Source Codes: Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks--AAAI 2020

Awesome graph anomaly detection techniques built based on deep learning frameworks. Collections of commonly used datasets, papers as well as implementations are listed in this github repository. We also invite researchers interested in anomaly detection, graph representation learning, and graph anomaly detection to join this project as contribut…

Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

This repo is a collection of AWESOME things about fake news detection, including papers, code, etc.

This repository contains code for the paper "RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media" By J. Gao, S. Han, X. Song, et al. - LREC 2020

Deep Structure Learning for Rumor Detection on Twitter (IJCNN 2019)

Paper list of misinformation research using (multi-modal) large language models, i.e., (M)LLMs.

PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection

The public code for paper A Graph Convolutional Encoder and Decoder Model for Rumor Detection which is accepted by DSAA 2020

Detect rumors on Weibo by PyTorch.

Codes and Datasets for our WSDM 2022 Paper: "MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs"

Building model in order to identify whether a tweet is rumor

English and Turkish Misinformation Detection Dataset from "MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection"

Deep Spatial-Temporal Structure Learning for Rumor Detection on Twitter, Neural Computing and Applications, 2020

Data and Demo for ARGH!

The 14th National College Student Information Security Competition Entry: "Sina Weibo Rumor Detection System"

This repository stores the implementation of trigger identification task on Shanghai-HK Interdisciplinary Shared Tasks (2022).

This is REPORT model for rumor detection

In this project, I developed a machine learning model to detect rumors on Twitter. The model is based on a Random Forest classifier and was trained on a dataset of tweets. The key steps involved data preprocessing, feature extraction, and model training. The final model achieved impressive performance metrics.

Original PyTorch Implementation for the EMNLP 2023 Paper "Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media"