Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media

Grimme, Britta; Pohl, Janina; Winkelmann, Hendrik; Stampe, Lucas; Grimme, Christian

doi:10.1007/978-3-031-47896-3_6

Cited by 3 publications

(1 citation statement)

References 20 publications

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“…Schuchard et al (2019) show that social bots are disproportionately influential across social media conversations of interest across multiple centrality measures. The rise of AI and LLMs led to generated campaigns in social media, as can be seen in the work by Grimme et al (2023). Törnberg (2023) simulated social media environments through a combination of LLMs and Agent-Based Modeling to promote more constructive conversations.…”

Section: Related Workmentioning

confidence: 99%

LLMs Among Us: Generative AI Participating in Digital Discourse

Radivojevic,

Clark,

Brenner

2024

AAAI-SS

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The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of many social media platforms. While this can bring promising opportunities, it also raises many threats, such as biases and privacy concerns, and may contribute to the spread of propaganda by malicious actors. We developed the "LLMs Among Us" experimental framework on top of the Mastodon social media platform for bot and human participants to communicate without knowing the ratio or nature of bot and human participants. We built 10 personas with three different LLMs, GPT-4, Llama 2 Chat, and Claude. We conducted three rounds of the experiment and surveyed participants after each round to measure the ability of LLMs to pose as human participants without human detection. We found that participants correctly identified the nature of other users in the experiment only 42% of the time despite knowing the presence of both bots and humans. We also found that the choice of persona had substantially more impact on human perception than the choice of mainstream LLMs.

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Section: Related Workmentioning

confidence: 99%

LLMs Among Us: Generative AI Participating in Digital Discourse

Radivojevic,

Clark,

Brenner

2024

AAAI-SS

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Miko: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery

Lu,

Wang,

Luo

et al. 2024

Proceedings of the 32nd ACM International Conference on Multimedia

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Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news

Jung,

Lee,

Woo

et al. 2024

PLoS ONE

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This study presents a comprehensive exploration of topic modeling methods tailored for large language model (LLM) using data obtained from Web of Science and LexisNexis from June 1, 2020, to December 31, 2023. The data collection process involved queries focusing on LLMs, including “Large language model,” “LLM,” and “ChatGPT.” Various topic modeling approaches were evaluated based on performance metrics, including diversity and coherence. latent Dirichlet allocation (LDA), nonnegative matrix factorization (NMF), combined topic models (CTM), and bidirectional encoder representations from Transformers topic (BERTopic) were employed for performance evaluation. Evaluation metrics were computed across platforms, with BERTopic demonstrating superior performance in diversity and coherence across both LexisNexis and Web of Science. The experiment result reveals that news articles maintain a balanced coverage across various topics and mainly focus on efforts to utilize LLM in specialized domains. Conversely, research papers are more concise and concentrated on the technology itself, emphasizing technical aspects. Through the insights gained in this study, it becomes possible to investigate the future path and the challenges that LLMs should tackle. Additionally, they could offer considerable value to enterprises that utilize LLMs to deliver services.

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Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media

Cited by 3 publications

References 20 publications

LLMs Among Us: Generative AI Participating in Digital Discourse

LLMs Among Us: Generative AI Participating in Digital Discourse

Miko: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery

Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news

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