让语音数据融入社交媒体研究，突破视觉主导的分析局限
Re-calibrating methodologies in social media research: Challenge the visual, work with Speech
- 提出TikTok字幕工具包，实现语音数据与现有研究流程无缝对接
- 实证显示语音分析在叙事类内容中效果显著，但对音乐/非语言内容帮助有限
- 倡导将声音研究作为补充而非替代，提升多模态内容理解深度
本文从方法论角度反思社交媒体研究如何有效处理基于语音的数据。尽管当代媒介研究已广泛采用文本、视觉和关系数据，听觉维度仍相对未被充分探索。基于二次口语性理论及对纯视觉文化的批判，论文主张大规模考量声音与语音能深化对多模态数字内容的理解。为此，提出了可直接集成到现有工作流中的TikTok Subtitles Toolkit，支持便捷的语音处理。通过两个案例展示：#storytime等叙事类内容可通过语音分析获得丰富洞见，而以音乐或非语言表达为主的内容则难以从中获益。论文呼吁研究者审慎整合听觉探索，作为现有方法的补充而非替代。最终指出，拓展方法工具箱有助于更深入解读平台化内容，应对日益多模态的数字文化。
原文摘要 · Abstract (English)
This article methodologically reflects on how social media scholars can effectively engage with speech-based data in their analyses. While contemporary media studies have embraced textual, visual, and relational data, the aural dimension remained comparatively under-explored. Building on the notion of secondary orality and rejection towards purely visual culture, the paper argues that considering voice and speech at scale enriches our understanding of multimodal digital content. The paper presents the TikTok Subtitles Toolkit that offers accessible speech processing readily compatible with existing workflows. In doing so, it opens new avenues for large-scale inquiries that blend quantitative insights with qualitative precision. Two illustrative cases highlight both opportunities and limitations of speech research: while genres like #storytime on TikTok benefit from the exploration of spoken narratives, nonverbal or music-driven content may not yield significant insights using speech data. The article encourages researchers to integrate aural exploration thoughtfully to complement existing methods, rather than replacing them. I conclude that the expansion of our methodological repertoire enables richer interpretations of platformised content, and our capacity to unpack digital cultures as they become increasingly multimodal.
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