Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News (2020.emnlp-main)
Copied to clipboard
| 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. |
Similar Papers
Fact-Checking, Fake News, Propaganda, and Media Bias: Truth Seeking in the Post-Truth Era (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | social media has made it easy for everyone to share and spread information online. |
| Approach: | a tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions . |
| Outcome: | The tutorial will offer an overview of the broad and emerging research area of disinformation . it will focus on the latest developments and research directions . |
That is a Known Lie: Detecting Previously Fact-Checked Claims (2020.acl-main)
Copied to clipboard
| 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. |
BREAKING! Presenting Fake News Corpus for Automated Fact Checking (P19-2)
Copied to clipboard
| 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 . |
Fact-Checking Meets Fauxtography: Verifying Claims About Images (D19-1)
Copied to clipboard
| 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. |
CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media (2022.aacl-main)
Copied to clipboard
| Challenge: | Existing systems to automate fact-checking lack credibility in the eyes of the users. |
| Approach: | They propose to perform automatic fact-checking by verifying whether an input claim has been fact- checked by professional fact- checkers and to return back an article that explains their decision. |
| Outcome: | The proposed method improves on the CLEF’21 CheckThat! test set by two points absolute. |
The Battlefront of Combating Misinformation and Coping with Media Bias (2022.aacl-tutorials)
Copied to clipboard
| Challenge: | a growing number of misinformation and misinformation is affecting our daily lives . a tutorial aims to address the challenges of detecting fake news and media bias . |
| Approach: | They provide an overview of the frontier in fighting misinformation . they propose to develop a robust fake news detection system to combat misinformation. |
| Outcome: | This tutorial examines the frontiers of fake news detection and media bias detection . it focuses on how to fact-check information pieces and uncover bias and agenda of news sources . |
Rumor Detection on Social Media: Datasets, Methods and Opportunities (D19-50)
Copied to clipboard
| Challenge: | Social media platforms are used for information gathering, but they also lead to the spreading of rumors and fake news. |
| Approach: | This paper presents a comprehensive list of datasets used for rumor detection . it also reviews the important studies based on what types of information they exploit . |
| Outcome: | This paper presents an overview of the recent studies in the rumor detection field . it provides a comprehensive list of datasets used for rumour detection . |
Fact Checking or Psycholinguistics: How to Distinguish Fake and True Claims? (D19-66)
Copied to clipboard
| 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. |
Scientific Fact-Checking: A Survey of Resources and Approaches (2023.findings-acl)
Copied to clipboard
| Challenge: | Fact-checking is the task of assessing the veracity of factual claims based on credible evidence and background knowledge. |
| Approach: | They propose to automate scientific fact-checking using natural language processing to assess the veracity of factual claims based on credible evidence and background knowledge. |
| Outcome: | The proposed methods can help combat the spread of misinformation and help individuals understand new scientific breakthroughs. |
A Survey on Automated Fact-Checking (2022.tacl-1)
Copied to clipboard
| 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. |