Tsakalidis, Adam and Liakata, Maria and Damoulas, Theo and Cristea, Alexandra I. (2019) 'Can we assess mental health through social media and smart devices? addressing bias in methodology and evaluation.', in Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III. Cham: Springer, pp. 407-423. Lecture notes in computer science., 11053 (11053).
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.
|Item Type:||Book chapter|
|Additional Information:||Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 11053).|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1007/978-3-030-10997-4_25|
|Publisher 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|
|Date accepted:||15 June 2018|
|Date deposited:||02 August 2018|
|Date of first online publication:||18 January 2019|
|Date first made open access:||No date available|
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