Toggle Main Menu Toggle Search

Open Access padlockePrints

A Customisable Pipeline for Continuously Harvesting Socially-Minded Twitter Users

Lookup NU author(s): Flavio Primo, Professor Paolo MissierORCiD, Professor Alexander RomanovskyORCiD



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, speci fic classes of users such as influencers have been given satisfactory operational de finitions 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 signifi cant footprint on Twitter. To explore this hypothesis, we have designed a continuous user profi le discovery pipeline that produces an ever-growing dataset of user profiles by harvesting and analysing contexts from the Twitter stream. The profi les dataset includes key network and content-based users metrics,enabling experimentation with user-de fined score functions that characterise specifi c 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 de finitions of online activism, by comparing three experimental ranking functions. The code is publicly available.

Publication metadata

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

Pages: 91-106

Online publication date: 26/04/2019

Acceptance date: 04/03/2019

Date deposited: 20/05/2019

ISSN: 0302-9743

Publisher: Springer


DOI: 10.1007/978-3-030-19274-7_8

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783030192730