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

• URL: http://arxiv.org/abs/2412.18148v1
• Date: Tue, 24 Dec 2024 04:04:54 GMT
• Title: Are We in the AI-Generated Text World Already? Quantifying and Monitoring AIGT on Social Media
• Authors: Zhen Sun, Zongmin Zhang, Xinyue Shen, Ziyi Zhang, Yule Liu, Michael Backes, Yang Zhang, Xinlei He,
• Abstract summary: Social media platforms are experiencing a growing presence of AI-Generated Texts (AIGTs) Despite its importance, a systematic study to assess the prevalence of AIGTs on social media is still lacking. This paper aims to quantify, monitor, and analyze the AIGTs on online social media platforms.
• Score: 38.99664377299462
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• Abstract: 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, a systematic study to assess the prevalence of AIGTs on social media is still lacking. To address this gap, this paper aims to quantify, monitor, and analyze 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 over time and observe different trends of AI Attribution Rate (AAR) across social media platforms from January 2022 to October 2024. 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 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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