WebThe wide dissemination of fake news is increasingly threatening both individuals and society. Fake news detection aims to train a model on the past news and detect fake … Web20 apr. 2024 · We highlight the entity bias in fake news detection datasets and for the first time, propose to mitigate this bias for better generalization ability of fake news detectors. We design a debiasing framework that is convenient to be deployed along with different fake news detection models.
Generalizing to the Future: Mitigating Entity Bias in Fake News ...
Web6 jul. 2024 · Request PDF On Jul 6, 2024, Lu Cheng and others published Bias Mitigation for Toxicity Detection via Sequential Decisions Find, read and cite all the research you need on ResearchGate Webstruction of fake news datasets for this purpose have been conducted (Shu et al.,2024). Most datasets for fake news detection consist of factual and fake news that actually diffuse over the Internet. The topics and contents of fake news change over time because they are strongly influenced by the interests of the general population (Schmidt et ... pasta too bethel park pa catering menu
Generalizing to the Future: Mitigating Entity Bias in Fake News …
Web6 jul. 2024 · Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media … Web26 jun. 2024 · Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a … WebFake News Detection. It aims at classifying a news piece as real or fake. Existing methods can be roughly grouped as: content-based and social-context-based fake news … pasta topper crossword