JACIII Vol.21 No.7 pp. 1189-1201
doi: 10.20965/jaciii.2017.p1189


A Method for Detecting Harmful Entries on Informal School Websites Using Morphosemantic Patterns

Michal Ptaszynski*1,†, Fumito Masui*1, Yoko Nakajima*2, Yasutomo Kimura*3, Rafal Rzepka*4, and Kenji Araki*4

*1Department of Computer Science, Kitami Institute of Technology
165 Kouen-cho, Kitami, Hokkaido 090-8507, Japan

*2Department of Information Engineering, National Institute of Technology, Kushiro College
2-32-1 Otanoshike-Nishi, Kushiro-shi, Hokkaido 084-0916, Japan

*3Department of Information and Management Science, Otaru University of Commerce
3-5-21 Midori, Otaru 047-8501, Japan

*4Graduate School of Information Science and Technology, Hokkaido University
Kita 14, Nishi 9, Kita-ku, Sapporo, Hokkaido 060-0814, Japan

Corresponding author

January 18, 2017
August 3, 2017
November 20, 2017
cyberbullying detection, morphosemantics, pattern extraction, semantic role labeling, natural language processing

This paper presents a novel method of analyzing morphosemantic patterns in language to the detect cyberbullying, or frequently appearing harmful messages and entries that aim to humiliate other users. The morphosemantic patterns represent a novel concept, with the assumption that analyzed elements can be perceived as a combination of morphological information, such as parts of speech, and semantic information, such as semantic roles, categories, etc. The patterns are further automatically extracted from the data containing harmful entries (cyberbullying) and non-harmful entries found on the informal websites of Japanese high schools. These website data were prepared and standardized by the Human Rights Center in Mie Prefecture, Japan. The patterns extracted in this way are further applied to a document classification task using the provided data in 10-fold cross-validation. The results indicate that morphosemantic sentence representation can be considered useful in the task of detecting the deceptive and provocative language used in cyberbullying.

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Last updated on Dec. 12, 2017