| Challenge: | Recent explosion of false claims in social media has led to manual fact-checking initiatives . however, existing methods are inadequate to deal with the growing number of false content claims. |
| Approach: | They propose to model claims about images using a new dataset to examine the relationship between the image and the claim. |
| Outcome: | The proposed method improves on the baseline and will enable future research on fact-checking claims about images. |
Similar Papers
That is a Known Lie: Detecting Previously Fact-Checked Claims (2020.acl-main)
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| Challenge: | a large number of fact-checked claims have been accumulated over the years . despite the importance of fact checking, it has been largely ignored by the research community . |
| Approach: | They propose to automate fact-checking by focusing on claims that have already been fact-tested . they propose to use specialized datasets to compare different methods . |
| Outcome: | The proposed task shows that it improves over state-of-the-art methods. |
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
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| Challenge: | fabricated stories and hoaxes are still pervading our cyberspace. |
| Approach: | They propose a framework to search for fact-checking articles that address the content of an original tweet that may contain misinformation posted by online users. |
| Outcome: | The proposed framework can detect and disseminate fake news on real-world datasets and warn fake news posters and online users about misinformation. |
Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? (D19-66)
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| Challenge: | Using psycholinguistic features to distinguish lies from true statements is a difficult task and a problem to be solved. |
| Approach: | They compare psycholinguistic text features with fact checking approaches to distinguish lies from true statements using data from a large ongoing study. |
| Outcome: | The proposed methods outperform both fact checking and human baselines but the accuracy is not high. |
A Survey on Automated Fact-Checking (2022.tacl-1)
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| Challenge: | Fact-checking is an essential task in journalism due to the speed with which information and misinformation can spread in the media ecosystem. |
| Approach: | They propose to use natural language processing to automate fact-checking by identifying common concepts and defining definitions. |
| Outcome: | The proposed method can predict the veracity of claims using natural language processing, machine learning, and databases. |
“Image, Tell me your story!” Predicting the original meta-context of visual misinformation (2024.emnlp-main)
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| Challenge: | a surge of visual misinformation poses a growing threat to our society. |
| Approach: | They propose to use images to contextualize images to establish original meta-context . they use a framework that collects evidence and questions from the open web to ground the image . |
| Outcome: | The proposed approach helps human fact-checkers identify misinformation . it uses image contextualization to establish the original meta-context of the image . |
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (2022.findings-emnlp)
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| Challenge: | Recent years have brought us a proliferation of false claims online, which spread fast . fact-checkers have been using automated fact-finding to verify claims . |
| Approach: | They propose a system that can detect claims that can be fact-checked by a given database . they create a manually annotated document dataset and propose evaluation measures . |
| Outcome: | The proposed system achieves sizable performance gains over strong baselines. |
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
ChartCheck: Explainable Fact-Checking over Real-World Chart Images (2024.findings-acl)
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| Challenge: | Data visualizations are often used to summarize and communicate key information, but they can also be misused to spread misinformation and promote agendas. |
| Approach: | They propose a dataset for explainable fact-checking against real-world charts that uses vision-language and chart-to-table models to evaluate the validity of the dataset. |
| Outcome: | The proposed model is based on vision-language and chart-to-table models and proposes a baseline to the community. |
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 . |
| Approach: | They introduce manually verified corpus of compelling fake and questionable news articles on the USA politics . they plan to extend the corpus in the future and use it for automated fake news detection. |
| Outcome: | The proposed model is based on linguistic features and will be extended in the future . it will be used to improve the existing model and improve the tools in the field of fake news detection . |
ClaimDiff: Comparing and Contrasting Claims on Contentious Issues (2023.findings-acl)
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| Challenge: | Using fact verification tasks, however, can not detect subtle differences in factually consistent claims, which might bias the readers. |
| Approach: | They propose a novel dataset that primarily focuses on comparing the nuance between claim pairs. |
| Outcome: | The proposed dataset shows that human-labeled 2,941 claim pairs are weaker than baselines, showing a 19% absolute gap with the baselines. |