Studies on a multidimensional public opinion network model and its topic detection algorithm

"We the Media" networks are real time and open, and such networks lack a gatekeeper system. As netizens' comments on emergency events are disseminated, negative public opinion topics and confrontations concerning those events also spread widely on "We the Media" networks. Gradually, this phenomenon has attracted scholarly attention, and all social circles attach importance to the phenomenon as well. In existing topic detection studies, a topic is mainly defined as an "event" from the perspective of news-media information flow, but in the "We the Media" era, there are often many different views or topics surrounding a specific public opinion event. In this paper, a study on the detection of public opinion topics in "We the Media" networks is presented, starting with the characteristics of the elements found in public opinions on "We the Media" networks; such public opinions are multidimensional, multilayered and possess multiple attributes. By categorizing the elements' attributes using social psychology and system science categories as references, we build a multidimensional network model oriented toward the topology of public opinions on "We the Media" networks. Based on the real process by which multiple topics concerning the same event are generated and disseminated, we designed a topic detection algorithm that works on these multidimensional public opinion networks. As a case study, the "Explosion in Tianjin Port on August 12, 2015" accident was selected to conduct empirical analyses on the algorithm's effectiveness. The theoretical and empirical research findings of this paper are summarized along the following three aspects. 1. The multidimensional network model can be used to effectively characterize the communication characteristics of multiple topics on "We the Media" networks, and it provided the modeling ideas for the present paper and for other related studies on "We the Media" public opinion networks. 2. Using the multidimensional topic detection algorithm, 70% of the public opinion topics concerning the case study event were effectively detected, which shows that the algorithm is effective at detecting topics from the information flow on "We the Media" networks. 3. By defining the psychological scores of single and paired Chinese keywords in public opinion information, the topic detection algorithm can also be used to judge the sentiment tendencies of each topic, which can facilitate a timely understanding of public opinion and reveal negative topics under discussion on "We the Media" networks.