Social media platforms such as Twitter/X, Reddit, Bluesky and Mastodon are nowadays important spaces where people engage with, discuss and promote social issues and politics. While the deliberative potential of online discussion platforms and hence their contribution to democracy and social cohesion is the subject of both concern (and sometimes, moral panic) and also on-going academic research, there can be no doubt that social media is important in the modern information environment, with social media actors responding to and at times influencing how mainstream media engages with and promotes issues.
Framing is the process whereby actors with an interest in a contested topic use strategic communication to provide interpretation, context or perspective that aligns with the actor’s interests. The actors might include individuals, groups, organisations, governments, and the topic could relate to, for example, current events, government policy or social issues, and it has been argued that framing involves strategically “selecting and highlighting some features of while omitting others”, and that frames are “manifested by the presence or absence of certain key words” (Entman, 1991, pp. 52–53).
This presentation outlines work-in-progress on a quantitative methodology for identifying frames and understanding how frames spread in the Australian information environment. Quantitative approaches to frame identification include correspondence analysis, semantic network analysis, topic modelling and more recently, large language models (LLMs). Our approach for identifying frames draws on Kwak et al’s (2021) FrameAxis, which uses LLMs to identify “microframes” which are dimensions of discussion relating to an issue (a frame comprises one or more microframes). In discussion about immigration, for example, a microframe might be legal versus illegal immigration, and the FrameAxis approach measures how biased a given text (for example, a newspaper article) is with regard to a particular microframe i.e. does it focus predominantly on ‘illegal’ immigration rather than ‘legal’ immigration (or vice-versa)? FrameAxis also measures the relative prominence (intensity) of a particular microframe: is the text heavily focused on the legal-illegal microframe, hence giving less weight to other possible microframes (e.g. the segregated-desegregated microframe, relating to integration of migrants into society)?
While the presentation will focus on the identification of frames, we will also briefly discuss the second part of the methodology, which involves using social network analysis to understand how frames are disseminated on social media and the relative contribution of different actors (e.g. elites vs everyday citizens) to this spread.
Entman, R. M. (1991). Framing U.S. coverage of international news: Contrasts in narratives of the KAL and Iran air incidents. Journal of Communication, 43(4), 51–58.
Kwak, H., An, J., Jing, E., & Ahn, Y.-Y. (2021). FrameAxis: Characterizing microframe bias and intensity with word embedding. PeerJ Computer Science, 7:e644.