Analysis of Online Mass Communication on 'Evolution'
Keywords:
evolution, public understanding, network analysis, machine learning, automated classificationAbstract
Evolution is a central concept that unifies all areas of the life sciences. Despite longstanding scientific efforts in science education, the public’s scientific awareness of evolution still needs to improve. Furthermore, the teaching of evolution is subject to recurring controversy. This study aimed to identify the gap between the content covered in the curriculum and the public’s understanding of evolution and explore the reasons for this gap. A content analysis using data mining on a major online portal’s knowledge search service was also conducted to determine how the publicly exchanged content on evolution differs from scientific knowledge. The characteristics of creating and consuming content on evolution through the online portal service based on analyzing the number of posts related to biological evolution and the number of active participants were also examined. Finally, based on the data collected, the feasibility of automatic document classification to distinguish between scientific understanding and non-scientific beliefs on the evolution of life and related content circulating on the Internet is also discussed.