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Lookup NU author(s): Flavio Primo,
Professor Paolo Missier,
Professor Alexander Romanovsky
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Springer , 2019.
For re-use rights please refer to the publisher's terms and conditions.
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.
Author(s): Primo F, Missier P, Romanovsky A, Figueredo M, Cacho N
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 19th International Conference on Web Engineering (ICWE 2019)
Year of Conference: 2019
Online publication date: 26/04/2019
Acceptance date: 04/03/2019
Date deposited: 20/05/2019
Library holdings: Search Newcastle University Library for this item
Series Title: Lecture Notes in Computer Science