Zhang, Z. orcid.org/0000-0002-8587-8618 and Bors, G. (2019) “Less is more” : mining useful features from Twitter user profiles for Twitter user classification in the public health domain. Online Information Review, 44 (1). pp. 213-237. ISSN 1468-4527
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 Emerald Publishing Limited. This is an author-produced version of a paper subsequently published in Online Information Review. This version is distributed under the terms of the Creative Commons Attribution-NonCommercial Licence (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You may not use the material for commercial purposes. |
Keywords: | Social media; Machine learning; Twitter; Public health; Data science |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Oct 2020 10:07 |
Last Modified: | 26 Oct 2020 11:25 |
Status: | Published |
Publisher: | Emerald |
Refereed: | Yes |
Identification Number: | 10.1108/oir-05-2019-0143 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167077 |
Download
Filename: Less is more - accepted manuscript WRRO.pdf
Licence: CC-BY-NC 4.0