Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy

• Published in: Sustainability 2020-10, Vol.12 (19), p.8057
• Main Authors: Huang, Chun-Che, Liang, Wen-Yau, Lin, Shian-Hua, Tseng, Tzu-Liang (Bill), Wang, Yu-Hsien, Wu, Kuo-Hsin
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Big Data; Clean energy; Computer applications; Conflicts of interest; Digital media; Energy; Historical account; Internet; Motivation; Popularity; Public opinion; Social interactions; Social networks; Social organization; Social research; Social sustainability; Strategic planning; Sustainability; Sustainable development; User generated content
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su12198057

More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment.