Toxicity and Social Network Analysis of Green Marketing Content for Electric Cars through Digital Media - Singgalen, Yerik Afrianto (Institute of Computer Science (IOCS), 2024)
This study aims to investigate the effectiveness of green marketing strategies in influencing consumer interest and purchasing behavior towards electric cars, focusing on media coverage, as exemplified by BBC News. Specifically, it seeks to understand how media portrayals of electric cars through green marketing narratives impact consumer perceptions and preferences in the context of sustainability. The research adopts the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology. The study culminates in noteworthy findings obtained through Toxicity Analysis and Social Network Analysis (SNA). Toxicity Analysis yielded specific numerical values across categories: Toxicity (0.05645, 0.99613), Severe Toxicity (0.00002, 0.00333), Identity Attack (0.00211, 0.35185), Insult (0.03630, 0.99520), Profanity (0.01584, 0.93590), and Threat (0.00279, 0.43515). These metrics signify varying levels of negative sentiment and potentially harmful language within the examined dataset. Concurrently, SNA provided structural insights with a diameter of 6, low density (0.009484), negligible reciprocity (0.000000), modest centralization (0.038160), and high modularity (0.872000). While the network exhibits centralized influence and limited reciprocity, the high modularity suggests distinct communities or clusters. These findings underscore the importance of considering sentiment dynamics and network structure, emphasizing the need for targeted interventions to mitigate toxicity and cultivate healthier communication environments.