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1. Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting

   https://doi.org/10.18653/v1/2023.findings-emnlp.45  

   Zou, Kaijian; Zhang, Xinliang; Wu, Winston; Beauchamp, Nicholas; Wang, Lu (November 2023, Findings of the Association for Computational Linguistics: EMNLP 2023)

   News media is expected to uphold unbiased reporting. Yet they may still affect public opinion by selectively including or omitting events that support or contradict their ideological positions. Prior work in NLP has only studied media bias via linguistic style and word usage. In this paper, we study to which degree media balances news reporting and affects consumers through event inclusion or omission. We first introduce the task of detecting both partisan and counter- partisan events: events that support or oppose the author’s political ideology. To conduct our study, we annotate a high-quality dataset, PAC, containing 8 , 511 (counter-)partisan event annotations in 304 news articles from ideologically diverse media outlets. We benchmark PAC to highlight the challenges of this task. Our findings highlight both the ways in which the news subtly shapes opinion and the need for large language models that better understand events within a broader context. Our dataset can be found at https://github.com/ launchnlp/Partisan-Event-Dataset. 

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2. Partisan bias in the identification of fake news

   https://doi.org/10.1016/j.tics.2021.05.001  

   Gawronski, Bertram (September 2021, Trends in Cognitive Sciences)
3. In News We Trust?: Examining Credibility and Sharing Behaviors of Fake News

   https://doi.org/10.1145/3328529.3328554  

   Stefanone, Michael A.; Vollmer, Matthew; Covert, Jessica M. (January 2019, The 10th International Conference on Social Media and Society)
4. People, not search-engine algorithms, choose unreliable or partisan news

   https://doi.org/10.1038/d41586-023-01634-5  

   Mustafaraj, Eni (June 2023, Nature)

   Analysis of people’s web searches and visited websites suggests that it is more likely that they are choosing to engage with partisan or unreliable news than that they are being unduly exposed to it by search-engine algorithms. 

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5. ‘Highly Partisan’ and ‘Blatantly Wrong’: Analyzing News Publishers’ Critiques of Google’s Reviewed Claims

   Lurie, Emma; Mustafaraj, Eni (October 2020, Proceedings of the 2020 Truth and Trust Online Conference (TTO 2020))

   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. 

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