpose

This research paper aims to explore the impact of using wiki activities to support a blended learning course (70 per cent in-class and 30 per cent PBwiki activities) on the knowledge management of library and information science (LIS) students compared to 100 per cent in-class learning.

Design/methodology/approach

In the 2015 academic year, the researcher compared an experimental group (41 students) and a control group (41 students). Instruction of the experimental group was based on combining 2 h (70 per cent) of in-class learning and 1 h (30 per cent) of wiki-based learning activities each week. The control group’s experience was 100 per cent in a physical classroom without the use of a wiki. The researcher used a t-test to compare the means of the control and experimental groups in achievement tests and the students’ attitudes based on principles of activity theory (technological, individual and community levels) at 0.05 alpha levels.

Findings

The principal results of the study are that students in the experimental group perform better than those in the control group on the achievement test, learning tracks and attitudes. Learning tracks analysis shows that students in the experimental group had greater participation in different topics of discussion in the PBwiki than did the control group. The first topic discussed by students in the wiki is related to exam revision, and the second topic is related to the course content.

Originality/value

This research paper is useful for readers, parents, students and schools to explore the effectiveness of PBwiki activities to support blended courses in LIS education.