# Identification of Opinion Leaders and Followers--A Case Study of Green Energy and Low Carbons
## 4 Case Study  
The ABC network platform is taken as an example to describe the application of our research in practice. The ABC network platform is a discussion platform created by the government to promote community communication. This platform was created as part of a public policy proposal to improve policy communication and make policy public. This case study is taken from the National Energy Conference organized by the Energy Bureau of Taiwan's Ministry of Economic Affairs. However, there are still many disagreements when it comes to choosing opinions due to value divergence. To discuss and clarify issues with the public, the proposition, \"Where does future electric power come from?\" is open on the policy consultant forum (People Talk), with three sub-issue forums including, 'environment low carbon sustainable development','stable supply and open source' and'reduce expenditures effectively'. In particular,'stable supply and open source' is the focus of this case study. The proposed solution approach is applied in this case. Step I. Collect materials: Judging by the forum (posts, fan pages) on social media about green energy and low carbon, we collected materials, including text and response information. This study collected materials from users' discussion contents related to the \"stable supply\" issue on the ABC network platform between May 2019 and the end of 2019. The data collection is implemented with the Python-Jieba crawler program, which is particularly suitable for Chinese text analysis automatically. The collected materials are listed below: Post users: 36; total posts: 205 (total posts have been deducted from the number of administrator responses and consecutive posts); effective responses: 61; effective count of being responded to 47. Step II. Input matrix elements: Input elements in matrix \(T\). The elements include general posts, counts of responses and being responded to as judged by experts. Step III. Remove meaningless users: Remove users whose posts are never responded to. Step IV. Tag the user's category: Input social community support  
Step V. Influence analysis: We analyze a user's influence by comparing the levels of influence power according to three characteristics and input the influence into the matrix \(I\). Step VI. If the count of users' posts, responses, and influence power, reaches a certain level of relevance, then move forward. If the users have greater counts of responses or respondents, list them in front. (Figure 1). Step VII. According to matrix \(I\) and \(T\) based on Equation (1), group the users by their relationships. Group A [1, 2, 3, 4, 5, 6, 18, 20, 21, 24]; Group B [2, 16, 24, 26, 27, 34, 36]; Group C [17, 18, 19]; Group D [19, 20, 21, 36]; Group E [2, 27, 28, 29, 30, 36]; Group F [7, 20, 26]; Group G [2, 9, 27, 31, 34]; Group H [7, 27]; Group I [7, 8, 15]; Group J [21, 22, 23]; Group K [25, 26]; Group L [34, 35]. Step VIII. Identification: Determine the interaction between users, followers, and opinion leaders, according to the definition of each group of opinion leaders. In Group A, opinion leader 1 is recognized by Axiom 1, and the influencers are Users 3 and 5. This group is identified as an argument group based on Axiom 5. In Group B, User 16's post contents are usually meaningless. According to Axiom 1, User 16 is not an opinion leader. Moreover, the group does not belong to any pattern. Group C is an argument pattern. Users 18 and 19 are influencers. In this pattern, no opinion leaders and followers are identified. The influencers influence each other without focusing on any particular key person. In group D, the response of User 19 has a great influence on Users 20 and 21. However, the influence of the posts responded to by Users 20 and 21 is not great. There is a discussion relationship between Users 19 and 20, so it is a social pattern. In Group E, the post contents of User 36 are valuable. However, other users' responses are not good. Thus, User 36 is an opposing opinion leader. Figure 1: Matrix element conversion.