A Test Collection for Research on Depression and Language Use
Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction be...
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Published in | Experimental IR Meets Multilinguality, Multimodality, and Interaction pp. 28 - 39 |
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Main Authors | , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
2016
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Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3319445634 9783319445632 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-319-44564-9_3 |
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Abstract | Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction between language and depression. In this paper, we describe our first steps to fill this gap. We outline the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. Further, we propose a novel early detection task and define a novel effectiveness measure to systematically compare early detection algorithms. This new measure takes into account both the accuracy of the decisions taken by the algorithm and the delay in detecting positive cases. We also present baseline results with novel detection methods that process users’ interactions in different ways. |
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AbstractList | Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be associated with distinctive linguistic patterns. However, there is a lack of publicly available data for doing research on the interaction between language and depression. In this paper, we describe our first steps to fill this gap. We outline the methodology we have adopted to build and make publicly available a test collection on depression and language use. The resulting corpus includes a series of textual interactions written by different subjects. The new collection not only encourages research on differences in language between depressed and non-depressed individuals, but also on the evolution of the language use of depressed individuals. Further, we propose a novel early detection task and define a novel effectiveness measure to systematically compare early detection algorithms. This new measure takes into account both the accuracy of the decisions taken by the algorithm and the delay in detecting positive cases. We also present baseline results with novel detection methods that process users’ interactions in different ways. |
Author | Losada, David E. Crestani, Fabio |
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Copyright | Springer International Publishing Switzerland 2016 |
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DOI | 10.1007/978-3-319-44564-9_3 |
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Discipline | Languages & Literatures Computer Science |
EISBN | 9783319445649 3319445642 |
EISSN | 1611-3349 |
Editor | Cappellato, Linda Larsen, Birger Balog, Krisztian Quaresma, Paulo Fuhr, Norbert Gonçalves, Teresa Macdonald, Craig Ferro, Nicola |
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PublicationSubtitle | 7th International Conference of the CLEF Association, CLEF 2016, Évora, Portugal, September 5-8, 2016, Proceedings |
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Snippet | Several studies in the literature have shown that the words people use are indicative of their psychological states. In particular, depression was found to be... |
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SubjectTerms | Depressed Individual Late Detection Minority Class Positive Case Test Collection |
Title | A Test Collection for Research on Depression and Language Use |
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