Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility

• URL: http://arxiv.org/abs/2311.11282v3
• Date: Fri, 24 Jan 2025 15:54:18 GMT
• Title: Differential impact from individual versus collective misinformation tagging on the diversity of Twitter (X) information engagement and mobility
• Abstract summary: In Twitter's new feature, Community Notes, misinformation tagging was peer-reviewed by other fact-checkers before revelation to the poster.<n>With collective misinformation tagging, posters were less likely to retreat from diverse information engagement.
• Score: 3.236485341473765
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fears about the destabilizing impact of misinformation online have motivated individuals and platforms to respond. Individuals have increasingly challenged others' online claims with fact-checks in pursuit of a healthier information ecosystem and to break down echo chambers of self-reinforcing opinion. Using Twitter (now X) data, here we show the consequences of individual misinformation tagging: tagged posters had explored novel political information and expanded topical interests immediately prior, but being tagged caused posters to retreat into information bubbles. These unintended consequences were softened by a collective verification system for misinformation moderation. In Twitter's new feature, Community Notes, misinformation tagging was peer-reviewed by other fact-checkers before revelation to the poster. With collective misinformation tagging, posters were less likely to retreat from diverse information engagement. Detailed comparison demonstrated differences in toxicity, sentiment, readability, and delay in individual versus collective misinformation tagging messages. These findings provide evidence for differential impacts from individual versus collective moderation strategies on the diversity of information engagement and mobility across the information ecosystem.

Related papers

• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)
• Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes [79.79659145328856]
  We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation. We make the case that polarization and misinformation adherence are closely tied.
  arXiv  Detail & Related papers  (2022-06-30T12:34:24Z)
• DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation [72.18912216025029]
  We present DisinfoMeme to help detect disinformation memes. The dataset contains memes mined from Reddit covering three current topics: the COVID-19 pandemic, the Black Lives Matter movement, and veganism/vegetarianism.
  arXiv  Detail & Related papers  (2022-05-25T09:54:59Z)
• Analyzing Behavioral Changes of Twitter Users After Exposure to Misinformation [1.8251012479962594]
  We aim to understand whether general Twitter users changed their behavior after being exposed to misinformation. We compare the before and after behavior of exposed users to determine whether the frequency of the tweets they posted underwent any significant change. We also study the characteristics of two specific user groups, multi-exposure and extreme change groups, which were potentially highly impacted.
  arXiv  Detail & Related papers  (2021-11-01T04:48:07Z)
• Characterizing User Susceptibility to COVID-19 Misinformation on Twitter [40.0762273487125]
  This study attempts to answer it who constitutes the population vulnerable to the online misinformation in the pandemic. We distinguish different types of users, ranging from social bots to humans with various level of engagement with COVID-related misinformation. We then identify users' online features and situational predictors that correlate with their susceptibility to COVID-19 misinformation.
  arXiv  Detail & Related papers  (2021-09-20T13:31:15Z)
• News consumption and social media regulations policy [70.31753171707005]
  We analyze two social media that enforced opposite moderation methods, Twitter and Gab, to assess the interplay between news consumption and content regulation. Our results show that the presence of moderation pursued by Twitter produces a significant reduction of questionable content. The lack of clear regulation on Gab results in the tendency of the user to engage with both types of content, showing a slight preference for the questionable ones which may account for a dissing/endorsement behavior.
  arXiv  Detail & Related papers  (2021-06-07T19:26:32Z)
• Social Media COVID-19 Misinformation Interventions Viewed Positively, But Have Limited Impact [16.484676698355884]
  Social media platforms like Facebook and Twitter rolled out design interventions, including banners linking to authoritative resources and more specific "false information" labels. We found that most participants indicated a positive attitude towards interventions, particularly post-specific labels for misinformation. Still, the majority of participants discovered or corrected misinformation through other means, most commonly web searches, suggesting room for platforms to do more to stem the spread of COVID-19 misinformation.
  arXiv  Detail & Related papers  (2020-12-21T00:02:04Z)
• "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study [0.0]
  Health-related misinformation threatens adherence to public health messaging. Monitoring misinformation on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts.
  arXiv  Detail & Related papers  (2020-12-14T17:24:59Z)
• The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemic [15.885290526721544]
  We focus on tweets related to the COVID-19 pandemic, analyzing the spread of misinformation, professional fact checks, and the crowd response to popular misleading claims about COVID-19. We train a classifier to create a novel dataset of 155,468 COVID-19-related tweets, containing 33,237 false claims and 33,413 refuting arguments. We observe that the surge in misinformation tweets results in a quick response and a corresponding increase in tweets that refute such misinformation.
  arXiv  Detail & Related papers  (2020-11-11T13:48:44Z)
• Information Consumption and Social Response in a Segregated Environment: the Case of Gab [74.5095691235917]
  This work provides a characterization of the interaction patterns within Gab around the COVID-19 topic. We find that there are no strong statistical differences in the social response to questionable and reliable content. Our results provide insights toward the understanding of coordinated inauthentic behavior and on the early-warning of information operation.
  arXiv  Detail & Related papers  (2020-06-03T11:34:25Z)
• Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
  We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms. We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features. We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
  arXiv  Detail & Related papers  (2020-04-20T20:00:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.