Summary of Uncovering Latent Arguments in Social Media Messaging by Employing Llms-in-the-loop Strategy, By Tunazzina Islam et al.
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
by Tunazzina Islam, Dan Goldwasser
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed LLMs-in-the-Loop strategy leverages Large Language Models to extract latent arguments from social media messaging, addressing the limitations of traditional supervised and unsupervised methods for analyzing public opinion. By applying this framework to contentious topics, researchers can identify arguments associated with specific themes, as demonstrated through case studies using publicly available datasets on climate campaigns and COVID-19 vaccine campaigns. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Automated methods are essential for analyzing public opinion in social media discussions. The proposed approach uses Large Language Models (LLMs) to extract latent arguments from social media messaging, making it possible to identify specific nuances in public discourse. This breakthrough could reduce the need for labor-intensive manual coding techniques and a human-in-the-loop approach. |
Keywords
» Artificial intelligence » Discourse » Supervised » Unsupervised
