Tweet, but verify: epistemic study of information verification on Twitter

Virtual lab coats: The effects of verified source information on social media post credibility

Social media platform's lack of control over its content made way to the fundamental problem of misinformation. As users struggle with determining the truth, social media platforms should strive to empower users to make more accurate credibility judgements. A good starting point is a more accurate perception of the credibility of the message's source. Two pre-registered online experiments (N = 525;N = 590) were conducted to investigate how verified source information affects perceptions of Tweets (study 1) and generic social media posts (study 2). In both studies, participants reviewed posts by an unknown author and rated source and message credibility, as well as likelihood of sharing. Posts varied by the information provided about the account holder: (1) none, (2) the popular method of verified source identity, or (3) verified credential of the account holder (e.g., employer, role), a novel approach. The credential was either relevant to the content of the post or not. Study 1 presented the credential as a badge, whereas study 2 included the credential as both a badge and a signature. During an initial intuitive response, the effects of these cues were generally unpredictable. Yet, after explanation how to interpret the different source cues, two prevalent reasoning errors surfaced. First, participants conflated source authenticity and message credibility. Second, messages from sources with a verified credential were perceived as more credible, regardless of whether this credential was context relevant (i.e., virtual lab coat effect). These reasoning errors are particularly concerning in the context of misinformation. In sum, credential verification as tested in this paper seems ineffective in empowering users to make more accurate credibility judgements. Yet, future research could investigate alternative implementations of this promising technology.

Gaming with health misinformation: a social capital-based study of corrective information sharing factors in social media

Correction is an important tool to reduce the negative impact of health misinformation on social media. In the era of "I share, therefore I am" social media, users actively share corrective information to achieve the "anti-convincing" effect of health misinformation. Focusing on the local Chinese context, this study constructs a structural equation model using social capital as a mediating variable to explore whether usage of Chinese users' social media can promote corrective information sharing by influencing the structural, cognitive, and relational dimensions of social capital and the role of health literacy in corrective information sharing. It was found that social media use did not significantly affect corrective information share willingness but significantly influenced share willingness through social interaction connections, trust, and shared experiences, and share willingness significantly influenced sharing behavior. The moderating effect showed that health literacy played a significant moderating effect in the influence of corrective information share willingness on sharing behavior. This study introduces the three dimensions of social capital at the theoretical level and finds that users will share corrective information for the purpose of social capital accumulation. It also provides empirical evidence for specific practices, including improving users' health literacy and actively mobilizing them to participate in the blocking and management of health misinformation in social media.

Trust, Media Credibility, Social Ties, and the Intention to Share Information Verification in an Age of Fake News

Social media is now the primary form of communication between internet users and has soared in popularity, which has directly impacted the spread of the phenomenon of fake news. Fake news is not only a widespread phenomenon; it is also problematic and dangerous for society. The aim of this study is to understand the phenomenon of fake news better. The study utilised a structural modelling equation in order to identify how Polish society perceives the problem of fake news and assess the extent to which it trusts content that is published on the internet. The key goal was to determine what factors have the most significant influence on the verification of information being viewed on the internet. By deploying the partial least squares method of validation, SmartPLS3 software was used to process the survey results. The strongest positive effect on information verification behaviour was found to be fake news awareness, which was followed by the intention to share information. The research did not consider any clear connections that may exist between the nature of fake news and its recipient; however, much of the fake news that appears on the internet is political in nature. The study can be used by news reporting companies and provides preliminary information for developers responsible for running social media sites as well as users who want to combat and limit the spread of fake news online. This study expands on the available literature related to fake news by identifying the effects on information verification behaviour of fake news awareness and the intention to share data.

Perception of “Fake News” and Potentially Manipulative Content in Digital Media—A Generational Approach

The presence of “fake news” and potentially manipulative content in the media is nothing new, but this area has largely expanded with the emergence of the Internet and digital media, thus opening itself up to anyone who has online access. As a result, there is an increasing amount of such content in the media, especially in digital media. This paper deals with the perception of fake news and potentially manipulative content by various generations—in particular, the perceptions of the young and the middle-aged generations, with the focus being on their ability to recognise, verify, and relate to such content. The results of this study were gained by means of a qualitative methodology applied to focus groups in Bosnia and Herzegovina. The results are presented through a thematic analysis of the differences in perception of “fake news” between these generations, firstly in terms of their apprehension and interpretation of it, and secondly in terms of their relation to it. The authors conclude that both generations lack competence concerning media literacy, and that providing education in the field of digital media might offer a long-term solution for building resistance to “fake news” for future generations.

