arXiv AI By Manar Alsaid

Assessing the Performance of AI Models in Misinformation Detection Using Predefined Social Noise Constructs

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arXiv Machine Learning
Sep 25

Agentic Detection of Online Conspiracies

The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.

By Lior Biton, Oren Tsur
arXiv Machine Learning
Jun 10

AI Application Gives Users Real-Time Feedback on the Level of Peace in the Social Media Videos They Watch

arXiv:2601. 05232v3 Announce Type: replace-cross Abstract: Most people now get their news from videos on social media, such as YouTube and Facebook, rather than through curated journalism.

By P. Gilda (Columbia University), P. Dungarwal (Columbia University), A. Thongkham (Columbia University), E. T. Ajayi (St John's University), S. Choudhary (Columbia University), T. M. Terol (Columbia University), C. Lam (Columbia University), J. P. Araujo (Columbia University), M. McFadyen-Mungalln (Columbia University), L. S. Liebovitch (Columbia University), P. T. Coleman (Columbia University), H. West (Columbia University), K. Sieck (Toyota Research Institute), S. Carter (Toyota Research Institute)
arXiv Computation and Language
Sep 18

FakeSpotter: A content and strategy agnostic Viral Misinformation Detection Tool

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.

By Giovanni Spitale, Federico Germani
arXiv AI
Aug 11

Build it, Break it, Repeat: Benchmarking and improving LLM-manipulated disinformation detection in social media posts

arXiv:2608. 09510v1 Announce Type: cross Abstract: Detecting machine-generated disinformation on social media is increasingly difficult as large language models (LLMs) make it easier to generate and rewrite misleading content at scale.

By Kevin Thomas, Milosz Kasprzyk, Reuel C Igbokwe Onuigbo, Elliott Pert, Cameron Tovey, Jo\~ao A. Leite, Olesya Razuvayevskaya, Carolina Scarton