Public interest in cryptocurrencies has consistently risen over the past decade. Owing to this rapid growth, cryptocurrency-related information is being increasingly shared online. As considerable portions of such information in online communities are noise, extracting meaningful information is important. Therefore, judging whose opinion should be considered more important or who the opinion leaders in online communities are is critical. This study analyzed the topics that contain meaningful information, in particular, user groups, by investigating the correlation between topic weights and their price change. The proposed analysis method involves (1) effective classification of the user groups using a hypertext-induced topic selection algorithm, (2) textual information analysis through topic modeling, and (3) the identification of user groups that have a high interest in the Bitcoin price by measuring the correlation between the price and the topics and by measuring the topic similarities between each user group and all users to determine the user group that can effectively represent the entire community. By analyzing the information shared by users, we observed that most users are interested in the price information, whereas users having social influence are not only interested in the price but also in other information.
Identification and Classification of Risks and Potential Events by using Preliminary Hazard Analysis Method (PHA) in Kermanshah Oil Refinery