Can I write<i> this</i><i> is</i><i> ableist</i><i> AF</i> in a peer review? : a corpus-driven analysis of Twitter engagement strategies across disciplinary groups

Abstract

At a time when scholars are increasingly expected to participate in public knowledge dissemination, social media platforms like Twitter hold great promise for engaging both experts and non-experts. However, it remains unclear in what ways academic tweets are shaped by disciplinary concerns and how this might, in turn, impact audience engagement. Our paper reports an early-stage corpusdriven analysis of 4,000 English tweets from 40 scholars' Twitter accounts across four disciplinary groups: Arts and Humanities (AH), Social Sciences (SS), Life Sciences (LS), and Physical Sciences (PS). Engagement rates, multimodal elements, tweet types, and interaction markers were quantitatively calculated using corpus and computational methods and qualitatively analysed through close reading. Our findings revealed some disciplinary variation in the corpus: specifically, LS used more multimodal elements than SS on Twitter; SS used fewer interactional markers than LS and PS on Twitter. We further found that LS also has the highest number of threads and the longest threads, often to unfold their multimodal information. Despite being the least multimodal and interactive disciplinary group, SS has the highest engagement rate. Our analysis suggests that explicit evaluation and critique play an important role in eliciting responses on Twitter, particularly with regard to current social or political issues-a finding that resonates with previous research on science communication and popularization. The findings can be applied in science communication training to raise disciplinary awareness in shaping one's social media presence.