Monetisasi Empati Dalam Live Streaming TikTok: Analisis Hukum Ekonomi Islam - Robiansyah, Firman; Fitri, Ghaida Nuraliza; Pramudya, Muhamad Daffa; Putri, Vica Permata (Jurnal Penelitian Ekonomi Akuntansi, 2025)
The aims of this study is to analyze the phenomenon of empathy monetization in TikTok live streaming from the perspective of Islamic economic law, particularly within the Maqashid Shariah framework. Qualitative research used as a research method with a literature study approach. Data and information were collected from various literature sources, such as scientific journal articles and other related documents discussing Islamic economic law, Maqashid Shariah, and live streaming phenomena. The results show that empathy monetization in TikTok live streaming involves the practice of buying and selling emotional support, where users provide virtual donations in exchange for attention and empathy given by streamers. From the perspective of Islamic economic law, this practice contains elements of gharar and maysir, as there is no guarantee that the empathy provided by streamers is proportional to the donations given by users. Furthermore, viewed from Maqashid Shariah, this practice potentially contradicts the objectives of hifz al-nafs and hifz al-mal, as it can lead to emotional exploitation and commercialization of human values that degrade human dignity. The conclusion of this research is that empathy monetization in TikTok live streaming is inconsistent with the principles of Islamic economic law and contradicts Maqashid Shariah. The implications of these findings include the need for clearer regulations regarding monetization practices on social media platforms, as well as education for the public about ethics and boundaries in social media interactions. This study has limitations because it is qualitative in nature, and thus the authors did not conduct direct and in-depth observations of the TikTok phenomenon. We strongly hope that future researchers will conduct studies with direct observation to obtain more relevant data.