The increasing importance of videos as a medium for engagement, communication, and content creation makes them critical for organizations to consider for user feedback. However, sifting through vast amounts of video content on social media platforms to extract requirements-relevant feedback is challenging. This study delves into the potential of TikTok and YouTube, two widely used social media platforms that focus on video content, in identifying relevant user feedback that may be further refined into requirements using subsequent requirement generation steps. We evaluated the prospect of videos as a source of user feedback by analyzing audio and visual text, and metadata (i.e., description/title) from 6276 videos of 20 popular products across various industries. We employed state-of-the-art deep learning transformer-based models, and classified 3097 videos consisting of requirements relevant information. We then clustered relevant videos and found multiple requirements relevant feedback themes for each of the 20 products. This feedback can later be refined into requirements artifacts. We found that product ratings (feature, design, performance), bug reports, and usage tutorial are persistent themes from the videos. Video-based social media such as TikTok and YouTube can provide valuable user insights, making them a powerful and novel resource for companies to improve customer-centric development.

翻译：随着视频作为参与、沟通和内容创作媒介的重要性日益增加，它们已成为组织获取用户反馈的关键来源。然而，从社交媒体平台的海量视频内容中筛选出与需求相关的反馈极具挑战性。本研究深入探究了TikTok和YouTube这两个专注于视频内容的广泛使用的社交媒体平台，在识别相关用户反馈方面的潜力，这些反馈可经由后续需求生成步骤进一步提炼为需求。我们通过分析来自20款跨行业热门产品的6276个视频的音频、视觉文本及元数据（即描述/标题），评估了视频作为用户反馈来源的前景。我们采用了基于Transformer的先进深度学习模型，并分类出3097个包含需求相关信息的视频。随后，我们对相关视频进行聚类，发现了针对20款产品的多个需求相关反馈主题。这些反馈后续可被提炼为需求制品。研究发现，产品评价（功能、设计、性能）、错误报告以及使用教程是视频中持续出现的主题。基于视频的社交媒体（如TikTok和YouTube）能够提供宝贵的用户洞察，从而成为企业改善以客户为中心开发的强大且新颖的资源。