"Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications

• URL: http://arxiv.org/abs/2503.03927v1
• Date: Wed, 05 Mar 2025 21:51:52 GMT
• Title: "Impressively Scary:" Exploring User Perceptions and Reactions to Unraveling Machine Learning Models in Social Media Applications
• Authors: Jack West, Bengisu Cagiltay, Shirley Zhang, Jingjie Li, Kassem Fawaz, Suman Banerjee,
• Abstract summary: We aim to investigate how social media user perceptions and behaviors change once exposed to machine learning models.<n>We conducted user studies (N=21) and found that participants were unaware to both what the models output and when the models were used in Instagram and TikTok.<n>In response to being exposed to the models' functionality, we observed long term behavior changes in 8 participants.
• Score: 17.961040981236092
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning models deployed locally on social media applications are used for features, such as face filters which read faces in-real time, and they expose sensitive attributes to the apps. However, the deployment of machine learning models, e.g., when, where, and how they are used, in social media applications is opaque to users. We aim to address this inconsistency and investigate how social media user perceptions and behaviors change once exposed to these models. We conducted user studies (N=21) and found that participants were unaware to both what the models output and when the models were used in Instagram and TikTok, two major social media platforms. In response to being exposed to the models' functionality, we observed long term behavior changes in 8 participants. Our analysis uncovers the challenges and opportunities in providing transparency for machine learning models that interact with local user data.

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