Can Community Notes Replace Professional Fact-Checkers?

• URL: http://arxiv.org/abs/2502.14132v1
• Date: Wed, 19 Feb 2025 22:26:39 GMT
• Title: Can Community Notes Replace Professional Fact-Checkers?
• Authors: Nadav Borenstein, Greta Warren, Desmond Elliott, Isabelle Augenstein,
• Abstract summary: Policy changes by Twitter/X and Meta signal a shift away from partnerships with fact-checking organisations. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported.
• Score: 49.5332225129956
• License:
• Abstract: Two commonly-employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on crowdsourced community notes. However, the extent and nature of dependencies between fact-checking and helpful community notes remain unclear. To address these questions, we use language models to annotate a large corpus of Twitter/X community notes with attributes such as topic, cited sources, and whether they refute claims tied to broader misinformation narratives. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported. Fact-checking is especially crucial for notes on posts linked to broader narratives, which are twice as likely to reference fact-checking sources compared to other sources. In conclusion, our results show that successful community moderation heavily relies on professional fact-checking.

Related papers

• Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes [0.03511246202322249]
  The proliferation of misinformation on social media platforms has become a significant concern. This study focuses on the specific feature of Twitter Community Notes, despite its potential role in crowd-sourced fact-checking. We find that the majority of cited sources are news outlets that are left-leaning and are of high factuality, pointing to a potential bias in the platform's community fact-checking.
  arXiv  Detail & Related papers  (2024-06-18T09:47:58Z)
• ManiTweet: A New Benchmark for Identifying Manipulation of News on Social Media [74.93847489218008]
  We present a novel task, identifying manipulation of news on social media, which aims to detect manipulation in social media posts and identify manipulated or inserted information. To study this task, we have proposed a data collection schema and curated a dataset called ManiTweet, consisting of 3.6K pairs of tweets and corresponding articles. Our analysis demonstrates that this task is highly challenging, with large language models (LLMs) yielding unsatisfactory performance.
  arXiv  Detail & Related papers  (2023-05-23T16:40:07Z)
• 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)
• Who Shares Fake News? Uncovering Insights from Social Media Users' Post Histories [0.0]
  We propose that social-media users' own post histories are an underused resource for studying fake-news sharing. We identify cues that distinguish fake-news sharers, predict those most likely to share fake news, and identify promising constructs to build interventions.
  arXiv  Detail & Related papers  (2022-03-20T14:26:20Z)
• 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)
• Community-Based Fact-Checking on Twitter's Birdwatch Platform [0.0]
  Twitter introduced "Birdwatch," a community-driven approach to address misinformation on Twitter. On Birdwatch, users can identify tweets they believe are misleading, write notes that provide context to the tweet and rate the quality of other users' notes. We collect Birdwatch notes and ratings between the introduction of the feature in early 2021 and end of July 2021.
  arXiv  Detail & Related papers  (2021-04-15T00:25:52Z)
• Political Bias and Factualness in News Sharing across more than 100,000 Online Communities [7.892285386961407]
  We conduct the largest study of news sharing on reddit to date, analyzing more than 550 million links spanning 4 years. We find that, compared to left-leaning communities, right-leaning communities have 105% more variance in the political bias of their news sources. We show that extremely biased and low factual content is very concentrated, with 99% of such content being shared in only 0.5% of communities.
  arXiv  Detail & Related papers  (2021-02-17T02:35:13Z)
• Causal Understanding of Fake News Dissemination on Social Media [50.4854427067898]
  We argue that it is critical to understand what user attributes potentially cause users to share fake news. In fake news dissemination, confounders can be characterized by fake news sharing behavior that inherently relates to user attributes and online activities. We propose a principled approach to alleviating selection bias in fake news dissemination.
  arXiv  Detail & Related papers  (2020-10-20T19:37:04Z)
• 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)
• That is a Known Lie: Detecting Previously Fact-Checked Claims [34.30218503006579]
  A large number of fact-checked claims have been accumulated. Politicians like to repeat their favorite statements, true or false, over and over again. It is important to try to save this effort and to avoid wasting time on claims that have already been fact-checked.
  arXiv  Detail & Related papers  (2020-05-12T21:25:37Z)

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.