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2019 | OriginalPaper | Chapter

Overview of eRisk 2019 Early Risk Prediction on the Internet

Authors : David E. Losada, Fabio Crestani, Javier Parapar

Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Publisher: Springer International Publishing

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Abstract

This paper provides an overview of eRisk 2019, the third edition of this lab under the CLEF conference. The main purpose of eRisk is to explore issues of evaluation methodology, effectiveness metrics and other processes related to early risk detection. Early detection technologies can be employed in different areas, particularly those related to health and safety. This edition of eRisk had three tasks. Two of them shared the same format and focused on early detecting signs of depression (T1) or self-harm (T2). The third task focused on an innovative challenge related to automatically filling a depression questionnaire based on user interactions in social media.

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previous chapter Overview of the CLEF eHealth Evaluation Lab 2019

next chapter ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

However, following the extraction method suggested by Coppersmith and colleagues [2], the post discussing the diagnosis was removed from the collection.

More information about the server can be found on the lab website http://early.irlab.org/server.html.

Observe that Sadeque et al. (see [6], p. 497) computed the latency for all users such that \(g_u=1\). We argue that latency should be computed only for the true positives. The false negatives (\(g_u=1\), \(d_u=0\)) are not detected by the system and, therefore, they would not generate an alert.

Again, we adopt Sadeque et al.’s proposal but we estimate latency only over the true positives.

In the eRisk 2017 collection this led to setting p to 0.0078.

Slightly less than 25% because a couple of questions have more than four possible answers.

Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. JAMA Psychiatry 4(6), 561–571 (1961). https://doi.org/10.1001/archpsyc.1961.01710120031004CrossRef

Coppersmith, G., Dredze, M., Harman, C.: Quantifying mental health signals in Twitter. In: ACL Workshop on Computational Linguistics and Clinical Psychology (2014)

Losada, D.E., Crestani, F.: A test collection for research on depression and language use. In: Fuhr, N., et al. (eds.) CLEF 2016. LNCS, vol. 9822, pp. 28–39. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44564-9_3CrossRef

Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 346–360. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_30 CrossRef

Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk: early risk prediction on the internet. In: Bellot, P., et al. (eds.) CLEF 2018. LNCS, vol. 11018, pp. 343–361. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98932-7_30CrossRef

Sadeque, F., Xu, D., Bethard, S.: Measuring the latency of depression detection in social media. In: WSDM, pp. 495–503. ACM (2018)

Trotzek, M., Koitka, S., Friedrich, C.M.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. CoRR abs/1804.07000 (2018)

Title: Overview of eRisk 2019 Early Risk Prediction on the Internet
Authors: David E. Losada
Fabio Crestani
Javier Parapar
Publisher: Springer International Publishing
Book: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-28576-0

Electronic ISBN: 978-3-030-28577-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-28577-7_27

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