Authors:
Yuki Iwasaki
;
Ryohei Orihara
;
Yuichi Sei
;
Hiroyuki Nakagawa
;
Yasuyuki Tahara
and
Akihiko Ohsuga
Affiliation:
University of Electro-Communications, Japan
Keyword(s):
Flaming, Microblogging, Reputation Mining, Topic Extraction, Sentiment Analysis.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Collective Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Web Information Systems and Technologies
;
Web Intelligence
Abstract:
Nowadays, anybody can easily express their opinion publicly through Consumer Generated Media. Because
of this, a phenomenon of flooding criticism on the Internet, called flaming, frequently occurs. Although
there are strong demands for flaming management, a service to reduce damage caused by a flaming after
one occurs, it is very difficult to properly do so in practice. We are trying to keep the flaming from
happening. Concretely, we propose methods to identify a potential tweet which will be a likely candidate of
a flaming on Twitter, considering public opinion among twitter users. We divide flamings into three
categories: criminal episodes, struggles between conflicting values and secret exposures. The first two
represent the vast majority of flaming cases. As for the CEs, a Naïve Bayes-based method has been
promising to identify the cases. As for the SBCVs, we propose a dynamic P/N analysis based on daily
polarity, which represents the strength of the polarity of public opinion on
a given topic. An experiment
using a past flaming case has shown that the method has successfully explained the case as one caused by a
gap between the polarity of the tweet and that of public opinion.
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