ABSTRACT
During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson surfaced on Twitter using #ArsonEmergency. The extent to which bots and trolls were responsible for disseminating and amplifying this misinformation has received media scrutiny and academic research. Here, we study Twitter communities spreading this misinformation during the newsworthy event, and investigate the role of online communities using a natural experiment approach-before and after reporting of bots promoting the hashtag was broadcast by the mainstream media. Few bots were found, but the most bot-like accounts were social bots, which present as genuine humans, and trolling behaviour was evident. 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, the introduction of the bot report changed the discussion dynamic: Opposers only responded immediately, while Supporters countered strongly for days, but new unaffiliated accounts drawn into the discussion shifted the dominant narrative from arson misinformation to factual and official information. This foiled Supporters' efforts, highlighting the value of exposing misinformation. We speculate that the communication strategies observed here could inform counter-strategies in other misinformation-related discussions. Supplementary Information: The online version contains supplementary material available at 10.1007/s13278-022-00892-x.
ABSTRACT
To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
