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Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media

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arxiv 2412.18148 v3 pith:Y4UYN3UD submitted 2024-12-24 cs.AI cs.CLcs.CRcs.SI

classification cs.AIcs.CLcs.CRcs.SI
keywords mediasocialaigtsplatformsaigttextsacrossai-generated
verification ladder T0 review T1 audit T2 compute T3 formal
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Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs). However, the misuse of AIGTs could have profound implications for public opinion, such as spreading misinformation and manipulating narratives. Despite its importance, it remains unclear how prevalent AIGTs are on social media. To address this gap, this paper aims to quantify and monitor the AIGTs on online social media platforms. We first collect a dataset (SM-D) with around 2.4M posts from 3 major social media platforms: Medium, Quora, and Reddit. Then, we construct a diverse dataset (AIGTBench) to train and evaluate AIGT detectors. AIGTBench combines popular open-source datasets and our AIGT datasets generated from social media texts by 12 LLMs, serving as a benchmark for evaluating mainstream detectors. With this setup, we identify the best-performing detector (OSM-Det). We then apply OSM-Det to SM-D to track AIGTs across social media platforms from January 2022 to October 2024, using the AI Attribution Rate (AAR) as the metric. Specifically, Medium and Quora exhibit marked increases in AAR, rising from 1.77% to 37.03% and 2.06% to 38.95%, respectively. In contrast, Reddit shows slower growth, with AAR increasing from 1.31% to 2.45% over the same period. Our further analysis indicates that AIGTs on social media differ from human-written texts across several dimensions, including linguistic patterns, topic distributions, engagement levels, and the follower distribution of authors. We envision our analysis and findings on AIGTs in social media can shed light on future research in this domain.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text

    cs.CL 2025-06 conditional novelty 6.0 of 10

    Fine-tuned LLMs generate social media text that evades state-of-the-art detectors and human readers, dropping detection accuracy from up to 99.9% to near chance.

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