Digital traces of offline mobilization.

Abstract

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.

Sorry to ask but … how is apology effectiveness dependent on apology content and gender?

“…While both measures similarly capture reactions to the focal tweet, we used two distinct criterion (i.e., dependent) variables in an effort to balance the strengths and limitations of each one and cross-validate tests of our hypotheses. We first operationalized apology effectiveness by recording the number of “likes” (i.e., like count) ascribed to the focal tweet ( M = 20,582, SD = 36,921), which may approximate the extent to which its content is socially validated and legitimatized (Pennycook et al, 2020; Smith et al, 2023).…”

“…We controlled for several variables that could affect the tested relationships. These included the celebrity’s follower count and the number of the accounts followed to provide for the celebrity’s level of social connectedness (Smith et al, 2023). In addition, we controlled for the celebrity’s overall level of Twitter activity (i.e., the total number of tweets posted by the apologizer at the time of downloading the data), the target of the apology (i.e., general public vs. specific individual), the use of multimedia (e.g., video, image) in the focal tweet, and the industry in which the celebrity is employed (i.e., athletics, musician, actors/actresses, news anchors).…”

Theory‐Driven Perspectives on Generative Artificial Intelligence in Business and Management

“…Alongside definitional issues and the need to reexamine how teamwork constructs operate within human-GAI teams, there are practical considerations posed by the introduction of GAI at work. As researchers, we are already facing a poignant challenge in connecting the myriad of ways individuals can interact with networked technologies with their offline behaviours (Brown et al, 2022;Smith et al, 2023). At present, efforts to capture the interplay between actions taken online and actions taken in the real world have largely failed to understand the nuanced behavioural and psychological mechanisms that might link the two (see Smith et al, 2023).…”

The DECIDE Framework: Describing Ethical Choices in Digital-Behavioral-Data Explorations

“…Collective action research within social psychology offers a useful example of this. Over the past two decades, much progress has been made in understanding the mechanisms driving collective action, with many studies relying on digital data (e.g., tweets, Facebook posts) to extend our understanding of how intra-group interactions online might justify, mobilise and incite collective action (Smith et al, 2023;Brown et al, 2022;Smith et al, 2020;Postmes & Brunsting, 2002). While the primary purpose of this research is to understand how and why people and groups might choose to engage in collective action, publishing these studies may have unintended consequences in aiding those who wish to stifle peaceful protest, or alternatively, to provide malicious actors with a means to organise themselves more effectively online (Young et al, 2019).…”

“…Acknowledging such risks within published findings is essential to protect yourself as a researcher and to mitigate against potential harm caused. For instance, in a recent paper Smith et al, (2023) used Twitter data to determine the link between online interactions and attendance at Brexit protests in the U.K. In their discussion, the authors note that they applied the responsible research and innovation (RRI) framework from UK Research & Innovation (2016), acknowledging that their findings should be used to safeguard protests (i.e., by providing information to relevant emergency responders), as opposed to limit lawful protest.…”