Misinformation
164 papers with code • 1 benchmarks • 32 datasets
Datasets
Most implemented papers
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
Identifying public misinformation is a complicated and challenging task.
Explainable Tsetlin Machine framework for fake news detection with credibility score assessment
The proliferation of fake news, i. e., news intentionally spread for misinformation, poses a threat to individuals and society.
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.
Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models
While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic.
Evidence-based Factual Error Correction
This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.
COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning
We propose a self-supervised training strategy where we only need a set of captioned images.
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset
Training these machine learning models require datasets of sufficient scale, diversity and quality.
CSI: A Hybrid Deep Model for Fake News Detection
Specifically, we incorporate the behavior of both parties, users and articles, and the group behavior of users who propagate fake news.
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning
Misinformation such as fake news is one of the big challenges of our society.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
The increasing concern with misinformation has stimulated research efforts on automatic fact checking.