Modelling Online Engagement Patterns To Infer Risk

The purpose of this pre-registered study was to investigate how different ideological groups justified and mobilised collective action online. We collected 6878 posts from the social media accounts of pro-Black Lives Matter (n = 13) and anti-Black Lives Matter (n = 9) groups who promoted collective action in the month after George Floyd's murder and the Black Lives Matter (BLM) protests and counter-protests. We used content analysis and natural language processing (NLP) to analyse the content and psychological function of the posts. We found that both groups perceived their action as ‘system-challenging’, with pro-BLM accounts focused more on outgroup actions to mobilise collective action, and anti-BLM accounts focused more on ingroup identity. The reverse pattern occurred when the accounts were attempting to justify action. The implications are that groups’ ideology and socio-structural position should be accounted for when understanding differences in how and why groups mobilise through online interactions.

(From the journal abstract)

Brown, O., Lowery, C., & Smith, L. G. E. (2022) How opposing ideological groups use online interactions to justify and mobilise collective action. European Journal of Social Psychology, 00, 1– 29. https://doi.org/10.1002/ejsp.2886

Since 2009, there has been an increase in global protests and related online activity. Yet, it is unclear how and why online activity is related to the mobilization of offline collective action. One proposition is that online polarization (or a relative change in intensity of posting mobilizing content around a salient grievance) can mobilize people offline. The identity-norm nexus and normative alignment models of collective action further argue that to be mobilizing, these posts need to be socially validated. To test these propositions, across two analyses, we used digital traces of online behavior and data science techniques to model people’s online and offline behavior around a mass protest. In Study 1a, we used Twitter behavior posted on the day of the protest by attendees or nonattendees (759 users; 7,592 tweets) to train and test a classifier that predicted, with 80% accuracy, who participated in offline collective action. Attendees used their mobile devices to plan logistics and broadcast their presence at the protest. In Study 1b, using the longitudinal Twitter data and metadata of a subset of users from Study 1a (209 users; 277,556 tweets), we found that participation in the protest was not associated with an individual’s online polarization over the year prior to the protest, but it was positively associated with the validation (“likes”) they received on their relevant posts. These two studies demonstrate that rather than being low cost or trivial, socially validated online interactions about a grievance are actually key to the mobilization and enactment of collective action.

(From the journal abstract)

Smith, L. G. E., Piwek, L., Hinds, J., Brown, O., & Joinson, A. (2023). Digital traces of offline mobilization. Journal of Personality and Social Psychology, 125(3), 496–518. https://doi.org/10.1037/pspa0000338

Understanding people’s movement patterns has many important applications, from analyzing habits and social behaviors, to predicting the spread of disease. Information regarding these movements and their locations is now deeply embedded in digital data generated via smartphones, wearable sensors, and social-media interactions. Research has largely used data-driven modeling to detect patterns in people’s movements, but such approaches are often devoid of psychological theory and fail to capitalize on what movement data can convey about associated thoughts, feelings, attitudes, and behavior. This article outlines trends in current research in this area and discusses how psychologists can better address theoretical and methodological challenges in future work while capitalizing on the opportunities that digital movement data present. We argue that combining approaches from psychology and data science will improve researchers’ and policy makers’ abilities to make predictions about individuals’ or groups’ movement patterns. At the same time, an interdisciplinary research agenda will provide greater capacity to advance psychological theory.

(From the journal abstract)

Hinds, J., Brown, O., Smith, L. G. E., Piwek, L., Ellis, D. A., & Joinson, A. N. (2022). Integrating Insights About Human Movement Patterns From Digital Data Into Psychological Science. Current Directions in Psychological Science, 31(1), 88-95. https://doi.org/10.1177/09637214211042324

There has been a rise in the number of terrorist incidents in which social media use has been implicated in the planning and execution of the attack. Efforts to identify online risk signals of terrorist offending is challenging due to the existence of the specificity problem– that while many people express ideologically and hateful views, very few go on to commit terrorist acts. Here, we demonstrate that risk signals of terrorist offending can be identified in a sample of 119,473 online posts authored by 26 convicted right-wing extremists and 48 right-wing extremists who did not have convictions. Combining qualitative analysis with computational modelling, we show that it is not ideological or hateful content that indicates the risk of an offence, but rather content about violent action, operational planning, and logistics. Our findings have important implications for theories of mobilization and radicalization.

(From the journal abstract)

Brown, O., Smith, L. G. E., Davidson, B. I., Racek, D., & Joinson, A. (2023) Online risk signals of offline terrorist offending. PsyArXiv https://doi.org/10.31234/osf.io/hej3r