Meaning making and relatedness: exploring critical data literacies on social media

Abstract

Purpose The goal of this study is to explore how content creators engage in critical data literacies on TikTok, a social media site that encourages the creation and dissemination of user-created, short-form videos. Critical data literacies encompass the ability to reason with, critique, control, and repurpose data for creative uses. Existing work on critical data literacies on social media has focused on understanding of personal data, critique of data use, and strategies to protect privacy. This work focuses on how TikTok content creators repurpose data to construct their own narratives. Design/methodology/approach Through hashtag search, the authors created a corpus of 410 TikTok videos focused on discussing environmental and climate action, and qualitatively coded the videos for data literacies practices and video features (audio, footage, background images) that may support these practices. Findings Content creators engaged in multiple practices to attach meanings to data and situate environmental and climate action discourse in lived experiences. While there were instances of no data practices, we found cases where creators compiled different data sources, situated data in personal and local contexts, and positioned their experiences as data points to supplement or counter other statistics. Creators further leveraged the platform’s technical features, particularly the ability to add original audio and background images, to add narratives to the collective discourse. Originality/value This study presents a unique focus on examining critical data literacies on social media. Findings highlight how content creators repurpose data and integrate personal experiences. They illustrate platform features to support data practices and inform the design of learning environments.

Data and Knowledge Modelling as the Methodological Foundation of the Digital Humanities

“…Curricula can guide students to think critically about how selection, cleaning, and transformation of data embed certain assumptions and biases. Exercises in identifying exclusion or misrepresentation in dataset construction and model design help sensitize students to issues of epistemic injustice that may be obscured by technical processes (Irgens 2020). Further critical engagement involves assessing whose perspectives and narratives are privileged in data modelling pipelines, prompting reflection on power dynamics and marginalisation (D'Ignazio and Klein 2020).…”