November 30, 2025
Classifying TikToks Locally: Political Content Detection with Phi-4 on Android
Can AI directly analyze political content displayed on phones without sending sensitive screen data to the cloud? This study finds that LLMs running locally can do so privately, but their current speed limitations point to hybrid methods as the most practical path for mobile media research.
Abstract
Large Language Models (LLMs) like ChatGPT-4o and Phi-4 have demonstrated great potential in identifying complex semantic content, such as political discourse, especially in cloud or desktop-based settings. However, their application on mobile devices, where privacy concerns are high, remains largely unexplored. Mobile phones have become the main way people access political discussions and news. This study investigates whether local execution of LLMs can serve as a viable, privacy-preserving way for analyzing political content seen on mobile screens. Using a Google Pixel 9, we benchmarked Phi-4 with 2,000 OCR-extracted text samples from TikTok screen recordings, comparing classification latency and feasibility. While results show that local classification is possible, latency is high, averaging over 14 seconds per sample. Although dictionary-based methods are faster, they lack the semantic flexibility of LLMs. Our findings suggest a hybrid approach and targeted frame selection strategies could enable scalable, privacy-friendly mobile media analysis in the near future.
To continue reading please visit: https://doi.org/10.1145/3771882.3773943
This open access article was published in the Proceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia on 30 November 2025.
Krieter, P., de León, E., Votta, F., & Roşca, A. (2025). Classifying TikToks Locally: Political Content Detection with Phi-4 on Android. Proceedings of the 24th International Conference on Mobile and Ubiquitous Multimedia, MUM ’25, 436–438. https://doi.org/10.1145/3771882.3773943
Keywords: Political content, Human-centered computing, Ubiquitous and mobile computing systems and tools
Authors
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