Beyond Content: Behavioral Policies Reveal Actors in Information Operations
- URL: http://arxiv.org/abs/2602.02838v1
- Date: Mon, 02 Feb 2026 21:39:21 GMT
- Title: Beyond Content: Behavioral Policies Reveal Actors in Information Operations
- Authors: Philipp J. Schneider, Lanqin Yuan, Marian-Andrei Rizoiu,
- Abstract summary: We introduce a platform-agnostic framework that identifies malicious actors from their behavioral policies by modeling user activity as sequential decision processes.<n>We apply this approach to 12,064 Reddit users, including 99 accounts linked to the Russian Internet Research Agency in Reddit's 2017 transparency report.<n>Activity-based representations, which model how users act rather than what they post, consistently outperform content models in detecting malicious accounts.
- Score: 1.1693100084511296
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features. These approaches are increasingly brittle as generative models produce convincing text, platforms restrict access to behavioral data, and actors migrate to less-regulated spaces. We introduce a platform-agnostic framework that identifies malicious actors from their behavioral policies by modeling user activity as sequential decision processes. We apply this approach to 12,064 Reddit users, including 99 accounts linked to the Russian Internet Research Agency in Reddit's 2017 transparency report, analyzing over 38 million activity steps from 2015-2018. Activity-based representations, which model how users act rather than what they post, consistently outperform content models in detecting malicious accounts. When distinguishing trolls -- users engaged in coordinated manipulation -- from ordinary users, policy-based classifiers achieve a median macro-$F_1$ of 94.9%, compared to 91.2% for text embeddings. Policy features also enable earlier detection from short traces and degrade more gracefully under evasion strategies or data corruption. These findings show that behavioral dynamics encode stable, discriminative signals of manipulation and point to resilient, cross-platform detection strategies in the era of synthetic content and limited data access.
Related papers
- Causal Flow Q-Learning for Robust Offline Reinforcement Learning [53.63254824501714]
We introduce a practical implementation that learns expressive flow-matching policies from confounded demonstrations.<n>Our proposed confounding-robust augmentation procedure achieves a success rate 120% that of confounding-unaware, state-of-the-art offline RL methods.
arXiv Detail & Related papers (2026-02-02T21:50:52Z) - RoGBot: Relationship-Oblivious Graph-based Neural Network with Contextual Knowledge for Bot Detection [3.884231159866055]
We propose a novel framework that integrates detailed textual features with enriched user metadata.<n>Our method uses transformer-based models (e.g., BERT) to extract deep semantic embeddings from tweets.<n> Experimental results on the Cresci-15, Cresci-17, and PAN 2019 datasets demonstrate the robustness of our approach.
arXiv Detail & Related papers (2025-10-25T05:14:58Z) - Searching for Privacy Risks in LLM Agents via Simulation [61.229785851581504]
We present a search-based framework that alternates between improving attack and defense strategies through the simulation of privacy-critical agent interactions.<n>We find that attack strategies escalate from direct requests to sophisticated tactics, such as impersonation and consent forgery.<n>The discovered attacks and defenses transfer across diverse scenarios and backbone models, demonstrating strong practical utility for building privacy-aware agents.
arXiv Detail & Related papers (2025-08-14T17:49:09Z) - DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective [70.77570343385928]
We introduce a novel taxonomy, classifying existing methods based on their reliance on internal features (IF) (inherent to the data) versus external features (EF) (artificially introduced for auditing)<n>We formulate two primary attack types: evasion attacks, designed to conceal the use of a dataset, and forgery attacks, intending to falsely implicate an unused dataset.<n>Building on the understanding of existing methods and attack objectives, we further propose systematic attack strategies: decoupling, removal, and detection for evasion; adversarial example-based methods for forgery.<n>Our benchmark, DATABench, comprises 17 evasion attacks, 5 forgery attacks, and 9
arXiv Detail & Related papers (2025-07-08T03:07:15Z) - On the Use of Proxies in Political Ad Targeting [49.61009579554272]
We show that major political advertisers circumvented mitigations by targeting proxy attributes.
Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
arXiv Detail & Related papers (2024-10-18T17:15:13Z) - Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
We test for user strategization by conducting a lab experiment and survey.
We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes"
Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
arXiv Detail & Related papers (2024-05-09T07:36:08Z) - Beyond Trial-and-Error: Predicting User Abandonment After a Moderation Intervention [0.6918368994425961]
Current content moderation follows a reactive, trial-and-error approach.<n>We introduce a proactive, predictive approach that enables moderators to anticipate the impact of their actions before implementation.<n>We study the reactions of 16,540 users to a massive ban of online communities on Reddit.
arXiv Detail & Related papers (2024-04-23T08:52:41Z) - SeGA: Preference-Aware Self-Contrastive Learning with Prompts for
Anomalous User Detection on Twitter [14.483830120541894]
We propose SeGA, preference-aware self-contrastive learning for anomalous user detection.
SeGA uses large language models to summarize user preferences via posts.
We empirically validate the effectiveness of the model design and pre-training strategies.
arXiv Detail & Related papers (2023-12-17T05:35:28Z) - A Deep Behavior Path Matching Network for Click-Through Rate Prediction [9.800832176496002]
We propose to match the user's current behavior path with historical behavior paths to predict user behaviors on the app.
We design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths.
Our model shows excellent performance on two real-world datasets, outperforming the state-of-the-art CTR model.
arXiv Detail & Related papers (2023-02-01T08:08:21Z) - Countering Malicious Content Moderation Evasion in Online Social
Networks: Simulation and Detection of Word Camouflage [64.78260098263489]
Twisting and camouflaging keywords are among the most used techniques to evade platform content moderation systems.
This article contributes significantly to countering malicious information by developing multilingual tools to simulate and detect new methods of evasion of content.
arXiv Detail & Related papers (2022-12-27T16:08:49Z) - Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI
Approach to Detecting State-Sponsored Trolls [8.202465737306222]
Detection of state-sponsored trolls operating in influence campaigns on social media is a critical and unsolved challenge.
We propose a new AI-based solution that identifies troll accounts solely through behavioral cues associated with their sequences of sharing activity.
arXiv Detail & Related papers (2022-10-17T07:01:17Z) - Relational Graph Neural Networks for Fraud Detection in a Super-App
environment [53.561797148529664]
We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App.
We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users.
Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
arXiv Detail & Related papers (2021-07-29T00:02:06Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.