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2020 | OriginalPaper | Chapter

Overview of eRisk 2020: 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 2020, the fourth 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 two tasks. The first task focused on early detecting signs of self-harm. The second task challenged the participants to automatically filling a depression questionnaire based on user interactions in social media.

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Footnotes
2
In the initial configuration, the test period was shorter but, because of the COVID-19 situation, we decided to extend the test stage in order to facilitate participation.
 
3
Computed with respect to the positive class.
 
4
Observe that Sadeque et al. (see [9], 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.
 
5
Again, we adopt Sadeque et al.’s proposal but we estimate latency only over the true positives.
 
6
In the evaluation we set p to 0.0078, a setting obtained from the eRisk 2017 collection.
 
7
Actually, slightly less than 25% because a couple of questions have more than four possible answers.
 
Literature
1.
go back to reference Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. JAMA Psychiatry 4(6), 561–571 (1961) Beck, A.T., Ward, C.H., Mendelson, M., Mock, J., Erbaugh, J.: An inventory for measuring depression. JAMA Psychiatry 4(6), 561–571 (1961)
4.
go back to reference Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2017, Dublin, Ireland (2017) Losada, D.E., Crestani, F., Parapar, J.: eRISK 2017: CLEF lab on early risk prediction on the internet: experimental foundations. In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2017, Dublin, Ireland (2017)
5.
go back to reference Losada, D.E., Crestani, F., Parapar, J.: Overview of eRISK 2018: early risk prediction on the internet (extended lab overview). In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2018, Avignon, France (2018) Losada, D.E., Crestani, F., Parapar, J.: Overview of eRISK 2018: early risk prediction on the internet (extended lab overview). In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2018, Avignon, France (2018)
8.
go back to reference Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk at CLEF 2019: early risk prediction on the Internet (extended overview). In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2019, Lugano, Switzerland (2019) Losada, D.E., Crestani, F., Parapar, J.: Overview of eRisk at CLEF 2019: early risk prediction on the Internet (extended overview). In: CEUR Proceedings of the Conference and Labs of the Evaluation Forum, CLEF 2019, Lugano, Switzerland (2019)
9.
go back to reference Sadeque, F., Xu, D., Bethard, S.: Measuring the latency of depression detection in social media. In: WSDM, pp. 495–503. ACM (2018) Sadeque, F., Xu, D., Bethard, S.: Measuring the latency of depression detection in social media. In: WSDM, pp. 495–503. ACM (2018)
10.
go back to reference Trotzek, M., Koitka, S., Friedrich, C.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. Knowl. Data Eng. 32(3), 588–601 (2018)CrossRef Trotzek, M., Koitka, S., Friedrich, C.: Utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences. IEEE Trans. Knowl. Data Eng. 32(3), 588–601 (2018)CrossRef
Metadata
Title
Overview of eRisk 2020: Early Risk Prediction on the Internet
Authors
David E. Losada
Fabio Crestani
Javier Parapar
Copyright Year
2020
DOI
https://doi.org/10.1007/978-3-030-58219-7_20

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