Group Specific Text Discovery Using Abnormal Detection

Abstract

In social media many users send personal messages depending on their environments and such messages are used as outputs from a sensor system observing the real world. But the social media is quite different from a general sensor system because users regarded as sensors make messages based on various judgement criteria and the criteria is not controllable. In this study we assume that the judgement criteria occurs according to their belonging communities. So we try to extract messages emphasizing the difference of communities. In this paper we proposed a group specific text discovery method using abnormal detection. We use Twitter as messages generated by social media users. Because the tweets include description of events and tweet generated location, we can extract characteristic tweets based on their generated location. In an evaluation experiment we used tweets related to heavy snow in Yamanashi and found some messages describing local information comparing with tweets except Yanamashi.