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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. Preparing for the Worst: Lessons for News Media After Hurricane Maria in Puerto Rico

   https://doi.org/10.1080/17512786.2019.1682941  

   Takahashi, Bruno; Zhang, Qucheng; Chavez, Manuel (January 2019, Journalism Practice)

   Hurricane Maria (2017) was the most devastating hurricane in Puerto Rico in the last 90 years. The entire communication system collapsed, including cellular. Media organizations in Puerto Rico, with the exception of one radio station, were unable to transmit much-needed information during and immediately after Maria made landfall. This study examines changes in journalistic practices, organizational readiness and disaster coverage plans, and infrastructure preparedness almost 18 months after the event. This study extends the limited research examining long-term changes to news media preparedness plans in the context of disasters and expands theoretical understandings of media practices in the context of the hierarchy of influences model. The results suggest that infrastructure damage severely hampered the ability of news organizations to perform their work, but solidarity among media was useful in the immediate aftermath of the disaster. Each media played a differentiated and important role after the disaster based on their resources and organizational structure. The study shows that preparedness plans were inadequate and that changes are slowly been implemented to deal with challenges related to infrastructure, electricity, and technology, but with limited focus on the long-term well-being of media workers. Recommendations to improve responses to future natural disasters are presented. 

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3. Information Switching Patterns of Risk Communication in Social Media during Disasters

   https://doi.org/10.1109/TBDATA.2024.3524828  

   Momin, Khondhaker Al; Sadri, Arif Mohaimin; Olofsson, Kristin; Muraleetharan, KK; Gladwin, Hugh (January 2025, IEEE Transactions on Big Data)

   In an era increasingly affected by natural and human-caused disasters, the role of social media in disaster communication has become ever more critical. Despite substantial research on social media use during crises, a significant gap remains in detecting crisis-related misinformation. Detecting deviations in information is fundamental for identifying and curbing the spread of misinformation. This study introduces a novel Information Switching Pattern Model to identify dynamic shifts in perspectives among users who mention each other in crisisrelated narratives on social media. These shifts serve as evidence of crisis misinformation affecting user-mention network interactions. The study utilizes advanced natural language processing, network science, and census data to analyze geotagged tweets related to compound disaster events in Oklahoma in 2022. The impact of misinformation is revealed by distinct engagement patterns among various user types, such as bots, private organizations, non-profits, government agencies, and news media throughout different disaster stages. These patterns show how different disasters influence public sentiment, highlight the heightened vulnerability of mobile home communities, and underscore the importance of education and transportation access in crisis response. Understanding these engagement patterns is crucial for detecting misinformation and leveraging social media as an effective tool for risk communication during disasters 

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4. All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison

   https://doi.org/10.18653/v1/2023.emnlp-main.957  

   Liu, Yujian; Zhang, Xinliang; Zou, Kaijian; Huang, Ruihong; Beauchamp, Nicholas; Wang, Lu (November 2023, Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing)

   Public opinion is shaped by the information news media provide, and that information in turn may be shaped by the ideological preferences of media outlets. But while much attention has been devoted to media bias via overt ideological language or topic selection, a more unobtrusive way in which the media shape opinion is via the strategic inclusion or omission of partisan events that may support one side or the other. We develop a latent variable-based framework to predict the ideology of news articles by comparing multiple articles on the same story and identifying partisan events whose inclusion or omission reveals ideology. Our experiments first validate the existence of partisan event selection, and then show that article alignment and cross-document comparison detect partisan events and article ideology better than competitive baselines. Our results reveal the high-level form of media bias, which is present even among mainstream media with strong norms of objectivity and nonpartisanship. Our codebase and dataset are available at https://github.com/launchnlp/ATC. 

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5. Where It Really Matters: Few-Shot Environmental Conservation Media Monitoring for Low-Resource Languages

   https://doi.org/10.1609/aaai.v38i20.30218  

   Jain, Sameer; Keh, Sedrick Scott; Chhetri, Shova; Dewan, Karun; Izquierdo, Pablo; Prussmann, Johanna; Shrestha, Pooja; Suárez, César; Shi, Zheyuan Ryan; Li, Lei; et al (March 2024, Proceedings of the AAAI Conference on Artificial Intelligence)

   Environmental conservation organizations routinely monitor news content on conservation in protected areas to maintain situational awareness of developments that can have an environmental impact. Existing automated media monitoring systems require large amounts of data labeled by domain experts, which is only feasible at scale for high-resource languages like English. However, such tools are most needed in the global south where the news of interest is mainly in local low-resource languages, and far fewer experts are available to annotate datasets on a sustainable basis. In this paper, we propose NewsSerow, a method to automatically recognize environmental conservation content in low-resource languages. NewsSerow is a pipeline of summarization, in-context few-shot classification, and self-reflection using large language models (LLMs). Using at most 10 demonstration example news articles in Nepali, NewsSerow significantly outperforms other few-shot methods and can achieve comparable performance with models fully fine-tuned using thousands of examples. With NewsSerow, Organization X has been able to deploy the media monitoring tool in Nepal, significantly reducing their operational burden, and ensuring that AI tools for conservation actually reach the communities that need them the most. NewsSerow has also been deployed for countries with other languages like Colombia. 

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