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Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation

Tsakalidis, Adam; Liakata, Maria; Damoulas, Theo; Cristea, Alexandra I.

Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation Thumbnail


Authors

Adam Tsakalidis

Maria Liakata

Theo Damoulas



Contributors

Ulf Brefeld
Editor

Edward Curry
Editor

Elizabeth Daly
Editor

Brian MacNamee
Editor

Alice Marascu
Editor

Fabio Pinelli
Editor

Michele Berlingerio
Editor

Neil Hurley
Editor

Abstract

Predicting mental health from smartphone and social media data on a longitudinal basis has recently attracted great interest, with very promising results being reported across many studies. Such approaches have the potential to revolutionise mental health assessment, if their development and evaluation follows a real world deployment setting. In this work we take a closer look at state-of-the-art approaches, using different mental health datasets and indicators, different feature sources and multiple simulations, in order to assess their ability to generalise. We demonstrate that under a pragmatic evaluation framework, none of the approaches deliver or even approach the reported performances. In fact, we show that current state-of-the-art approaches can barely outperform the most naive baselines in the real-world setting, posing serious questions not only about their deployment ability, but also about the contribution of the derived features for the mental health assessment task and how to make better use of such data in the future.

Citation

Tsakalidis, A., Liakata, M., Damoulas, T., & Cristea, A. I. (2019). Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation. In U. Brefeld, E. Curry, E. Daly, B. MacNamee, A. Marascu, F. Pinelli, …N. Hurley (Eds.), Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III (407-423). https://doi.org/10.1007/978-3-030-10997-4_25

Conference Name European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018 Applied Data Science Track)
Conference Location Dublin
Start Date Sep 10, 2018
End Date Sep 14, 2018
Acceptance Date Jun 15, 2018
Online Publication Date Jan 18, 2019
Publication Date Jan 18, 2019
Deposit Date Aug 2, 2018
Publicly Available Date Aug 2, 2018
Volume 11053
Pages 407-423
Series Title Lecture notes in computer science
Series Number 11053
Series ISSN 0302-9743,1611-3349
Book Title Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III.
ISBN 9783030109967
DOI https://doi.org/10.1007/978-3-030-10997-4_25
Public URL https://durham-repository.worktribe.com/output/1144122
Additional Information Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11053).

Files

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Copyright Statement
This is a post-peer-review, pre-copyedit version of an article published in Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-10997-4_25







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