Aletras, N. and Chamberlain, B.P. (2018) Predicting Twitter user socioeconomic attributes with network and language information. In: Proceedings of the 29th ACM Conference on Hypertext and Social Media. 29th ACM Conference on Hypertext and Social Media, 09-12 Jul 2018, Baltimore, MD, USA. ACM , pp. 20-24. ISBN 978-1-4503-5427-1
Abstract
Inferring socioeconomic attributes of social media users such as occupation and income is an important problem in computational social science. Automated inference of such characteristics has applications in personalised recommender systems, targeted computational advertising and online political campaigning. While previous work has shown that language features can reliably predict socioeconomic attributes on Twitter, employing information coming from users' social networks has not yet been explored for such complex user characteristics. In this paper, we describe a method for predicting the occupational class and the income of Twitter users given information extracted from their extended networks by learning a low-dimensional vector representation of users, i.e. graph embeddings. We use this representation to train predictive models for occupational class and income. Results on two publicly available datasets show that our method consistently outperforms the state-of-the-art methods in both tasks. We also obtain further significant improvements when we combine graph embeddings with textual features, demonstrating that social network and language information are complementary.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The owner/author(s). This is an author-produced version of a paper subsequently published in Proceedings of the 29th ACM Conference on Hypertext and Social Media. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | social media; graph embeddings; user profiling |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 02 Aug 2019 10:15 |
Last Modified: | 06 Aug 2019 16:43 |
Status: | Published |
Publisher: | ACM |
Refereed: | Yes |
Identification Number: | 10.1145/3209542.3209577 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:144802 |