Can the crowd judge truthfulness? A longitudinal study on recent misinformation about COVID-19

Recently, the misinformation problem has been addressed with a crowdsourcing-based approach: to assess the truthfulness of a statement, instead of relying on a few experts, a crowd of non-expert is exploited. We study whether crowdsourcing is an effective and reliable method to assess truthfulness during a pandemic, targeting statements related to COVID-19, thus addressing (mis)information that is both related to a sensitive and personal issue and very recent as compared to when the judgment is done. In our experiments, crowd workers are asked to assess the truthfulness of statements, and to provide evidence for the assessments. Besides showing that the crowd is able to accurately judge the truthfulness of the statements, we report results on workers' behavior, agreement among workers, effect of aggregation functions, of scales transformations, and of workers background and bias. We perform a longitudinal study by re-launching the task multiple times with both novice and experienced workers, deriving important insights on how the behavior and quality change over time. Our results show that workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e.g., source of information, behavior) impact the quality of the data. The longitudinal study demonstrates that the time-span has a major effect on the quality of the judgments, for both novice and experienced workers. Finally, we provide an extensive failure analysis of the statements misjudged by the crowd-workers.

Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution

Few studies have mined social media platforms to assess environmental concerns. In this study, Twitter was scraped to obtain a ~140,000 tweet dataset related specifically to marine plastic pollution. The goal is to understand what kind of users profiles are tweeting and how and when they do it. In addition, topic modelling and graph theory techniques have allowed us to identify main concerns on this topic: i) impact on wildlife, ii) microplastics/water pollution, iii) estimates/reports, iv) legislation/protection, and v) recycling/cleaning initiatives. Results reveal a scarce influence of organizations involved in research and marine environmental awareness, so some guidelines are depicted that could help to adjust their communication plans. This is relevant to engage society through reliable information, change habits and reinforce sustainable behaviour. A visualization tool has been created to analyze the results over time.

Healthcare Professionals' Role in Social Media Public Health Campaigns: Analysis of Spanish Pro Vaccination Campaign on Twitter

The COVID-19 pandemic has generated a great impact worldwide both on the population health but also on an economic and social level. In this health emergency, a key element has been and still is the need for information, which has become a daily concern for many people. Social media represent powerful tools for searching and gathering health-related information, thus becoming a new place where health authorities need to be present to disseminate information of preventive measures like vaccines against COVID-19, as well as try to block information against these public health measures. The main goal of this study was to analyze the role that healthcare professionals have in Twitter to support the campaign of public institutions on vaccination against COVID-19. To address this study, an analysis of the messages sent on Twitter containing the hashtag #yomevacuno, between 12 December 2020 was developed using the NodeXL software (Social Media Research Foundation, Redwood, CA, USA), focusing on content analysis of tweets and users’ accounts to identify healthcare professionals. The results show that healthcare professionals represent only 11.38% of users, being responsible for 6.35% of impressions generated by the network #yomevacuno. We can observe that traffic information generated by healthcare professionals is not significant in comparison with institutions (p = 0.633), but it is compared to common users (p = 0.0014). The most active healthcare professionals were pharmacists (40.17%), nurses (27.17%), and physicians (12.14%). Their activity (90.43% of messages) was mainly focused on sharing messages generated by other users’ accounts. From original content generated by healthcare professionals, only 78.95% had a favorable storytelling on the vaccine, but without sharing information about vaccines or vaccination. As a conclusion for this study, the participation of healthcare professionals in the dissemination and generation of information within the #yomevacuno communication strategy, led by the Spanish Ministry of Health, has been scarce. We emphasize the need to enhance communication skills in social networks to support public health campaigns through these increasingly important social media.

