Social media has been transforming political communication dynamics for over a decade. Here using nearly a billion tweets, we analyse the change in Twitter’s news media landscape between the 2016 and 2020 US presidential elections. Using political bias and fact-checking tools, we measure the volume of politically biased content and the number of users propagating such information. We then identify influencers—users with the greatest ability to spread news in the Twitter network. We observe that the fraction of fake and extremely biased content declined between 2016 and 2020. However, results show increasing echo chamber behaviours and latent ideological polarization across the two elections at the user and influencer levels.
Truth of Varying Shades: On Political Fact-Checking and Fake News
We present an analytic study on the language of news media in the context of political fact-checking and fake news detection. We compare the language of real news with that of satire, hoaxes, and propaganda to find linguistic characteristics of untrustworthy text. To probe the feasibility of automatic political fact-checking, we also present a case study based on PolitiFact.com using their factuality judgments on a 6-point scale. Experiments show that while media fact-checking remains to be an open research question, stylistic cues can help determine the truthfulness of text.
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- Award ID(s):
- 1714566
- NSF-PAR ID:
- 10074110
- Date Published:
- Journal Name:
- Conference on Empirical Methods in Natural Language Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
In recent years, the speed at which information disseminates has received an alarming boost from the pervasive usage of social media. To the detriment of political and social stability, this has also made it easier to quickly spread false claims. Due to the sheer volume of information, manual fact-checking seems infeasible, and as a result, computational approaches have been recently explored for automated fact-checking. In spite of the recent advancements in this direction, the critical step of recognizing and prioritizing statements worth fact-checking has received little attention. In this paper, we propose a hybrid approach that combines simple heuristics with supervised machine learning to identify claims made in political debates and speeches, and provide a mechanism to rank them in terms of their "check-worthiness". The viability of our method is demonstrated by evaluations on the English language dataset as part of the Check-worthiness task of the CLEF-2018 Fact Checking Lab.more » « less
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Ideology is at the core of political science research. Yet, there still does not exist general-purpose tools to characterize and predict ideology across different genres of text. To this end, we study Pretrained Language Models using novel ideology-driven pretraining objectives that rely on the comparison of articles on the same story written by media of different ideologies. We further collect a large-scale dataset, consisting of more than 3.6M political news articles, for pretraining. Our model POLITICS outperforms strong baselines and the previous state-of-the-art models on ideology prediction and stance detection tasks. Further analyses show that POLITICS is especially good at understanding long or formally written texts, and is also robust in few-shot learning scenarios.more » « less
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Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the “wisdom of crowds” can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.
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De Cristofaro, Emiliano ; Nakov, Preslav (Ed.)Google’s reviewed claims feature was an early attempt to incorporate additional credibility signals from fact-checking onto the search results page. The feature, which appeared when users searched for the name of a subset of news publishers, was criticized by dozens of publishers for its errors and alleged anticonservative bias. By conducting an audit of news publisher search results and focusing on the critiques of publishers, we find that there is a lack of consensus between fact-checking ecosystem stakeholders that may be important to address in future iterations of public facing fact-checking tools. In particular, we find that a lack of transparency coupled with a lack of consensus on what makes a fact-check relevant to a news article led to the breakdown of reviewed claims.more » « less
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