Duplicating Deceit: Inauthentic Behavior Among Indian Misinformation Duplicators on X/Twitter

2025-07-17

AI Summary

This study investigates unnatural coordinated amplification of AltNews-verified misinformation across multiple accounts on India’s X/Twitter platform. Analyzing over 12 million tweets, we employ large-scale behavioral modeling, temporal clustering, and resurrection-style propagation detection to reveal that repetitive misinformation dissemination is predominantly driven by human coordination—not bots. To address this, we propose TweeXster, the first framework for systematically identifying account clusters repeatedly disseminating malicious or abusive content. TweeXster integrates time-sensitive collaborative behavioral representations with robust clustering algorithms, enabling precise detection of thousands of manipulative account clusters in real-world data. Our findings empirically confirm that human-driven coordination underpins “copy-style” misinformation propagation—a critical mechanism previously undercharacterized. The framework delivers an interpretable, deployable detection paradigm for platform-level governance, advancing both technical methodology and policy-relevant understanding of coordinated inauthentic behavior.

Technology Category

• Application Domains: Misinformation & Fake News
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Web Mining and Content Analysis: Content-based information diffusion
• Responsible Web: Mitigating misinformation and disinformation
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

This paper investigates inauthentic duplication on social media, where multiple accounts share identical misinformation tweets. Leveraging a dataset of misinformation verified by AltNews, an Indian fact-checking organization, we analyze over 12 million posts from 5,493 accounts known to have duplicated such content. Contrary to common assumptions that bots are primarily responsible for spreading false information, fewer than 1% of these accounts exhibit bot-like behavior. We present TweeXster, a framework for detecting and analyzing duplication campaigns, revealing clusters of accounts involved in repeated and sometimes revived dissemination of false or abusive content.

Problem

• Detects inauthentic duplication of misinformation on X/Twitter
• Analyzes human-driven spread of false content, not bots
• Introduces TweeXster to identify duplication campaign clusters

Innovation

• TweeXster framework detects duplication campaigns
• Analyzes clusters of accounts spreading misinformation
• Fewer than 1% accounts show bot-like behavior