FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias (2022.lrec-1)
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| Challenge: | Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. |
| Approach: | They propose a new reddit dataset for fake news spreader analysis, called FACTOID, which tracks political discussions on Reddit since the beginning of 2020. |
| Outcome: | The proposed dataset contains over 4K users with 3.4M posts and includes their credibility level (very low to very high) and political bias strength (extreme right to extreme left). |
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| Challenge: | Existing collections of fake news articles contain claims fact-checked for veracity . Developing fake news detection models requires annotated collections of real and fake news. |
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Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study (2023.emnlp-main)
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| Challenge: | a network of doppelganger websites (impersonating genuine news sites) was discovered in 2022 . a novel dataset enables studies of disinformation networks and the training of NLP tools for disinformation detection. |
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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 . |
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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. |
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Identifying and Understanding User Reactions to Deceptive and Trusted Social News Sources (P18-2)
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| Challenge: | a new study examines how users react to news sources with different levels of credibility . a recent study found that 59% of bitly-URLs on Twitter are shared without ever being read . |
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Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection (2020.lrec-1)
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| Challenge: | Prior fake news datasets lack multimodal text and image data, metadata, comment data, and fine-grained classification at the scale and breadth of their datasets. |
| Approach: | They propose to use a multimodal dataset to build a machine learning classification model that uses text and image data to classify fake news. |
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BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
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| Challenge: | a new study shows that fake news spreads faster than mainstream articles on the same topic . however, there is no dataset containing compelling fake and questionable news articles . |
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Annotating and Analyzing Biased Sentences in News Articles using Crowdsourcing (2020.lrec-1)
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| Challenge: | a lack of publicly available news bias datasets has hindered efforts to detect subtle biases in news articles. |
| Approach: | They propose a news bias dataset which contains sentences with bias labels . they propose to use the dataset to develop and evaluate methods for detecting news bias . |
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How Do Social Bots Participate in Misinformation Spread? A Comprehensive Dataset and Analysis (2025.emnlp-main)
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| Challenge: | Social media platforms provide an ideal environment to spread misinformation, where social bots can accelerate the spread. |
| Approach: | They construct a large-scale dataset that includes annotations for misinformation and social bots on the Sina Weibo platform. |
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Words are the Window to the Soul: Language-based User Representations for Fake News Detection (2020.coling-main)
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| Challenge: | Existing studies on fake news classification focus on textual content, but also social context in which news are consumed. |
| Approach: | They propose a model that creates representations of individuals on social media based only on the language they produce and uses them to detect fake news. |
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