Short-form video (SFV) has become a globally popular form of entertainment in recent years, appearing on major social media platforms. However, current research indicate that short video addiction can lead to numerous negative effects on both physical and psychological health, such as decreased attention span and reduced motivation to learn. Additionally, Short-form Video Addiction (SFVA) has been linked to other issues such as a lack of psychological support in real life, family or academic pressure, and social anxiety. Currently, the detection of SFVA typically occurs only after users experience negative consequences. Therefore, we aim to construct a short video addiction dataset based on social network behavior and design an early detection framework for SFVA. Previous mental health detection research on online social media has mostly focused on detecting depression and suicidal tendency. In this study, we propose the first early detection framework for SFVA EarlySD. We first introduce large language models (LLMs) to address the common issues of sparsity and missing data in graph datasets. Meanwhile, we categorize social network behavior data into different modalities and design a heterogeneous social network structure as the primary basis for detecting SFVA. We conduct a series of quantitative analysis on short video addicts using our self-constructed dataset, and perform extensive experiments to validate the effectiveness of our method EarlySD, using social data and heterogeneous social graphs in the detection of short video addiction.

翻译：近年来，短视频已成为全球范围内流行的娱乐形式，广泛出现在各大社交媒体平台。然而，现有研究表明，短视频成瘾会对身心健康产生诸多负面影响，例如注意力持续时间下降和学习动机减弱。此外，短视频成瘾还与现实生活中缺乏心理支持、家庭或学业压力以及社交焦虑等其他问题相关。目前，短视频成瘾的检测通常仅在用户经历负面后果后才进行。因此，我们旨在构建一个基于社交网络行为的短视频成瘾数据集，并设计一个针对短视频成瘾的早期检测框架。以往关于在线社交媒体的心理健康检测研究大多集中于抑郁和自杀倾向的识别。在本研究中，我们提出了首个针对短视频成瘾的早期检测框架 EarlySD。我们首先引入大语言模型来解决图数据集中普遍存在的稀疏性和数据缺失问题。同时，我们将社交网络行为数据划分为不同模态，并设计了一个异质社交网络结构，作为检测短视频成瘾的主要依据。我们利用自建数据集对短视频成瘾者进行了一系列定量分析，并通过大量实验验证了我们所提出的方法 EarlySD 在利用社交数据和异质社交图进行短视频成瘾检测方面的有效性。