On mission Twitter Profiles: A Study of Selective Toxic Behavior

• URL: http://arxiv.org/abs/2401.14252v1
• Date: Thu, 25 Jan 2024 15:42:36 GMT
• Title: On mission Twitter Profiles: A Study of Selective Toxic Behavior
• Authors: Hina Qayyum, Muhammad Ikram, Benjamin Zi Hao Zhao, an D. Wood, Nicolas Kourtellis, Mohamed Ali Kaafar
• Abstract summary: This study aims to characterize profiles potentially used for influence operations, termed 'on-mission profiles' Longitudinal data from 138K Twitter or X, profiles and 293M tweets enables profiling based on theme diversity.
• Score: 5.0157204307764625
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The argument for persistent social media influence campaigns, often funded by malicious entities, is gaining traction. These entities utilize instrumented profiles to disseminate divisive content and disinformation, shaping public perception. Despite ample evidence of these instrumented profiles, few identification methods exist to locate them in the wild. To evade detection and appear genuine, small clusters of instrumented profiles engage in unrelated discussions, diverting attention from their true goals. This strategic thematic diversity conceals their selective polarity towards certain topics and fosters public trust. This study aims to characterize profiles potentially used for influence operations, termed 'on-mission profiles,' relying solely on thematic content diversity within unlabeled data. Distinguishing this work is its focus on content volume and toxicity towards specific themes. Longitudinal data from 138K Twitter or X, profiles and 293M tweets enables profiling based on theme diversity. High thematic diversity groups predominantly produce toxic content concerning specific themes, like politics, health, and news classifying them as 'on-mission' profiles. Using the identified ``on-mission" profiles, we design a classifier for unseen, unlabeled data. Employing a linear SVM model, we train and test it on an 80/20% split of the most diverse profiles. The classifier achieves a flawless 100% accuracy, facilitating the discovery of previously unknown ``on-mission" profiles in the wild.

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