Abstract
New Psychoactive Substances (NPS) are drugs that lay in a grey area of legislation, since they are not internationally and officially banned, possibly leading to their not prosecutable trade. The exacerbation of the phenomenon is that NPS can be easily sold and bought online. Here, we consider large corpora of textual posts, published on online forums specialized on drug discussions, plus a small set of known substances and associated effects, which we call seeds. We propose a semi-supervised approach to knowledge extraction, applied to the detection of drugs (comprising NPS) and effects from the corpora under investigation. Based on the very small set of initial seeds, the work highlights how a contrastive approach and context deduction are effective in detecting substances and effects from the corpora. Our promising results, which feature a F1 score close to 0.9, pave the way for shortening the detection time of new psychoactive substances, once these are discussed and advertised on the Internet.
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Notes
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http://www.emcdda.europa.eu/start/2016/drug-markets#pane2/4; All URLs in the paper have been accessed on July 10, 2016.
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A noun-phrase is a phrase that plays the role of a noun such as “the kid that Santa Claus forgot".
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\(precision=\frac{TP}{TP+FP}\).
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\(recall=\frac{TP}{TP+FN}\).
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harmonic mean of precision and recall: \(F1=2\cdot \frac{precision \cdot recall}{precision+recall}\).
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Acknowledgements
This publication arises from the project CASSANDRA, (Computer Assisted Solutions for Studying the Availability aNd Distribution of novel psychoActive substances)" which has received funding from the European Union under the ISEC programme.
Prevention of and fight against crime [JUST2013/ISEC/DRUGS/AG/6414].
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Del Vigna, F., Petrocchi, M., Tommasi, A., Zavattari, C., Tesconi, M. (2016). Semi-supervised Knowledge Extraction for Detection of Drugs and Their Effects. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_31
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