Fact vs. Opinion: the Role of Argumentation Features in News Classification (2020.coling-main)
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| Challenge: | A 2018 study led by the Media Insight Project showed that most journalists think that their news organizations should clearly mark what is news reporting and what is commentary or opinion in order to combat fake news and gain public trust. |
| Approach: | They propose to classify news articles into newsstories and opinion pieces using models that aim to sup-plement the article content representation with argumentation features. |
| Outcome: | The proposed model outperforms linguistic features and improves on fine-tuned transformer-based models on data from publishers. |
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| Challenge: | Important efforts to characterize news media outlets in terms of their political bias and factuality are labor-intensive and prone to human biases. |
| Approach: | They propose a method that emulates criteria used by professional fact-checkers to assess the factuality and political bias of an entire outlet. |
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Predicting Factuality of Reporting and Bias of News Media Sources (D18-1)
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| Challenge: | a new study examines the factuality of news media and its biases . social media has democratized content creation and spread information online . |
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Analyzing the Persuasive Effect of Style in News Editorial Argumentation (2020.acl-main)
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| Challenge: | Existing research has investigated the persuasive effect of content and style on argumentative content. |
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Automatic Fake News Detection: Are Models Learning to Reason? (2021.acl-short)
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Demystifying Neural Fake News via Linguistic Feature-Based Interpretation (2022.coling-1)
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| Challenge: | Recent advances to neural fake news generators have made it difficult to understand how misinformation generated by these models may best be confronted. |
| Approach: | They conduct feature-based analysis to gain an interpretative understanding of the linguistic attributes that neural fake news generators may most effectively exploit. |
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News Editorials: Towards Summarizing Long Argumentative Texts (2020.coling-main)
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| Challenge: | Using news summarization, we aim to target opinionated articles with a well-defined argumentation structure. |
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| Outcome: | The summarization of opinionated articles with a well-defined argumentation structure is evaluated using a tailored annotation scheme. |
Reports of personal experiences and stories in argumentation: datasets and analysis (2022.acl-long)
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| Challenge: | Personal experiences and stories are important in argumentation, but they are not considered in the social sciences. |
| Approach: | They propose to use annotated documents to scale-up the analysis using existing annotations. |
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A Survey on Predicting the Factuality and the Bias of News Media (2024.findings-acl)
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| Challenge: | a growing number of scholars are profiling entire news outlets to profile fake content . political bias detection is also an important topic, but the two problems have been addressed separately . |
| Approach: | They argue that media profiling should be based on factuality and bias together . they argue that it is difficult to fact-check every single suspicious claim or article manually . |
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Discovering Biased News Articles Leveraging Multiple Human Annotations (2020.lrec-1)
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| Challenge: | Political propaganda and one-sided views can be found in the news and can cause distrust in media. |
| Approach: | They propose to annotate politically biased news articles by an algorithm annotated by domain experts and crowd workers and to compare them to crowd workers. |
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All Things Considered: Detecting Partisan Events from News Media with Cross-Article Comparison (2023.emnlp-main)
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| Challenge: | a recent study shows that media influence opinion via the inclusion or omission of partisan events. |
| Approach: | They 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. |
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