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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Notes
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Reddit privacy policy states explicitly that the posts and comments redditors make are not private and will still be accessible after the redditor’s account is deleted. Reddit does not permit unauthorized commercial use of its contents or redistribution, except as permitted by the doctrine of fair use. This research is an example of fair use.
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The number of terms per submission are counted after pre-processing the texts with the scikit-learn Python toolkit, scikit-learn.org. This was configured with no stopword processing and no vocabulary pruning based on document frequency.
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We employed sklearn library, version 0.16.1, for Python. Vectorisation was done with the TfidfVectorizer–with a standard stoplist and removing terms that appear in less than 20 documents–and classification was done with the LogisticRegression class.
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This strategy does not make any text analysis and, therefore, it does not make sense to wait any longer to make the decision.
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Again, this strategy does not make any text analysis and, therefore, it does not make sense to wait any longer to make the decision.
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Acknowledgements
This research was funded by the Swiss National Science Foundation (project “Early risk prediction on the Internet: an evaluation corpus”, 2015). The first author also thanks the financial support obtained from “Ministerio de Economía y Competitividad” of the Goverment of Spain and FEDER Funds under the research project TIN2015-64282-R.
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Losada, D.E., Crestani, F. (2016). A Test Collection for Research on Depression and Language Use. In: Fuhr, N., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2016. Lecture Notes in Computer Science(), vol 9822. Springer, Cham. https://doi.org/10.1007/978-3-319-44564-9_3
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