arXiv Computation and Language September 18, 2026 By Giovanni Spitale, Federico Germani

FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

Read the original on arXiv Computation and Language →

FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.

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