An advisory method for BBS users and evaluation of BBS comments

Yu Ichifuji; Susumu Konno; Hideaki Sone

doi:10.1016/j.sbspro.2010.01.039

An advisory method for BBS users and evaluation of BBS comments

Yu Ichifuji, Susumu Konno, Hideaki Sone

Organization for Innovations in Data Synergy (organization affiliation only)

Research output: Contribution to journal › Conference article › peer-review

2 Citations (Scopus)

Abstract

Recently, BBS has a problem which threatens the safety of BBS community: vandalism comments. Such comments occur by fault and by misunderstanding. Thus, showing the probability of becoming vandalism comment can reduce such vandalism comments. We proposed a method to calculate the probability of becoming vandalism comment. This method classified each comment into "Normal comment", "Vandalism comment" or "Flaming comment" using word and pair of words based on Bayesian theorem. As the result of experiment, we showed that proposed method could classify normal comments into normal comment class with 64.7% accuracy and vandalism or flaming comments into vandalism comment class with 85.4% accuracy.

Original language	English
Pages (from-to)	218-224
Number of pages	7
Journal	Procedia - Social and Behavioral Sciences
Volume	2
Issue number	1
DOIs	https://doi.org/10.1016/j.sbspro.2010.01.039
Publication status	Published - 2010
Event	1st International Conference on Security Camera Network, Privacy Protection and Community Safety 2009, SPC2009 - Kiryu, Japan Duration: 2009 Oct 28 → 2009 Oct 30

Keywords

Bayesian theorem
Bulletin Board System (BBS)
Morphological analysis

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10.1016/j.sbspro.2010.01.039

Cite this

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title = "An advisory method for BBS users and evaluation of BBS comments",

abstract = "Recently, BBS has a problem which threatens the safety of BBS community: vandalism comments. Such comments occur by fault and by misunderstanding. Thus, showing the probability of becoming vandalism comment can reduce such vandalism comments. We proposed a method to calculate the probability of becoming vandalism comment. This method classified each comment into {"}Normal comment{"}, {"}Vandalism comment{"} or {"}Flaming comment{"} using word and pair of words based on Bayesian theorem. As the result of experiment, we showed that proposed method could classify normal comments into normal comment class with 64.7% accuracy and vandalism or flaming comments into vandalism comment class with 85.4% accuracy.",

keywords = "Bayesian theorem, Bulletin Board System (BBS), Morphological analysis",

author = "Yu Ichifuji and Susumu Konno and Hideaki Sone",

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T1 - An advisory method for BBS users and evaluation of BBS comments

AU - Ichifuji, Yu

AU - Konno, Susumu

AU - Sone, Hideaki

PY - 2010

Y1 - 2010

N2 - Recently, BBS has a problem which threatens the safety of BBS community: vandalism comments. Such comments occur by fault and by misunderstanding. Thus, showing the probability of becoming vandalism comment can reduce such vandalism comments. We proposed a method to calculate the probability of becoming vandalism comment. This method classified each comment into "Normal comment", "Vandalism comment" or "Flaming comment" using word and pair of words based on Bayesian theorem. As the result of experiment, we showed that proposed method could classify normal comments into normal comment class with 64.7% accuracy and vandalism or flaming comments into vandalism comment class with 85.4% accuracy.

AB - Recently, BBS has a problem which threatens the safety of BBS community: vandalism comments. Such comments occur by fault and by misunderstanding. Thus, showing the probability of becoming vandalism comment can reduce such vandalism comments. We proposed a method to calculate the probability of becoming vandalism comment. This method classified each comment into "Normal comment", "Vandalism comment" or "Flaming comment" using word and pair of words based on Bayesian theorem. As the result of experiment, we showed that proposed method could classify normal comments into normal comment class with 64.7% accuracy and vandalism or flaming comments into vandalism comment class with 85.4% accuracy.

KW - Bayesian theorem

KW - Bulletin Board System (BBS)

KW - Morphological analysis

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An advisory method for BBS users and evaluation of BBS comments

Abstract

Keywords

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