Using Qtag to Extract Dominant Public Opinion in Very Large-Scale Conversation

These days VLSC (Very Large-Scale Conversation) is a particular type of online conversation, that is large scale, public, text-based, many-to-many and persistent. The nature of VLSC allows the accumulation of thousands of conversation in a fraction of time, and it often grows out of users’ readable capacity. Therefore, extracting dominant public opinion on VLSC is usually impossible without causing an information overload. In this paper, Qtag is proposed to improve the VLSC environment by extracting public opinion easily, enhancing the value of conversation, and increasing the participants’ willingness to engage. A simulation which mimics reality is built to create a VLSC environment, and two sets of questionnaire are conducted to compare users’ experiences before and after Qtag trial.