ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information

@inproceedings{BarisSchlicht2021ECOLED,
  title={ECOL: Early Detection of COVID Lies Using Content, Prior Knowledge and Source Information},
  author={I. Baris Schlicht and Zeyd Boukhers},
  booktitle={CONSTRAINT@AAAI},
  year={2021}
}
Social media platforms are vulnerable to fake news dissemination, which causes negative consequences such as panic and wrong medication in the healthcare domain. Therefore, it is important to automatically detect fake news in an early stage before they get widely spread. This paper analyzes the impact of incorporating content information, prior knowledge, and credibility of sources into models for the early detection of fake news. We propose a framework modeling those features by using BERT… 
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References

SHOWING 1-10 OF 41 REFERENCES
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
TLDR
A comprehensive repository, FakeHealth, is constructed, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network to mitigate problems of fake health news detection.
Early Detection of Fake News by Utilizing the Credibility of News, Publishers, and Users based on Weakly Supervised Learning
TLDR
A novel structure-aware multi-head attention network (SMAN), which combines the news content, publishing, and reposting relations of publishers and users, to jointly optimize the fake news detection and credibility prediction tasks and can detect fake news in 4 hours with over 91%, which is much faster than the state-of-the-art models.
Fake News Early Detection
TLDR
Experiments conducted on two real-world datasets indicate the proposed method can outperform the state-of-the-art and enable fake news early detection when there is limited content information.
Fake News Detection on Social Media: A Data Mining Perspective
TLDR
This survey presents a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets, and future research directions for fake news detection on socialMedia.
Fake News Early Detection: A Theory-driven Model
TLDR
Experiments conducted on two real-world datasets indicate that the proposed method can outperform the state-of-the-art and enable fake news early detection, even when there is limited content information.
Fighting an Infodemic: COVID-19 Fake News Dataset
TLDR
A manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19 is curate and released, and four machine learning baselines are benchmarked.
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
TLDR
This work jointly leverage the limited amount of clean data along with weak signals from social engagements to train deep neural networks in a meta-learning framework to estimate the quality of different weak instances to detect fake news.
Linked Credibility Reviews for Explainable Misinformation Detection
TLDR
An architecture based on a core concept of Credibility Reviews (CRs) that can be used to build networks of distributed bots that collaborate for misinformation detection is proposed and demonstrates several advantages over existing systems: extensibility, domain-independence, composability, explainability and transparency via provenance.
Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web
TLDR
The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites.
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