“Less is more”: Mining useful features from Twitter user profiles for Twitter user classification in the public health domain
ISSN: 1468-4527
Article publication date: 31 December 2019
Issue publication date: 22 January 2020
Abstract
Purpose
This work studies automated user classification on Twitter in the public health domain, a task that is essential to many public health-related research works on social media but has not been addressed. The purpose of this paper is to obtain empirical knowledge on how to optimise the classifier performance on this task.
Design/methodology/approach
A sample of 3,100 Twitter users who tweeted about different health conditions were manually coded into six most common stakeholders. The authors propose new, simple features extracted from the short Twitter profiles of these users, and compare a large set of classification models (including state-of-the-art) that use more complex features and with different algorithms on this data set.
Findings
The authors show that user classification in the public health domain is a very challenging task, as the best result the authors can obtain on this data set is only 59 per cent in terms of F1 score. Compared to state-of-the-art, the methods can obtain significantly better (10 percentage points in F1 on a “best-against-best” basis) results when using only a small set of 40 features extracted from the short Twitter user profile texts.
Originality/value
The work is the first to study the different types of users that engage in health-related communication on social media, applicable to a broad range of health conditions rather than specific ones studied in the previous work. The methods are implemented as open source tools, and together with data, are the first of this kind. The authors believe these will encourage future research to further improve this important task.
Keywords
Acknowledgements
This paper does not contain any studies with human participants performed by any of the authors (nevertheless data collection from Twitter was still subject to the authors’ institution’s internal ethical approval).
Citation
Zhang, Z. and Bors, G. (2020), "“Less is more”: Mining useful features from Twitter user profiles for Twitter user classification in the public health domain", Online Information Review, Vol. 44 No. 1, pp. 213-237. https://doi.org/10.1108/OIR-05-2019-0143
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited