Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media

Author:

Alvari Hamidreza1,Shaabani Elham1,Sarkar Soumajyoti1,Beigi Ghazaleh1,Shakarian Paulo2

Affiliation:

1. Arizona State Univ.

2. Arizona State University

Publisher

ACM

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1. A Survey on the Applications of Semi-supervised Learning to Cyber-security;ACM Computing Surveys;2024-06-22

2. Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global Elections;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

3. Toward Mitigating Misinformation and Social Media Manipulation in LLM Era;Companion Proceedings of the ACM Web Conference 2024;2024-05-13

4. An Accurate and Efficient Algorithm to Identify Malicious Nodes of a Graph;IEEE Transactions on Information Forensics and Security;2024

5. ContrastFaux: Sparse Semi-supervised Fauxtography Detection on the Web using Multi-view Contrastive Learning;Proceedings of the ACM Web Conference 2023;2023-04-30

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