Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes
- URL: http://arxiv.org/abs/2406.12444v1
- Date: Tue, 18 Jun 2024 09:47:58 GMT
- Title: Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes
- Authors: Uku Kangur, Roshni Chakraborty, Rajesh Sharma,
- Abstract summary: 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.
- Score: 0.03511246202322249
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In recent years, the proliferation of misinformation on social media platforms has become a significant concern. Initially designed for sharing information and fostering social connections, platforms like Twitter (now rebranded as X) have also unfortunately become conduits for spreading misinformation. To mitigate this, these platforms have implemented various mechanisms, including the recent suggestion to use crowd-sourced non-expert fact-checkers to enhance the scalability and efficiency of content vetting. An example of this is the introduction of Community Notes on Twitter. While previous research has extensively explored various aspects of Twitter tweets, such as information diffusion, sentiment analytics and opinion summarization, there has been a limited focus on the specific feature of Twitter Community Notes, despite its potential role in crowd-sourced fact-checking. Prior research on Twitter Community Notes has involved empirical analysis of the feature's dataset and comparative studies that also include other methods like expert fact-checking. Distinguishing itself from prior works, our study covers a multi-faceted analysis of sources and audience perception within Community Notes. 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. Left biased and low factuality sources validate tweets more, while Center sources are used more often to refute tweet content. Additionally, source factuality significantly influences public agreement and helpfulness of the notes, highlighting the effectiveness of the Community Notes Ranking algorithm. These findings showcase the impact and biases inherent in community-based fact-checking initiatives.
Related papers
- Susceptibility to Unreliable Information Sources: Swift Adoption with
Minimal Exposure [10.288282142373976]
Users tend to adopt low-credibility sources with fewer exposures than high-credibility sources.
The adoption of information sources often mirrors users' prior exposure to sources with comparable credibility levels.
arXiv Detail & Related papers (2023-11-09T20:16:06Z) - 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) - Trust and Believe -- Should We? Evaluating the Trustworthiness of
Twitter Users [5.695742189917657]
Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society.
In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities.
Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
arXiv Detail & Related papers (2022-10-27T06:57:19Z) - Rumor Detection with Self-supervised Learning on Texts and Social Graph [101.94546286960642]
We propose contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better.
We term this framework as Self-supervised Rumor Detection (SRD)
Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
arXiv Detail & Related papers (2022-04-19T12:10:03Z) - 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) - Dynamics of Cross-Platform Attention to Retracted Papers [25.179837269945015]
Retracted papers circulate widely on social media, digital news and other websites before their official retraction.
We quantify the amount and type of attention 3,851 retracted papers received over time in different online platforms.
arXiv Detail & Related papers (2021-10-15T01:40:20Z) - 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) - 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) - 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.