Two major communication theories provide the foundation for analyzing entertainment’s impact.
2.1 Cultivation Theory (George Gerbner, 1976) Gerbner argued that heavy television viewing “cultivates” perceptions of reality that align with the fictional world portrayed on screen. For example, viewers who consume excessive amounts of crime procedurals tend to overestimate the prevalence of violence in the real world. In the streaming era, this theory extends to binge-watching, where immersive narrative worlds (e.g., Stranger Things, Squid Game) disproportionately shape young adults’ risk assessment and social expectations. Hegre-Art.14.08.16.Marcelina.First.Session.XXX.... -HOT
2.2 Agenda-Setting and Framing (McCombs & Shaw, 1972) While traditionally applied to news, agenda-setting also operates in entertainment. Popular media does not tell audiences what to think, but what to think about. A Netflix documentary series like Tiger King temporarily elevates exotic animal welfare into public discourse; a hit show like Succession frames wealth, family dysfunction, and corporate ethics in a specific, dramatized light. Two major communication theories provide the foundation for
In 2023, global consumers spent an average of over 450 minutes per day engaging with digital media, with entertainment content (streaming video, social media, gaming, and music) accounting for the vast majority of that time (DataReportal, 2024). Entertainment is no longer a peripheral distraction but a central pillar of everyday life. Historically dismissed as “low culture” by critical theorists like Theodor Adorno, popular media now demands serious academic inquiry due to its unprecedented scale, personalization, and narrative power. This paper investigates a central question: How does entertainment content function as both a reflection of existing social realities and an active agent in reshaping them? In the streaming era, this theory extends to
Entertainment content is acutely sensitive to the cultural moment.
The transition from broadcast to streaming (Netflix, TikTok, YouTube) has intensified the mirror/molder dynamic. Recommendation algorithms analyze user behavior to serve hyper-targeted entertainment content. This creates a feedback loop: the algorithm mirrors a user’s past preferences, then molds future tastes by narrowing exposure diversity. For example, a viewer who watches one true-crime documentary will be fed dozens more, cultivating a worldview where violent crime is ubiquitous and justice is always cinematic. This algorithmic curation blurs the line between passive reflection and active construction.