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Behavioral Forensics in Social Networks: Identifying Misinformation, Disinformation and Refutation Spreaders Using Machine Learning

Computer Science

Behavioral Forensics in Social Networks: Identifying Misinformation, Disinformation and Refutation Spreaders Using Machine Learning

E. M. Khan, A. Ram, et al.

Dive into the intriguing world of user behavior on social networks with groundbreaking research by Euna Mehnaz Khan, Ayush Ram, Bhavtosh Rath, Emily Vraga, and Jaideep Srivastava. This innovative study tackles misinformation head-on with a new behavioral forensics model that classifies users based on their reactions, providing vital insights into user intent and cybersecurity.

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Playback language: English
Abstract
This paper addresses the increasing spread of misinformation and disinformation on social networks by proposing a novel behavioral forensics model. The model categorizes users into five classes based on their actions after exposure to both misinformation and its refutation, aiming to understand user intent. Network features, extracted using graph embedding models (LINE and PyTorch-BigGraph), are combined with user profile features to train a machine learning model. Evaluated on a Twitter dataset, the model achieves high precision and recall in detecting malicious actors, significantly outperforming baseline models.
Publisher
CIKM 2022
Published On
Oct 26, 2022
Authors
Euna Mehnaz Khan, Ayush Ram, Bhavtosh Rath, Emily Vraga, Jaideep Srivastava
Tags
misinformation
disinformation
social networks
behavioral forensics
machine learning
user classification
network features

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