Use of bot and content flags to limit the spread of misinformation among social networks: a behavior and attitude survey

The COVID-19 infodemic is driven partially by Twitter bots. Flagging bot accounts and the misinformation they share could provide one strategy for preventing the spread of false information online. This article reports on an experiment (N = 299) conducted with participants in the USA to see whether flagging tweets as coming from bot accounts and as containing misinformation can lower participants’ self-reported engagement and attitudes about the tweets. This experiment also showed participants tweets that aligned with their previously held beliefs to determine how flags affect their overall opinions. Results showed that flagging tweets lowered participants’ attitudes about them, though this effect was less pronounced in participants who frequently used social media or consumed more news, especially from Facebook or Fox News. Some participants also changed their opinions after seeing the flagged tweets. The results suggest that social media companies can flag suspicious or inaccurate content as a way to fight misinformation. Flagging could be built into future automated fact-checking systems and other misinformation abatement strategies of the social network analysis and mining community.

Fighting fake news in the COVID-19 era: policy insights from an equilibrium model

The COVID-19 crisis has revealed structural failures in governance and coordination on a global scale. With related policy interventions dependent on verifiable evidence, pandemics require governments to not only consider the input of experts but also ensure that science is translated for public understanding. However, misinformation and fake news, including content shared through social media, compromise the efficacy of evidence-based policy interventions and undermine the credibility of scientific expertise with potentially longer-term consequences. We introduce a formal mathematical model to understand factors influencing the behavior of social media users when encountering fake news. The model illustrates that direct efforts by social media platforms and governments, along with informal pressure from social networks, can reduce the likelihood that users who encounter fake news embrace and further circulate it. This study has implications at a practical level for crisis response in politically fractious settings and at a theoretical level for research about post-truth and the construction of fact.

Location impact on source and linguistic features for information credibility of social media

Purpose

Social media platforms provide a source of information about events. However, this information may not be credible, and the distance between an information source and the event may impact on that credibility. Therefore, the purpose of this paper is to address an understanding of the relationship between sources, physical distance from that event and the impact on credibility in social media.

Design/methodology/approach

In this paper, the authors focus on the impact of location on the distribution of content sources (informativeness and source) for different events, and identify the semantic features of the sources and the content of different credibility levels.

Findings

The study found that source location impacts on the number of sources across different events. Location also impacts on the proportion of semantic features in social media content.

Research limitations/implications

This study illustrated the influence of location on credibility in social media. The study provided an overview of the relationship between content types including semantic features, the source and event locations. However, the authors will include the findings of this study to build the credibility model in the future research.

Practical implications

The results of this study provide a new understanding of reasons behind the overestimation problem in current credibility models when applied to different domains: such models need to be trained on data from the same place of event, as that can make the model more stable.

Originality/value

This study investigates several events – including crisis, politics and entertainment – with steady methodology. This gives new insights about the distribution of sources, credibility and other information types within and outside the country of an event. Also, this study used the power of location to find alternative approaches to assess credibility in social media.

Epistemology in the Era of Fake News

Fake news has recently garnered increased attention across the world. Digital collaboration technologies now enable individuals to share information at unprecedented rates to advance their own ideologies. Much of this sharing occurs via social networking sites (SNSs), whose members may choose to share information without consideration for its authenticity. This research advances our understanding of information verification behaviors among SNS users in the context of fake news. Grounded in literature on the epistemology of testimony and theoretical perspectives on trust, we develop a news verification behavior research model and test six hypotheses with a survey of active SNS users. The empirical results confirm the significance of all proposed hypotheses. Perceptions of news sharers' network (perceived cognitive homogeneity, social tie variety, and trust), perceptions of news authors (fake news awareness and perceived media credibility), and innate intentions to share all influence information verification behaviors among SNS members. Theoretical implications, as well as implications for SNS users and designers, are presented in the light of these findings.

From Conversations to Digital Communication: The Mnemonic Consequences of Consuming and Producing Information via Social Media

Social media has become one of the most powerful and ubiquitous means by which individuals curate, share, and communicate information with their friends, family, and the world at large. Indeed, 90% of the American adolescents are active social media users, as well as 65% of American adults (Perrin, 2015; see also Duggan & Brenner, 2013). Despite this, psychologists are only beginning to understand the mnemonic consequences associated with social media use. In this article, we will distill this nascent literature by focusing on two primary factors: the type of information (personal vs. public) and the role (producer vs. consumer) individuals play when engaging with social media. In particular, we will highlight research examining induced forgetting for personal information as well as false memories and truthiness for public information. We will end by providing some tentative conclusions and a discussion of areas in need of additional research that will provide a more holistic understanding of the mnemonic consequences associated with social media use.

Analysing How People Orient to and Spread Rumours in Social Media by Looking at Conversational Threads

As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim.