The value of mood measurement for regulating negative influences of social media usage: A case study of TikTok

Abstract:

Social media usage has greatly increased during the pandemic. The use of social media has been linked to mental health consequences. Especially TikTok is under scrutiny because of its captivating UX Design as well as controversial privacy policies. This study shows how Mobile Affective Computing Research can reliably and objectively measure the implicit-emotional UX. The results suggest that TikTok triggers positive emotions during onboarding and strengthens the emotional bond with increasing usage – mainly due to the personalization algorithm. In particular, the study further reveals that there are no clear relationships when “only self-reported data” is used to understand app usage behavior. However, emotion data based on facial coding shows that TikTok users get emotionally aroused. Initially, a positive and relaxed mood triggers further usage time, while – counter-intuitively – later sessions result in an increasingly negative and aroused mood. However, people are largely unaware of these relationships when questioned. Thus, implicit measures, such as facial coding, may help identify valid social media usage reactions. These results yield implications that call for political regulation considering aspects of social media apps and user behavior.