A customisable pipeline for continuously harvesting socially-minded Twitter users
This work addresses the need for operational definitions of online activists on social media, though it is incremental as it builds on existing metrics for user characterization.
The authors tackled the problem of identifying socially-minded users like online activists on Twitter by developing a continuous pipeline that harvests and analyzes topical network fragments, resulting in a dataset enabling user-defined scoring functions and empirical validation on UK healthcare campaigns.
On social media platforms and Twitter in particular, specific classes of users such as influencers have been given satisfactory operational definitions in terms of network and content metrics. Others, for instance online activists, are not less important but their characterisation still requires experimenting. We make the hypothesis that such interesting users can be found within temporally and spatially localised contexts, i.e., small but topical fragments of the network containing interactions about social events or campaigns with a significant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profile discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profiles dataset includes key network and content-based users metrics, enabling experimentation with user-defined score functions that characterise specific classes of online users. The paper describes the design and implementation of the pipeline and its empirical evaluation on a case study consisting of healthcare-related campaigns in the UK, showing how it supports the operational definitions of online activism, by comparing three experimental ranking functions. The code is publicly available.