Fake News Detection
Fake news detection is a task in the field of natural language processing aimed at identifying and classifying the authenticity of news articles or other texts. The goal is to develop algorithms that can automatically recognize and flag fake news to combat the spread of misinformation and promote the circulation of accurate information. By enhancing the capability to verify the truthfulness of information, fake news detection has significant application value in maintaining a healthy public opinion environment and protecting public interests.
FNC-1
Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)
RAWFC
Grover-Mega
Text-Transformers + Five-fold five model cross-validation +Pseudo Label Algorithm
LIAR
Hybrid CNNs (Text + All)
COVID-19 Fake News Dataset
Ensemble Model + Heuristic Post-Processing
Hostility Detection Dataset in Hindi
Auxiliary IndicBert
MediaEval2016
PolitiFact
Social media
TextRNN
Weibo NER