Fake News Detection
151 papers with code • 9 benchmarks • 25 datasets
Fake News Detection is a natural language processing task that involves identifying and classifying news articles or other types of text as real or fake. The goal of fake news detection is to develop algorithms that can automatically identify and flag fake news articles, which can be used to combat misinformation and promote the dissemination of accurate information.
Benchmarks
These leaderboards are used to track progress in Fake News Detection
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Libraries
Use these libraries to find Fake News Detection models and implementationsDatasets
LIAR
RealNews
Weibo NER
FakeNewsNet
Snopes
FNC-1
Latest papers
Detecting and Grounding Multi-Modal Media Manipulation and Beyond
rshaojimmy/multimodal-deepfake
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HAMMER performs 1) manipulation-aware contrastive learning between two uni-modal encoders as shallow manipulation reasoning, and 2) modality-aware cross-attention by multi-modal aggregator as deep manipulation reasoning.
Bad Actor, Good Advisor: Exploring the Role of Large Language Models in Fake News Detection
ictmcg/arg
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To instantiate this proposal, we design an adaptive rationale guidance network for fake news detection (ARG), in which SLMs selectively acquire insights on news analysis from the LLMs' rationales.
A Survey on Interpretable Cross-modal Reasoning
ZuyiZhou/Awesome-Interpretable-Cross-modal-Reasoning
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In recent years, cross-modal reasoning (CMR), the process of understanding and reasoning across different modalities, has emerged as a pivotal area with applications spanning from multimedia analysis to healthcare diagnostics.
Performance Analysis of Transformer Based Models (BERT, ALBERT and RoBERTa) in Fake News Detection
shafna81/fakenewsdetection
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However, some studies suggest the performance can be improved with the use of improved BERT models known as ALBERT and RoBERTa.
How Good Are SOTA Fake News Detectors
Automatic fake news detection with machine learning can prevent the dissemination of false statements before they gain many views.
Tackling Fake News in Bengali: Unraveling the Impact of Summarization vs. Augmentation on Pre-trained Language Models
In this paper, we propose a methodology consisting of four distinct approaches to classify fake news articles in Bengali using summarization and augmentation techniques with five pre-trained language models.
Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection
ictmcg/ftt-acl23
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In this paper, we observe that the appearances of news events on the same topic may display discernible patterns over time, and posit that such patterns can assist in selecting training instances that could make the model adapt better to future data.
3HAN: A Deep Neural Network for Fake News Detection
ni9elf/3han
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The rapid spread of fake news is a serious problem calling for AI solutions.
A Preliminary Study of ChatGPT on News Recommendation: Personalization, Provider Fairness, Fake News
Considering the growing reliance on ChatGPT for language tasks, the importance of news recommendation in addressing social issues, and the trend of using language models in recommendations, this study conducts an initial investigation of ChatGPT's performance in news recommendations, focusing on three perspectives: personalized news recommendation, news provider fairness, and fake news detection.
LTCR: Long-Text Chinese Rumor Detection Dataset
False information can spread quickly on social media, negatively influencing the citizens' behaviors and responses to social events.
