Re-calibrating methodologies in social media research: Challenge the visual, work with Speech

TL;DR

This paper advocates for integrating speech analysis into social media research, exemplified by a TikTok speech toolkit, to enrich understanding of multimodal digital content and expand methodological approaches.

Contribution

It introduces the TikTok Subtitles Toolkit for accessible speech processing and discusses how speech analysis can complement visual methods in social media studies.

Findings

Speech analysis enriches understanding of TikTok content.

Genres like #storytime benefit from spoken narrative analysis.

Nonverbal and music-driven content may have limited insights from speech data.

Abstract

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.