A customisable pipeline for the semi-automated discovery of online activists and social campaigns on Twitter

Abstract

Substantial research is available on detecting influencers on social media platforms. In contrast, comparatively few studies exists on the role of online activists, defined informally as users who actively participate in socially-minded online campaigns. Automatically discovering activists who can potentially be approached by organisations that promote social campaigns is important, but not easy, as they are typically active only locally, and, unlike influencers, they are not central to large social media networks. We make the hypothesis that such interesting users can be found on Twitter within temporally and spatially localised contexts. We define these as small but topical fragments of the network, containing interactions about social events or campaigns with a significant online footprint. To explore this hypothesis, we have designed an iterative discovery pipeline consisting of two alternating phases of user discovery and context discovery. Multiple iterations of the pipeline result in a growing dataset of user profiles for activists, as well as growing set of online social contexts. This mode of exploration differs significantly from prior techniques that focus on influencers, and presents unique challenges because of the weak online signal available to detect activists. The paper describes the design and implementation of the pipeline as a customisable software framework, where user-defined operational definitions of online activism can be explored. We present an empirical evaluation on two extensive case studies, one concerning healthcare-related campaigns in the UK during 2018, the other related to online activism in Italy during the COVID-19 pandemic.

Responses to heat waves: what can Twitter data tell us?

“…The FR ranges from 0 and 1. Accounts with a FR close to 0 are not considered popular, while users with a FR close to 1 are extremely popular (Primo et al 2021).…”

User Modeling and Profiling in Information Systems

“…Information in the user profile can be explicitly provided by the user (explicit user profile), or more frequently, analyzed implicitly by using interaction data between the users and the system (implicit user profile) (Gauch et al 2007). Beyond personalized or adaptive systems, user profiling can also be at the base of behavioral analysis systems for improving decision-making, such as anomaly detection systems (Kwon et al 2021;Wang et al 2018), fraud detection systems (Lausen et al 2020;Zhao et al 2016), customer scoring systems (Esmeli et al 2020;Ramkumar et al 2010), influencer or leader detection systems (Girgin 2021;Primo et al 2021), and terrorist networks (Tundis and Mühlhäuser 2017;Yadav et al 2019).…”