Identification of Flaming and Its Applications in CGM - Case Studies toward Ultimate Prevention
Yuki Iwasaki, Ryohei Orihara, Yuichi Sei, Hiroyuki Nakagawa, Yasuyuki Tahara, Akihiko Ohsuga · 2014
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, namely, 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 Naive 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.