# Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls arXiv: https://arxiv.org/abs/2210.08786 ยท HTML: https://mlsanity.com/paper/2210.08786/ ## Key facts - Cited by 8 papers in the map (data through 14 Oct 2026). - Builds on 21 of its 71 references that are in the map. - First posted on arXiv 17 Oct 2022. ## Builds on (most cited first, 21 of 21) - [The Rise of Social Bots](https://mlsanity.com/paper/1407.5225/) (2014) - [Deep Neural Networks for Bot Detection](https://mlsanity.com/paper/1802.04289/) (2018) - [Echo Chambers: Emotional Contagion and Group Polarization on Facebook](https://mlsanity.com/paper/1607.01032/) (2016) - [RTbust: Exploiting Temporal Patterns for Botnet Detection on Twitter](https://mlsanity.com/paper/1902.04506/) (2019) - [Bots increase exposure to negative and inflammatory content in online social systems](https://mlsanity.com/paper/1802.07292/) (2018) - [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020) - [Online misinformation is linked to early COVID-19 vaccination hesitancy and refusal](https://mlsanity.com/paper/2104.10635/) (2021) - [Characterizing the 2016 Russian IRA Influence Campaign](https://mlsanity.com/paper/1812.01997/) (2018) - [Still out there: Modeling and Identifying Russian Troll Accounts on Twitter](https://mlsanity.com/paper/1901.11162/) (2019) - [TROLLMAGNIFIER: Detecting State-Sponsored Troll Accounts on Reddit](https://mlsanity.com/paper/2112.00443/) (2021) - [Who Let The Trolls Out? Towards Understanding State-Sponsored Trolls](https://mlsanity.com/paper/1811.03130/) (2018) - [Red Bots Do It Better: Comparative Analysis of Social Bot Partisan Behavior](https://mlsanity.com/paper/1902.02765/) (2019) - [Emotional Dynamics in the Age of Misinformation](https://mlsanity.com/paper/1505.08001/) (2015) - [Pathways through Conspiracy: The Evolution of Conspiracy Radicalization through Engagement in Online Conspiracy Discussions](https://mlsanity.com/paper/2204.10729/) (2022) - [Identifying and Characterizing Behavioral Classes of Radicalization within the QAnon Conspiracy on Twitter](https://mlsanity.com/paper/2209.09339/) (2022) - [Manipulation and abuse on social media](https://mlsanity.com/paper/1503.03752/) (2015) - [A Graph-Based Machine Learning Approach for Bot Detection](https://mlsanity.com/paper/1902.08538/) (2019) - [TrollHunter [Evader]: Automated Detection [Evasion] of Twitter Trolls During the COVID-19 Pandemic](https://mlsanity.com/paper/2012.02586/) (2020) - [How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events](https://mlsanity.com/paper/2209.07614/) (2022) - [One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study](https://mlsanity.com/paper/2209.01675/) (2022) - [Analysing user identity via time-sensitive semantic edit distance (t-SED): A case study of Russian trolls on Twitter](https://mlsanity.com/paper/1901.05228/) (2019) ## Built on by (most cited first, 8 of 8) - [Leveraging Large Language Models to Detect Influence Campaigns in Social Media](https://mlsanity.com/paper/2311.07816/) (2023) - [Contextualizing Internet Memes Across Social Media Platforms](https://mlsanity.com/paper/2311.11157/) (2023) - [IOHunter: Graph Foundation Model to Uncover Online Information Operations](https://mlsanity.com/paper/2412.14663/) (2024) - [Network-informed Prompt Engineering against Organized Astroturf Campaigns under Extreme Class Imbalance](https://mlsanity.com/paper/2501.11849/) (2025) - [Coordinated Reply Attacks in Influence Operations: Characterization and Detection](https://mlsanity.com/paper/2410.19272/) (2024) - [A survey of textual cyber abuse detection using cutting-edge language models and large language models](https://mlsanity.com/paper/2501.05443/) (2025) - [Beyond Content: Behavioral Policies Reveal Actors in Information Operations](https://mlsanity.com/paper/2602.02838/) (2026) - [Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns](https://mlsanity.com/paper/2607.05855/) (2026) ## Abstract (arXiv) The detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge for the research community, which has significant implications beyond the online realm. To address this challenge, we propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity, encompassing both their actions and the feedback they receive from others. Our approach does not incorporate any textual content shared and consists of two steps: First, we leverage an LSTM-based classifier to determine whether account sequences belong to a state-sponsored troll or an organic, legitimate user. Second, we employ the classified sequences to calculate a metric named the "Troll Score", quantifying the degree to which an account exhibits troll-like behavior. To assess the effectiveness of our method, we examine its performance in the context of the 2016 Russian interference campaign during the U.S. Presidential election. Our experiments yield compelling results, demonstrating that our approach can identify account sequences with an AUC close to 99% and accurately differentiate between Russian trolls and organic users with an AUC of 91%. Notably, our behavioral-based approach holds a significant advantage in the ever-evolving landscape, where textual and linguistic properties can be easily mimicked by Large Language Models (LLMs): In contrast to existing language-based techniques, it relies on more challenging-to-replicate behavioral cues, ensuring greater resilience in identifying influence campaigns, especially given the potential increase in the usage of LLMs for generating inauthentic content. Finally, we assessed the generalizability of our solution to various entities driving different information operations and found promising results that will guide future research.