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Social media companies wield power over their users through design, policy, and through their participation in public discourse. We set out to understand how companies leverage public relations to influence expectations of privacy and privacy-related norms. To interrogate the discourse productions of companies in relation to privacy, we examine the blogs associated with three major social media platforms: Facebook, Instagram (both owned by Facebook Inc.), and Snapchat. We analyze privacy-related posts using critical discourse analysis to demonstrate how these powerful entities construct narratives about users and their privacy expectations. We find that each of these platforms often make use of discourse about "vulnerable" identities to invoke relations of power, while at the same time, advancing interpretations and values that favor data capitalism. Finally, we discuss how these public narratives might influence the construction of users' own interpretations of appropriate privacy norms and conceptions of self. We contend that expectations of privacy and social norms are not simply artifacts of users' own needs and desires, but co-constructions that reflect the influence of social media companies themselves.
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This content will become publicly available on May 1, 2026
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy
The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised methods for extracting themes from public discourse, such as topic modeling, often reveal overarching patterns that might not capture specific nuances. Consequently, a significant portion of research into social media discourse still depends on labor-intensive manual coding techniques and a human-in-the-loop approach, which are both time-consuming and costly. In this work, we study the problem of discovering arguments associated with a specific theme. We propose a generic **LLMs-in-the-Loop** strategy that leverages the advanced capabilities of Large Language Models (LLMs) to extract latent arguments from social media messaging. To demonstrate our approach, we apply our framework to contentious topics. We use two publicly available datasets: (1) the climate campaigns dataset of 14k Facebook ads with 25 themes and (2) the COVID-19 vaccine campaigns dataset of 9k Facebook ads with 14 themes. Additionally, we design a downstream task as stance prediction by leveraging talking points in climate debates. Furthermore, we analyze demographic targeting and the adaptation of messaging based on real-world events.
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- Award ID(s):
- 2048001
- PAR ID:
- 10590746
- Publisher / Repository:
- Association for Computational Linguistics
- Date Published:
- ISBN:
- 979-8-89176-195-7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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