Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns

• URL: http://arxiv.org/abs/2607.05855v1
• Date: Tue, 07 Jul 2026 05:23:42 GMT
• Title: Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns
• Abstract summary: Information Operations on social media networks have been identified as a significant threat to democracy and modern society.<n>Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO user behavior.<n>We propose a novel unsupervised IO user detection approach called Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR)
• Score: 8.29955351018802
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Information Operations on social media networks have been identified as a significant threat to democracy and modern society, but they are challenging and expensive to detect by humans. Existing supervised IO detection methods fail to capture the dynamic nature of evolving IO user behavior, while existing unsupervised approaches rely on oversimplified assumptions of coordination among IO users that may not exist in practice. To overcome the limitations of existing methods, we formulate IO user detection as an anomaly detection problem and propose a novel unsupervised IO user detection approach called Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR), which leverages multimodal data, including temporal online user behavior, such as message posting activities, and the textual content of the messages. The motivation is that IO users are typically a very small fraction of all online users and have unique temporal behavioral and language patterns. Specifically, we train a Temporal Point Process (TPP) to capture abnormal temporal behavioral patterns of IO users because they are known to behave in a coordinated manner for IO campaigns. We further introduce a novel evidence function that converts LLM responses, which are generated from user post timelines, into quantitative scores to adjust the TPP outputs for better IO user detection. Experimental results show that TENSOR outperforms the baselines on five real-world IO datasets. Code is available at https://github.com/xiuzhenzhang/TENSOR.

Related papers

• QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
  We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
  arXiv  Detail & Related papers  (2026-04-17T08:53:24Z)
• Stream RAG: Instant and Accurate Spoken Dialogue Systems with Streaming Tool Usage [66.67531241554546]
  End-to-end speech-in speech-out dialogue systems are emerging as a powerful alternative to traditional ASR-LLM-TTS pipelines.<n>We introduce the first approach to extend tool use directly into speech-in speech-out systems.<n>We propose Streaming Retrieval-Augmented Generation (Streaming RAG), a novel framework that reduces user-perceived latency by predicting tool queries in parallel with user speech.
  arXiv  Detail & Related papers  (2025-10-02T14:18:20Z)
• On Multi-entity, Multivariate Quickest Change Point Detection [2.0369245689839817]
  Change Point Detection (CPD) is motivated by applications in crowd monitoring where traditional sensing methods may be infeasible.<n>We introduce the concept of Individual Deviation from Normality (IDfN), computed via a reconstruction-error-based autoencoder trained on normal behavior.<n>We aggregate these individual deviations using mean, variance, and Kernel Density Estimates (KDE) to yield a System-Wide Anomaly Score (SWAS)<n>Our unsupervised approach eliminates the need for labeled data or feature extraction, enabling real-time operation on streaming input.
  arXiv  Detail & Related papers  (2025-09-22T18:35:24Z)
• AViLA: Asynchronous Vision-Language Agent for Streaming Multimodal Data Interaction [27.982280356250605]
  We present AViLA, a vision-language agent for streaming data interaction that can handle ad-hoc queries and give time-aware responses.<n>Our experiments show that existing models often fail to respond at appropriate times, while AViLA significantly improves both accuracy and temporal awareness.
  arXiv  Detail & Related papers  (2025-06-23T10:11:30Z)
• Collaborative Instance Object Navigation: Leveraging Uncertainty-Awareness to Minimize Human-Agent Dialogues [54.81155589931697]
  Collaborative Instance object Navigation (CoIN) is a new task setting where the agent actively resolve uncertainties about the target instance.<n>We propose a novel training-free method, Agent-user Interaction with UncerTainty Awareness (AIUTA)<n>First, upon object detection, a Self-Questioner model initiates a self-dialogue within the agent to obtain a complete and accurate observation description.<n>An Interaction Trigger module determines whether to ask a question to the human, continue or halt navigation.
  arXiv  Detail & Related papers  (2024-12-02T08:16:38Z)
• PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System [6.068607290592521]
  We propose adaptive trace fetching, lightweight, real-time malicious behavior detection system. Specifically, we monitor malicious behavior with Event Tracing for Windows (ETW) and learn to selectively collect maliciousness-related APIs or call stacks. As a result, we can monitor a wider range of APIs and detect more intricate attack behavior.
  arXiv  Detail & Related papers  (2024-11-02T14:52:04Z)
• I2EDL: Interactive Instruction Error Detection and Localization [65.25839671641218]
  We propose a novel task of Interactive VLN in Continuous Environments (IVLN-CE) It allows the agent to interact with the user during the VLN-CE navigation to verify any doubts regarding the instruction errors. We leverage a pre-trained module to detect instruction errors and pinpoint them in the instruction by cross-referencing the textual input and past observations.
  arXiv  Detail & Related papers  (2024-06-07T16:52:57Z)
• Get my drift? Catching LLM Task Drift with Activation Deltas [55.75645403965326]
  Task drift allows attackers to exfiltrate data or influence the LLM's output for other users.<n>We show that a simple linear classifier can detect drift with near-perfect ROC AUC on an out-of-distribution test set.<n>We observe that this approach generalizes surprisingly well to unseen task domains, such as prompt injections, jailbreaks, and malicious instructions.
  arXiv  Detail & Related papers  (2024-06-02T16:53:21Z)
• 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)
• On the Universal Adversarial Perturbations for Efficient Data-free Adversarial Detection [55.73320979733527]
  We propose a data-agnostic adversarial detection framework, which induces different responses between normal and adversarial samples to UAPs. Experimental results show that our method achieves competitive detection performance on various text classification tasks.
  arXiv  Detail & Related papers  (2023-06-27T02:54:07Z)
• Sampling Is All You Need on Modeling Long-Term User Behaviors for CTR Prediction [15.97120392599086]
  We propose textbfM (textbfSampling-based textbfDeep textbfModeling), a simple yet effective sampling-based end-to-end approach for modeling long-term user behaviors. We show theoretically and experimentally that the proposed method performs on par with standard attention-based models on modeling long-term user behaviors.
  arXiv  Detail & Related papers  (2022-05-20T15:20:52Z)
• Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation [61.114580368455236]
  User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. We propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors.
  arXiv  Detail & Related papers  (2021-09-07T04:28:09Z)
• Multi-Interactive Attention Network for Fine-grained Feature Learning in CTR Prediction [48.267995749975476]
  In the Click-Through Rate (CTR) prediction scenario, user's sequential behaviors are well utilized to capture the user interest. Existing methods mostly utilize attention on the behavior of users, which is not always suitable for CTR prediction. We propose a Multi-Interactive Attention Network (MIAN) to comprehensively extract the latent relationship among all kinds of fine-grained features.
  arXiv  Detail & Related papers  (2020-12-13T05:46:19Z)

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.