rumor-detection
There are 3 repositories under rumor-detection topic.
Cartus / Automated-Fact-Checking-Resources
Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).
XiaoxiaoMa-MQ / Awesome-Deep-Graph-Anomaly-Detection
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 contributors and boost further research in this area.
TianBian95 / BiGCN
Source Codes: Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks--AAAI 2020
Language:Python 275ICTMCG / fake-news-detection
This repo is a collection of AWESOME things about fake news detection, including papers, code, etc.
Paper list of misinformation research using (multi-modal) large language models, i.e., (M)LLMs.
jerrygaoLondon / RPDNN
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
Language:Python 32Ahren09 / FinerFact
PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection
Language:Python 29lhbrichard / rumor-detection
The public code for paper A Graph Convolutional Encoder and Decoder Model for Rumor Detection which is accepted by DSAA 2020
Language:Python 25- Fake-News-Detection
hosseindamavandi / Fake-News-Detection
This repository contains AI models that identify deceptive content and combat misinformation
Language:Jupyter Notebook 23 ww-rm / weibo-rmdt
Detect rumors on Weibo by PyTorch.
Language:Python 17oraccc / Improving-Rumor-Detection-with-User-Comments
Source Code for TrustCom2022 Accepted Paper " 'Comments Matter and The More The Better': Improving Rumor Detecion with User Comments".
Language:Jupyter Notebook 15xuejianhuang / MST
Multiview Spatio-Temporal Learning with Dual Dynamic Graph Convolutional Networks for Rumor Detection
Language:Python 14joshchang0111 / EMNLP2023-RumorDAS
Original PyTorch Implementation for the EMNLP 2023 Paper "Beyond Detection: A Defend-and-Summarize Strategy for Robust and Interpretable Rumor Analysis on Social Media"
Language:Python 12The 14th National College Student Information Security Competition Entry: "Sina Weibo Rumor Detection System"
Language:JavaScript 7TerryLiu18 / REPORT
This is REPORT model for rumor detection
Language:Python 5JiaWu-Repository / DeepSLRD
Deep Structure Learning for Rumor Detection on Twitter (IJCNN 2019)
Language:Python 4ZavierYang / Rumor-Detection-on-Twitter
Building model in order to identify whether a tweet is rumor
Language:Python 4rajdeep345 / MTLTS
Codes and Datasets for our WSDM 2022 Paper: "MTLTS: A Multi-Task Framework To Obtain Trustworthy Summaries From Crisis-Related Microblogs"
Language:Python 3argh-rumor-detection / ARGH-Rumor-Generation
Data and Demo for ARGH!
Deep Spatial-Temporal Structure Learning for Rumor Detection on Twitter, Neural Computing and Applications, 2020
Language:Python 1richard-peng-xia / Trigger-Identification
This repository stores the implementation of trigger identification task on Shanghai-HK Interdisciplinary Shared Tasks (2022).
Language:Python 1zhiruiluo / deep_feature_fusion_rumor_detection
Source code for the publication "Deep Feature Fusion for Rumor Detection on Twitter"
Language:Python 1ogozcelik / MiDe22
English and Turkish Misinformation Detection Dataset from "MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection"
Language:Python 0cgao-comp / RumorNAS
This is the code for our paper **RumorNAS: Bi-directional Search to Stack Dynamic GNN for Rumor Detection**
Language:PythonPassantAdel / 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.
Language:Jupyter Notebook