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Constructing an inter‐post similarity measure to differentiate the psychological stages in offensive chats
Author(s) -
Miah Md. Waliur Rahman,
Yearwood John,
Kulkarni Siddhivinayak
Publication year - 2015
Publication title -
journal of the association for information science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.903
H-Index - 145
eISSN - 2330-1643
pISSN - 2330-1635
DOI - 10.1002/asi.23247
Subject(s) - similarity (geometry) , cluster analysis , pairwise comparison , computer science , similarity measure , sentence , offensive , measure (data warehouse) , information retrieval , natural language processing , artificial intelligence , psychology , data mining , mathematics , image (mathematics) , operations research
Offensive Internet chats, particularly the child‐exploiting type, tend to follow a documented psychological behavioral pattern. Researchers have identified some important stages in this pattern. The psychological stages broadly include befriending, information exchange, grooming, and approach . Similarities among the posts of a chat play an important role in differentiating as well as in identifying these stages. In this article a novel similarity measure is constructed which gives high Inter‐post‐similarity among the chat‐posts within a particular behavioral stage and low inter‐post‐similarity across different behavioral stages. A psychological stage corpus‐based dictionary is constructed from mining the terms associated with each stage. The dictionary works as a background knowledge‐base to support the similarity measure. To find the inter‐post similarity a modified sentence similarity measure is used. The proposed measure gives improved recognition of inter‐stage and intra‐stage similarity among the chat posts compared with other types of similarity measures. The pairwise inter‐post similarity is used for clustering chat‐posts into the psychological stages. Results of experiments demonstrate that the new clustering method gives better results than some current clustering methods.