Leveraging Large Language Models to Detect Influence Campaigns on Social Media

Luceri, Luca; Boniardi, Eric; Ferrara, Emilio

doi:10.1145/3589335.3651912

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2024

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Cited by 2 publications

References 19 publications

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Influence Role Recognition and LLM-Based Scholar Recommendation in Academic Social Networks

Cheng,

Edara,

Zhang

et al. 2024

2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)

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Influence Role Recognition and LLM-Based Scholar Recommendation in Academic Social Networks

Cheng,

Edara,

Zhang

et al. 2024

2024 IEEE 11th International Conference on Data Science and Advanced Analytics (DSAA)

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Multitask Learning for Crash Analysis: A Fine-Tuned LLM Framework Using Twitter Data

Jaradat,

Nayak,

Paz

et al. 2024

Smart Cities

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Road traffic crashes (RTCs) are a global public health issue, with traditional analysis methods often hindered by delays and incomplete data. Leveraging social media for real-time traffic safety analysis offers a promising alternative, yet effective frameworks for this integration are scarce. This study introduces a novel multitask learning (MTL) framework utilizing large language models (LLMs) to analyze RTC-related tweets from Australia. We collected 26,226 traffic-related tweets from May 2022 to May 2023. Using GPT-3.5, we extracted fifteen distinct features categorized into six classification tasks and nine information retrieval tasks. These features were then used to fine-tune GPT-2 for language modeling, which outperformed baseline models, including GPT-4o mini in zero-shot mode and XGBoost, across most tasks. Unlike traditional single-task classifiers that may miss critical details, our MTL approach simultaneously classifies RTC-related tweets and extracts detailed information in natural language. Our fine-tunedGPT-2 model achieved an average accuracy of 85% across the six classification tasks, surpassing the baseline GPT-4o mini model’s 64% and XGBoost’s 83.5%. In information retrieval tasks, our fine-tuned GPT-2 model achieved a BLEU-4 score of 0.22, a ROUGE-I score of 0.78, and a WER of 0.30, significantly outperforming the baseline GPT-4 mini model’s BLEU-4 score of 0.0674, ROUGE-I score of 0.2992, and WER of 2.0715. These results demonstrate the efficacy of our fine-tuned GPT-2 model in enhancing both classification and information retrieval, offering valuable insights for data-driven decision-making to improve road safety. This study is the first to explicitly apply social media data and LLMs within an MTL framework to enhance traffic safety.

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Leveraging Large Language Models to Detect Influence Campaigns on Social Media

Cited by 2 publications

References 19 publications

Influence Role Recognition and LLM-Based Scholar Recommendation in Academic Social Networks

Influence Role Recognition and LLM-Based Scholar Recommendation in Academic Social Networks

Multitask Learning for Crash Analysis: A Fine-Tuned LLM Framework Using Twitter Data

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