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Found 8 papers, 2 papers with code
Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement
Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly.
VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media
Specifically, when modeling the observed data from social media with neural temporal point process, we jointly learn a Gibbs-like distribution of group assignment based on how consistent an assignment is to (1) the account embedding space and (2) the prior knowledge.
Characterizing Online Engagement with Disinformation and Conspiracies in the 2020 U.S. Presidential Election
Identifying and characterizing disinformation in political discourse on social media is critical to ensure the integrity of elections and democratic processes around the world.
COVID-19 Vaccine Misinformation Campaigns and Social Media Narratives
In this work, we investigate misinformation communities and narratives that can contribute to COVID-19 vaccine hesitancy.
Network Inference from a Mixture of Diffusion Models for Fake News Mitigation
Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics.
COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.
NoiseRank: Unsupervised Label Noise Reduction with Dependence Models
Label noise is increasingly prevalent in datasets acquired from noisy channels.
Ranked #22 on
Image Classification
on Clothing1M
(using extra training data)
Combating Fake News: A Survey on Identification and Mitigation Techniques
The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion.
