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

arXiv: https://arxiv.org/abs/2412.18148 · HTML: https://mlsanity.com/paper/2412.18148/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 64 references that are in the map.
- First posted on arXiv 24 Dec 2024.

## Builds on (most cited first, 30 of 34)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Mixtral of Experts](https://mlsanity.com/paper/2401.04088/) (2024)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Release Strategies and the Social Impacts of Language Models](https://mlsanity.com/paper/1908.09203/) (2019)
- [GLTR: Statistical Detection and Visualization of Generated Text](https://mlsanity.com/paper/1906.04043/) (2019)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Automatic Detection of Generated Text is Easiest when Humans are Fooled](https://mlsanity.com/paper/1911.00650/) (2019)
- [DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text](https://mlsanity.com/paper/2306.05540/) (2023)
- [MAGE: Machine-generated Text Detection in the Wild](https://mlsanity.com/paper/2305.13242/) (2023)
- [TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation](https://mlsanity.com/paper/2109.13296/) (2021)
- [Evaluating Open-Domain Question Answering in the Era of Large Language Models](https://mlsanity.com/paper/2305.06984/) (2023)
- [MGTBench: Benchmarking Machine-Generated Text Detection](https://mlsanity.com/paper/2303.14822/) (2023)
- [Document-Level Machine Translation with Large Language Models](https://mlsanity.com/paper/2304.02210/) (2023)
- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Few-Shot Detection of Machine-Generated Text using Style Representations](https://mlsanity.com/paper/2401.06712/) (2024)
- [Machine-Made Media: Monitoring the Mobilization of Machine-Generated Articles on Misinformation and Mainstream News Websites](https://mlsanity.com/paper/2305.09820/) (2023)
- [ConDA: Contrastive Domain Adaptation for AI-generated Text Detection](https://mlsanity.com/paper/2309.03992/) (2023)
- [A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions](https://mlsanity.com/paper/2305.18339/) (2023)
- [HowkGPT: Investigating the Detection of ChatGPT-generated University Student Homework through Context-Aware Perplexity Analysis](https://mlsanity.com/paper/2305.18226/) (2023)
- [On the Detectability of ChatGPT Content: Benchmarking, Methodology, and Evaluation through the Lens of Academic Writing](https://mlsanity.com/paper/2306.05524/) (2023)
- [Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods](https://mlsanity.com/paper/2406.15583/) (2024)

## Built on by (most cited first, 30 of 35)

- [RepreGuard: Detecting LLM-Generated Text by Revealing Hidden Representation Patterns](https://mlsanity.com/paper/2508.13152/) (2025)
- [AI use in American newspapers is widespread, uneven, and rarely disclosed](https://mlsanity.com/paper/2510.18774/) (2025)
- [Characterizing LLM-driven Social Network: The Chirper.ai Case](https://mlsanity.com/paper/2504.10286/) (2025)
- [TH-Bench: Evaluating Evading Attacks via Humanizing AI Text on Machine-Generated Text Detectors](https://mlsanity.com/paper/2503.08708/) (2025)
- [What happens when generative AI models train recursively on each others' outputs?](https://mlsanity.com/paper/2505.21677/) (2025)
- [The Impact of AI-Generated Text on the Internet](https://mlsanity.com/paper/2604.26965/) (2026)
- [Can VLMs Detect and Localize Fine-Grained AI-Edited Images?](https://mlsanity.com/paper/2505.15644/) (2025)
- [When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Text](https://mlsanity.com/paper/2506.09975/) (2025)
- [Machines in the Crowd? Measuring the Footprint of Machine-Generated Text on Reddit](https://mlsanity.com/paper/2510.07226/) (2025)
- [Learning in an Echo Chamber: Online Learning with Replay Adversary](https://mlsanity.com/paper/2509.25135/) (2025)
- [On the Detectability of LLM-Generated Text: What Exactly Is LLM-Generated Text?](https://mlsanity.com/paper/2510.20810/) (2025)
- [A Multi-Agent Perspective on Modern Information Retrieval](https://mlsanity.com/paper/2502.14796/) (2025)
- [Echoes of Automation: The Increasing Use of LLMs in Newsmaking](https://mlsanity.com/paper/2508.06445/) (2025)
- [AI-Associated Lexical Shifts Across 34 Languages: Cross-Lingual Convergence and Diachronic Uptake in News Writing](https://mlsanity.com/paper/2605.25358/) (2026)
- [Mitigating GenAI-Powered Evidence Pollution for Out-Of-Context Misinformation Detection](https://mlsanity.com/paper/2501.14728/) (2025)
- [Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias](https://mlsanity.com/paper/2502.10577/) (2025)
- [Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation](https://mlsanity.com/paper/2503.23242/) (2025)
- [How Persuasive Could LLMs Be? A First Study Combining Linguistic-Rhetorical Analysis and User Experiments](https://mlsanity.com/paper/2508.09614/) (2025)
- [Artificial Phantasia: Emergent Mental Imagery in Large Language Models](https://mlsanity.com/paper/2509.23108/) (2025)
- [How AI Writing Assistance Distorts Perceptions of AI Users and Their Opinions](https://mlsanity.com/paper/2604.22503/) (2026)
- [DeGenTWeb: A First Look at LLM-dominant Websites](https://mlsanity.com/paper/2605.00087/) (2026)
- [The Future of Facts: Tracing the Factual Generation-Verification Gap](https://mlsanity.com/paper/2605.27564/) (2026)
- [Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs](https://mlsanity.com/paper/2605.29928/) (2026)
- [When New Generators Arrive: Lifelong Machine-Generated Text Attribution via Ridge Feature Transfer](https://mlsanity.com/paper/2606.05626/) (2026)
- [Belief Coevolution in a Social Network of Generalist and Specialist Large Language Models](https://mlsanity.com/paper/2607.27512/) (2026)
- [Deconstructing Jazz Piano Style Using Machine Learning](https://mlsanity.com/paper/2504.05009/) (2025)
- [RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media](https://mlsanity.com/paper/2509.22055/) (2025)
- ["Jutters"](https://mlsanity.com/paper/2601.11532/) (2025)
- [Adversarial Creation and Detection of AI-Generated Social Bot Content](https://mlsanity.com/paper/2606.07219/) (2026)
- [Show Me How You Reason and I'll Tell You Who You Are: Reasoning Graphs for Robust LLM Authorship Attribution](https://mlsanity.com/paper/2607.14905/) (2026)

## Abstract (arXiv)

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.