Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning
- 30 January 2026
- book chapter
- Published by Springer Nature
- p. 199-214
Abstract
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This publication has 29 references indexed in Scilit:
- Investigating the Duality of Interpretability and Explainability in Machine Learning2024
- Factuality challenges in the era of large language models and opportunities for fact-checkingNature Machine Intelligence, 2024
- Claim detection for automated fact-checking: A survey on monolingual, multilingual and cross-lingual researchNatural Language Processing Journal, 2024
- DeMis: Data-Efficient Misinformation Detection Using Reinforcement Learning2023
- GraphLIME: Local Interpretable Model Explanations for Graph Neural NetworksIEEE Transactions on Knowledge and Data Engineering, 2022
- A Multimodal Misinformation Detector for COVID-19 Short Videos on TikTok2021
- SiteForge: Detecting and localizing forged images on microblogging platforms using deep convolutional neural networkComputers & Industrial Engineering, 2021
- WELFake: Word Embedding Over Linguistic Features for Fake News DetectionIEEE Transactions on Computational Social Systems, 2021
- Exploiting Multi-domain Visual Information for Fake News Detection2019
- Explaining Explanations: An Overview of Interpretability of Machine Learning2018