FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

arXiv cs.CL Papers
misinformation-detection viral-risk content-analysis llm-assessment explainable-ai text-classification

Summary

FakeSpotter is a content- and strategy-agnostic tool designed to estimate the viral misinformation risk of textual content by measuring structural fingerprints, using repeated LLM assessments and logistic regression classifiers, with reported macro F1 scores of 0.788 for short texts and 0.793 for long texts on a labeled corpus.

arXiv:2609.19152v1 Announce Type: new Abstract: Misinformation detection tools often rely on binary true and false classifications or models trained on historical examples, limiting their usefulness when novel misleading narratives emerge. Here, we present FakeSpotter, a content- and strategy-agnostic tool designed to estimate the viral misinformation risk of textual content by measuring structural fingerprints of misinformation rather than directly adjudicating truthfulness. FakeSpotter operationalizes a theory-driven framework across linguistic, narrative, logical, and critical-thinking dimensions, using repeated LLM assessments and domain-specific logistic regression classifiers for short and long texts. In a labelled corpus of 764 texts from social media and FakeNewsNet, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts on a held-out test set. FakeSpotter's interpretive layer provides explainable outputs through feature-based scores, signal agreement, and a caution index, and can be used for social listening. These findings suggest that identifying the structural fingerprints of misinformation can support early, explainable, and human-supervised assessment of potentially viral misinformation.
Original Article
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# FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool
Source: [https://arxiv.org/abs/2609.19152](https://arxiv.org/abs/2609.19152)
[View PDF](https://arxiv.org/pdf/2609.19152)

> Abstract:Misinformation detection tools often rely on binary true and false classifications or models trained on historical examples, limiting their usefulness when novel misleading narratives emerge\. Here, we present FakeSpotter, a content\- and strategy\-agnostic tool designed to estimate the viral misinformation risk of textual content by measuring structural fingerprints of misinformation rather than directly adjudicating truthfulness\. FakeSpotter operationalizes a theory\-driven framework across linguistic, narrative, logical, and critical\-thinking dimensions, using repeated LLM assessments and domain\-specific logistic regression classifiers for short and long texts\. In a labelled corpus of 764 texts from social media and FakeNewsNet, FakeSpotter achieved macro F1 scores of 0\.788 for short texts and 0\.793 for long texts on a held\-out test set\. FakeSpotter's interpretive layer provides explainable outputs through feature\-based scores, signal agreement, and a caution index, and can be used for social listening\. These findings suggest that identifying the structural fingerprints of misinformation can support early, explainable, and human\-supervised assessment of potentially viral misinformation\.

## Submission history

From: Federico Germani \[[view email](https://arxiv.org/show-email/d66a32bb/2609.19152)\] **\[v1\]**Mon, 20 Jul 2026 14:57:41 UTC \(2,537 KB\)

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