Abstract
While short-form videos head to reshape the entire social media landscape, experts are exceedingly worried about their depressive impacts on viewers, as evidenced by medical studies. To prevent widespread consequences, platforms are eager to predict these videos' impact on viewers' mental health. Subsequently, they can take intervention measures, such as revising recommendation algorithms and displaying viewer discretion. Nevertheless, applicable predictive methods lack relevance to well-established medical knowledge, which outlines clinically proven external and environmental factors of depression. To account for such medical knowledge, we resort to an emergent methodological discipline, seeded Neural Topic Models (NTMs). However, existing seeded NTMs suffer from the limitations of single-origin topics, unknown topic sources, unclear seed supervision, and suboptimal convergence. To address those challenges, we develop a novel Knowledge-guided Multimodal NTM to predict a short-form video's depressive impact on viewers. Extensive empirical analyses using TikTok and Douyin datasets prove that our method outperforms state-of-the-art benchmarks. Our method also discovers medically relevant topics from videos that are linked to depressive impact. We contribute to IS with a novel video analytics method that is generalizable to other video classification problems. Practically, our method can help platforms understand videos' mental impacts, thus adjusting recommendations and video topic disclosure.
Abstract (translated)
尽管短时视频正在彻底改变社交媒体格局，但专家对它们对观众产生的沮丧影响感到担忧，这是医学研究的结果。为了防止广泛后果，平台渴望预测这些视频对观众心理健康的影响。因此，他们可以采取干预措施，例如修改推荐算法和显示观众自主性。然而，适用的预测方法与已建立的抑郁症医学知识不相关，这揭示了抑郁症的外部和环境因素。为了应对这种医学知识，我们求助于新兴的方法论学科，即以知识为导向的神经主题模型（NTMs）。然而，现有的NTM存在单一来源主题、未知主题来源、不明确的种子监督和次优收敛等限制。为了应对这些挑战，我们开发了一种新知识引导的多模态NTM，以预测短时视频对观众的沮丧影响。通过使用TikTok和Douyin数据集的丰富实证分析证明，我们的方法超越了最先进的基准。我们的方法还从与沮丧影响相关的视频中发现了具有医学相关性的主题。我们与IS合作研发了一种可应用于其他视频分类问题的视频分析方法。实际上，我们的方法可以帮助平台理解视频的心理健康影响，从而调整推荐和视频主题披露。
URL
https://arxiv.org/abs/2402.10045
