The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers

• URL: http://arxiv.org/abs/2501.11770v1
• Date: Mon, 20 Jan 2025 22:21:18 GMT
• Title: The Value of Nothing: Multimodal Extraction of Human Values Expressed by TikTok Influencers
• Authors: Alina Starovolsky-Shitrit, Alon Neduva, Naama Appel Doron, Ella Daniel, Oren Tsur,
• Abstract summary: In this paper, we extract implicit values from TikTok movies uploaded by online influencers targeting children and adolescents. We curated a dataset of hundreds of TikTok movies and annotated them according to the Schwartz Theory of Personal Values. Our results pave the way to further research on influence and value transmission in video-based social platforms.
• Score: 2.3592914313389253
• License:
• Abstract: Societal and personal values are transmitted to younger generations through interaction and exposure. Traditionally, children and adolescents learned values from parents, educators, or peers. Nowadays, social platforms serve as a significant channel through which youth (and adults) consume information, as the main medium of entertainment, and possibly the medium through which they learn different values. In this paper we extract implicit values from TikTok movies uploaded by online influencers targeting children and adolescents. We curated a dataset of hundreds of TikTok movies and annotated them according to the Schwartz Theory of Personal Values. We then experimented with an array of Masked and Large language model, exploring how values can be detected. Specifically, we considered two pipelines -- direct extraction of values from video and a 2-step approach in which videos are first converted to elaborated scripts and then values are extracted. Achieving state-of-the-art results, we find that the 2-step approach performs significantly better than the direct approach and that using a trainable Masked Language Model as a second step significantly outperforms a few-shot application of a number of Large Language Models. We further discuss the impact of fine-tuning and compare the performance of the different models on identification of values present or contradicted in the TikTok. Finally, we share the first values-annotated dataset of TikTok videos. Our results pave the way to further research on influence and value transmission in video-based social platforms.

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