@inproceedings{ishikawa-etal-2010-detection,
title = "Detection of submitters suspected of pretending to be someone else in a community site",
author = "Ishikawa, Naoki and
Nishimura, Ryo and
Watanabe, Yasuhiko and
Okada, Yoshihiro and
Murata, Masaki",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/727_Paper.pdf",
abstract = "One of the essential factors in community sites is anonymous submission. This is because anonymity gives users chances to submit messages (questions, problems, answers, opinions, etc.) without regard to shame and reputation. However, some users abuse the anonymity and disrupt communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect submitters suspected of pretending to be someone else to manipulate communications in a community site by using machine learning techniques. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.",
}
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<abstract>One of the essential factors in community sites is anonymous submission. This is because anonymity gives users chances to submit messages (questions, problems, answers, opinions, etc.) without regard to shame and reputation. However, some users abuse the anonymity and disrupt communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect submitters suspected of pretending to be someone else to manipulate communications in a community site by using machine learning techniques. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.</abstract>
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%0 Conference Proceedings
%T Detection of submitters suspected of pretending to be someone else in a community site
%A Ishikawa, Naoki
%A Nishimura, Ryo
%A Watanabe, Yasuhiko
%A Okada, Yoshihiro
%A Murata, Masaki
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F ishikawa-etal-2010-detection
%X One of the essential factors in community sites is anonymous submission. This is because anonymity gives users chances to submit messages (questions, problems, answers, opinions, etc.) without regard to shame and reputation. However, some users abuse the anonymity and disrupt communications in a community site. These users and their submissions discourage other users, keep them from retrieving good communication records, and decrease the credibility of the communication site. To solve this problem, we conducted an experimental study to detect submitters suspected of pretending to be someone else to manipulate communications in a community site by using machine learning techniques. In this study, we used messages in the data of Yahoo! chiebukuro for data training and examination.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/727_Paper.pdf
Markdown (Informal)
[Detection of submitters suspected of pretending to be someone else in a community site](http://www.lrec-conf.org/proceedings/lrec2010/pdf/727_Paper.pdf) (Ishikawa et al., LREC 2010)
ACL