Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness

Zhou, Cheng; Li, Kai; Lu, Yifeng

doi:10.1016/j.ipm.2021.102679

Cited by 62 publications

(55 citation statements)

References 77 publications

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“…In the social media context, uncertainty leads to inefficient communication and potential cognitive dissonance among the public (Dwivedi et al, 2018). Our results accord with those of Zhou et al (2021), who identified uncertainty as a salient feature of rumor propagation. Cyberbullies convey uncertainty by using nouns with modifiers (e.g., fillers, expressions of certainty, and tentative and adjunct words).…”

Section: General Linguistic Features Of Cyberbullyingsupporting

confidence: 85%

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

et al. 2022

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As a worldwide epidemic in the digital age, cyberbullying is a pertinent but understudied concern—especially from the perspective of language. Elucidating the linguistic features of cyberbullying is critical both to preventing it and to cultivating ethical and responsible digital citizens. In this study, a mixed-method approach integrating lexical feature analysis, sentiment polarity analysis, and semantic network analysis was adopted to develop a deeper understanding of cyberbullying language. Five cyberbullying cases on Chinese social media were analyzed to uncover explicit and implicit linguistic features. Results indicated that cyberbullying comments had significantly different linguistic profiles than non-bullying comments and that explicit and implicit bullying were distinct. The content of cases further suggested that cyberbullying language varied in the use of words, types of cyberbullying, and sentiment polarity. These findings offer useful insight for designing automatic cyberbullying detection tools for Chinese social networking platforms. Implications also offer guidance for regulating cyberbullying and fostering ethical and responsible digital citizens.

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Section: General Linguistic Features Of Cyberbullyingsupporting

confidence: 85%

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

et al. 2022

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“…He found the content of the tweet contains some apparent ambiguous words like approximately, which was used to excuse the activity of police officers. According to the social support theory [48], rumors with persuasive and uncertain words are more likely to be disseminated on social media. Individuals with similar experiences are inclined to obtain social support and start to trust those kinds of rumormongers and even nurtured to retweet the rumors.…”

Section: Case Studymentioning

confidence: 99%

RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

Wang,

Du,

Chen

et al. 2022

Preprint

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With the development of social media, various rumors can be easily spread on the Internet and such rumors can have serious negative effects on society. Thus, it has become a critical task for social media platforms to deal with suspected rumors. However, due to the lack of effective tools, it is often difficult for platform administrators to analyze and validate rumors from a large volume of information on a social media platform efficiently. We have worked closely with social media platform administrators for four months to summarize their requirements of identifying and analyzing rumors, and further proposed an interactive visual analytics system, RumorLens, to help them deal with the rumor efficiently and gain an in-depth understanding of the patterns of rumor spreading. Ru-morLens integrates natural language processing (NLP) and other data processing techniques with visualization techniques to facilitate interactive analysis and validation of suspected rumors. We propose well-coordinated visualizations to provide users with three levels of details of suspected rumors: an overview displays both spatial distribution and temporal evolution of suspected rumors; a projection view leverages a metaphor-based glyph to represent each suspected rumor and further enable users to gain a quick understanding of their overall characteristics and similarity with each other; a propagation view visualizes the dynamic spreading details of a suspected rumor with a novel circular visualization design, and facilitates interactive analysis and validation of rumors

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“…Bode and Vraga [ 19 ] defined misinformation as “the factually incorrect information that is not backed up with evidence.” Zhou et al [ 20 ] showed that misinformation with emotional and comparative terms is more likely to spread than correct information.…”

Section: Introductionmentioning

confidence: 99%

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

Zhao¹,

Zhu²,

Wan³

et al. 2022

J Med Internet Res

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Background During global health crises such as the COVID-19 pandemic, rapid spread of misinformation on social media has occurred. The misinformation associated with COVID-19 has been analyzed, but little attention has been paid to developing a comprehensive analytical framework to study its spread on social media. Objective We propose an elaboration likelihood model–based theoretical model to understand the persuasion process of COVID-19–related misinformation on social media. Methods The proposed model incorporates the central route feature (content feature) and peripheral features (including creator authority, social proof, and emotion). The central-level COVID-19–related misinformation feature includes five topics: medical information, social issues and people’s livelihoods, government response, epidemic spread, and international issues. First, we created a data set of COVID-19 pandemic–related misinformation based on fact-checking sources and a data set of posts that contained this misinformation on real-world social media. Based on the collected posts, we analyzed the dissemination patterns. Results Our data set included 11,450 misinformation posts, with medical misinformation as the largest category (n=5359, 46.80%). Moreover, the results suggest that both the least (4660/11,301, 41.24%) and most (2320/11,301, 20.53%) active users are prone to sharing misinformation. Further, posts related to international topics that have the greatest chance of producing a profound and lasting impact on social media exhibited the highest distribution depth (maximum depth=14) and width (maximum width=2355). Additionally, 97.00% (2364/2437) of the spread was characterized by radiation dissemination. Conclusions Our proposed model and findings could help to combat the spread of misinformation by detecting suspicious users and identifying propagation characteristics.

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Linguistic characteristics and the dissemination of misinformation in social media: The moderating effect of information richness

Cited by 62 publications

References 77 publications

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

To Be Ethical and Responsible Digital Citizens or Not: A Linguistic Analysis of Cyberbullying on Social Media

RumorLens: Interactive Analysis and Validation of Suspected Rumors on Social Media

Understanding How and by Whom COVID-19 Misinformation is Spread on Social Media: Coding and Network Analyses

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