Promoting and countering misinformation during Australia's 2019-2020 bushfires: A case study of polarisation

TL;DR

This study analyzes Twitter discussions during Australia's 2019-2020 bushfires, revealing how online communities and social bots contributed to misinformation about arson, and how opposition strategies evolved to counteract it.

Contribution

The paper provides a detailed case study of misinformation dynamics and bot involvement during a major environmental crisis, highlighting community polarization and counter-strategies.

Findings

Supporters promoted misinformation through direct engagement and external links.

Opposers retweeted factual information and gained high centrality.

Coordination between opposers and unaffiliated accounts increased over time.

Abstract

During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson resurfaced on Twitter using #ArsonEmergency. The extent to which bots were responsible for disseminating and amplifying this misinformation has received scrutiny in the media and academic research. Here we study Twitter communities spreading this misinformation during the population-level event, and investigate the role of online communities and bots. Our in-depth investigation of the dynamics of the discussion uses a phased approach -- before and after reporting of bots promoting the hashtag was broadcast by the mainstream media. Though we did not find many bots, the most bot-like accounts were social bots, which present as genuine humans. Further, we distilled meaningful quantitative differences between two polarised communities in the Twitter discussion, resulting in the following insights. First, Supporters of the arson narrative promoted misinformation by engaging others directly with replies and mentions using hashtags and links to external sources. In response, Opposers retweeted fact-based articles and official information. Second, Supporters were embedded throughout their interaction networks, but Opposers obtained high centrality more efficiently despite their peripheral positions. By the last phase, Opposers and unaffiliated accounts appeared to coordinate, potentially reaching a broader audience. Finally, unaffiliated accounts shared the same URLs as Opposers over Supporters by a ratio of 9:1 in the last phase, having shared mostly Supporter URLs in the first phase. This foiled Supporters' efforts, highlighting the value of exposing misinformation campaigns. We speculate that the communication strategies observed here could be discoverable in other misinformation-related discussions and could inform counter-strategies.