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

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Sishun Liu, Sajal Halder, Ke Deng, Yan Wang, Xiuzhen Zhang

Machine Learning · Submitted 7 Jul 2026 · 43 references

Pith's verdict: TENSOR fuses TPP behavioral anomaly scores with LLM-derived evidence via EPHAD for unsupervised IO detection; the fusion works but the contamination-mitigation framing is not supported by the data.

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How this review was made: An AI referee (glm-5.2) read the paper on July 8, 2026 and answered a fixed list of questions about it. T0 is the starting level every paper gets. The levels describe how deeply Pith has checked the paper, never the authors.

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arXiv ID2607.05855 (version 1)
Pith IDWTYNF63W, Pith's permanent record of this paper and its review
CitationKe Deng, Sajal Halder, Sishun Liu, Xiuzhen Zhang, Yan Wang. "Unsupervised Anomaly Detection of Information Operations Users via Behavioral and Language Patterns." arXiv:2607.05855 (2026). https://pith.science/paper/WTYNF63W
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The paper

The paper claims

Fusing posting-time patterns with LLM judgments detects coordinated influence operations

The reading

The paper proposes TENSOR, a method for detecting Information Operations (IO) users on social media without requiring labeled training data. The core idea is that IO users are rare and exhibit distinct temporal behavioral patterns (e.g., coordinated posting times) that differ from normal users. TENSOR trains a Temporal Point Process (TPP) on user posting timestamps to flag anomalous behavioral patterns, but this TPP is trained on contaminated data containing both normal and IO users, which degrades its accuracy. To correct for this, the paper introduces an evidence function: a Large Language Model (LLM) is prompted with each user's post timeline and its text output is converted into a quantitative score via semantic similarity mapping. This LLM-derived score is then fused with the TPP score through exponential tilting (EPHAD), a post-hoc adjustment mechanism that corrects the TPP's predictions at test time. The paper demonstrates that neither the TPP alone nor the LLM alone performs well, but their fusion yields a system that outperforms existing unsupervised baselines across five real-world IO datasets.

What carries the argument

Temporal Point Process (TPP), exponential tilting (EPHAD), evidence function from LLM semantic similarity

Extended reading notes

Core claim

The central mechanism is the fusion of a temporal-behavioral anomaly score from a TPP with a language-aware evidence score from an LLM via exponential tilting. The key finding is that an LLM that performs poorly at zero-shot IO detection on its own still provides useful corrective signal when used as an evidence function to adjust a TPP that was trained on contaminated data. The LLM's judgments, even if noisy, carry information about IO characteristics that is complementary to what the TPP captures from temporal patterns alone, and the exponential tilting formula provides a principled way to combine them.

Load-bearing premise

The LLM-based evidence function provides signal that is genuinely complementary to the TPP score rather than correlated with the TPP's errors. The paper shows the LLM alone performs poorly at zero-shot IO detection, yet assumes its output is sufficient to correct the TPP through exponential tilting. If the LLM's judgments are systematically wrong in the same way the TPP is wrong, the adjustment could reinforce errors rather than correct them.

Editorial extensions

If this is right

  • Platforms could deploy TENSOR-style detectors that adapt to new IO campaigns without needing labeled examples of those specific campaigns, addressing the generalization bottleneck of supervised methods.
  • The evidence-function approach could generalize beyond IO detection to any anomaly detection task where a contaminated training set is unavoidable and a separate, imperfect signal source is available for post-hoc correction.
  • The finding that a weak LLM signal can correct a contaminated statistical model suggests a broader design pattern: using LLMs as evidence providers rather than primary classifiers in domains where they lack sufficient zero-shot accuracy.
  • Unsupervised optimization of the temperature parameter beta, which the paper identifies as promising but leaves open, could make the method fully label-free for deployment.
Original 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.

Figures
Figure 1. Architecture of Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR). 4.3 Temporal-bEhavior-laNguage Signals for information Operation Recognition In this section, we propose Temporal-bEhavior-laNguage Signals for information Operation Recognition (TENSOR), which is sketched in
Figure 2. Prompts for zero-shot IO user detection in
Figure 3. Impact of data imbalance on Clustering’s performance. Data points represent control users (C0) and IO users (C1). Results indicate that significant data imbalance shifts cluster centroids with some random seeds, leading to performance inconsistency. users. This instability results in inconsistent performance, particularly on highly imbalanced datasets such as China_1 and Russia_1. 5.2 The impact of behavioral and language patterns on IO user detection If we only use the behavioral or language patterns of one user for IO user detection, we may get suboptimal results. To demonstrate…
Figure 4. AUPRC of TENSOR on all five IO datasets with different temperatures from 0.05 to 2

Figures reproduced from arXiv:2607.05855 by the authors.

References

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 3 sentences without a confident label

Uses the method or tool of 1 paper
  • In this paper, we use an open-weight reasoning model gpt (reasoning model, 117B parameters with 5.1B active parameters) [ref] with a medium reasoning effort.

    gpt-oss-120b & gpt-oss-20b Model Card · model · 0.89

Cites as background 2 papers

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