A Modality-level Explainable Framework for Misinformation Checking in Social Networks

8 December 2022

Papers citing "A Modality-level Explainable Framework for Misinformation Checking in Social Networks"

9 / 9 papers shown

Title
MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset Dan Saattrup Nielsen Ryan McConville 61 79 0 23 Feb 2022
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 133 843 0 16 Sep 2020
BERTweet: A pre-trained language model for English Tweets Dat Quoc Nguyen Thanh Tien Vu A. Nguyen VLM 96 917 0 20 May 2020
GraphLIME: Local Interpretable Model Explanations for Graph Neural Networks Q. Huang M. Yamada Yuan Tian Dinesh Singh Dawei Yin Yi-Ju Chang FAtt 90 357 0 17 Jan 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 514 42,449 0 03 Dec 2019
Explaining Explanations: An Overview of Interpretability of Machine Learning Leilani H. Gilpin David Bau Ben Z. Yuan Ayesha Bajwa Michael A. Specter Lalana Kagal XAI 95 1,857 0 31 May 2018
Graph Attention Networks Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio GNN 479 20,164 0 30 Oct 2017
Fake News Detection on Social Media: A Data Mining Perspective Kai Shu A. Sliva Suhang Wang Jiliang Tang Huan Liu GNN EgoV 85 2,782 0 07 Aug 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,115 0 22 Dec 2014