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

Roitero, Kevin; Soprano, Michael; Portelli, Beatrice; Luise, Massimiliano De; Spina, Damiano; Mea, Vincenzo Della; Serra, Giuseppe; Mizzaro, Stefano; Demartini, Gianluca

doi:10.1007/s00779-021-01604-6

Cited by 9 publications

(5 citation statements)

References 60 publications

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“…Other studies have examined the evolution of misinformation over time, tracking the emergence, spread, and persistence of false narratives. These investigations shed light on the lifespan of misinformation within social networks and provide a deeper understanding of the dynamics of information propagation during public health crises [25].…”

Section: Solutionmentioning

confidence: 96%

A Centrality Metrics Approach to Analyzing Temporal Spread Patterns of Misinformation in Social Networks

Duzen,

Riveni,

Aktas

2024

Preprint

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This study examines misinformation in social networks with well-known network science metrics. We utilize an already available dataset to analyze tweets related to COVID-19 vaccinations. The methodology we use in this study includes the creation of separate networks for retweets, replies, and mentions. We utilized various centrality metrics to assess the importance of individual tweets such as centrality metrics that primarily consider the network’s topology and connectivity, taking into account factors such as the number of connections, the importance of connections , information flow, and indirect influences. One of our objectives is to determine the length of time that tweets linked to unreliable sources exist within a network through reactions. The empirical results of our study reveal a noteworthy association: tweets that have longer durations tend to be posted by influential accounts within the network, in contrast to tweets that have shorter durations and demonstrate reduced influence. Our study contributes to the advancement of our understanding of the dynamics of misinformation spread, by our results regarding misinforma-tion persistence within social networks. Our observations show potential for informing approaches designed to address misinformation and disin-formation, especially in the setting of public health emergencies like as the current COVID-19 outbreak.

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Section: Solutionmentioning

confidence: 96%

A Centrality Metrics Approach to Analyzing Temporal Spread Patterns of Misinformation in Social Networks

Duzen,

Riveni,

Aktas

2024

Preprint

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“…Roitero et al (2020) looked at how crowd workers compare with expert fact-checkers highlighting a good level of agreement between the two. Later, Roitero et al (2021) looked at the longitudinal dimension of crowdsourced truthfulness assessment observing a consistency in the generated labels. La Barbera et al (2020) observed a political bias in crowd-generated truthfulness labels.…”

Section: Related Work Crowdsourcing For Misinformation Detectionmentioning

confidence: 99%

On the Role of Large Language Models in Crowdsourcing Misinformation Assessment

Xu,

Han,

Sadiq

et al. 2024

ICWSM

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The proliferation of online misinformation significantly undermines the credibility of web content. Recently, crowd workers have been successfully employed to assess misinformation to address the limited scalability of professional fact-checkers. An alternative approach to crowdsourcing is the use of large language models (LLMs). These models are however also not perfect. In this paper, we investigate the scenario of crowd workers working in collaboration with LLMs to assess misinformation. We perform a study where we ask crowd workers to judge the truthfulness of statements under different conditions: with and without LLMs labels and explanations. Our results show that crowd workers tend to overestimate truthfulness when exposed to LLM-generated information. Crowd workers are misled by wrong LLM labels, but, on the other hand, their self-reported confidence is lower when they make mistakes due to relying on the LLM. We also observe diverse behaviors among crowd workers when the LLM is presented, indicating that leveraging LLMs can be considered a distinct working strategy.

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“…Researchers and platforms have tried several approaches to ban or otherwise label COVID-19 dis/misinformation content on social media, including fact-checking (Roitero et al, 2021) and de-bunking (Wang et al, 2021) strategies. As mentioned above, many of these approaches are automated.…”

Section: Covid-19 Misinformation Il Interventionsmentioning

confidence: 99%

The Usage and Impact of ICTs during the Covid-19 Pandemic

Yang¹,

Zhu²,

Fichman³

2023

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This book takes a holistic view of the role of information and communication technologies (ICTs) during the pandemic through the lens of social informatics, as it is critical to our understanding of the relations between society and technology. Specific attention is given to various stakeholders and social contexts, with analysis at the individual, group, community, and society levels.Pushing the boundaries of information science research with timely and critical research questions, this edited volume showcases information science research in the context of COVID-19, by specifically accentuating sociotechnical practices, activities, and ICT interventions during the pandemic. Its social informatics focus appeals to a broad audience, and its global and international orientation provides a timely, innovative, and much-needed perspective to information science. This book is unique in its interdisciplinary nature as it consists of research studies on the intersections between ICTs and health, culture, social interaction, civic engagement, information dissemination, work, and education. Chapters apply a range of research methods, including questionnaire surveys, content analyses, and case studies from countries in Asia, Europe, and America, as well as global and international comparisons.The book's primary target audience includes scholars and students in information and library science, particularly those interested in the social aspect of the information society. It may be of interest to information professionals, library practitioners, educators, and information policymakers, as well as scholars and students in science and technology studies, cultural studies, political science, public administration, sociology, and communication studies.

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

Cited by 9 publications

References 60 publications

A Centrality Metrics Approach to Analyzing Temporal Spread Patterns of Misinformation in Social Networks

A Centrality Metrics Approach to Analyzing Temporal Spread Patterns of Misinformation in Social Networks

On the Role of Large Language Models in Crowdsourcing Misinformation Assessment

The Usage and Impact of ICTs during the Covid-19 Pandemic

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