Abstract
This study analyzes the response of Netflix to public opinion regarding its strategy of creating original content as it is manifested in micro-blogging tweets. This strategy has been expanded significantly over the years, leading Netflix to 129 original shows until the end of 2020. In the absence of conventional media measures, public opinion about the shows has played a crucial role in online platforms’ decision making in starting, extending, or cancelling shows. Unlike traditional TV, SVOD (subscription video-on-demand) platforms can precisely specify users’ favourite contents in any given categories, predict more precisely people’s preference, and suggest contents. These trends are manifested by popular hashtags like #MeToo often appearing in Twitterstorms. The data which they offer might be used to understand what users expect from future productions, influencing the decisions made by platforms such as Netflix. Therefore, by looking at both trends and latter productions, it can be traced if the platforms were successful in addressing the concerns reflected in those trends. In this proposed presentation, we will look at major social and occurrences over the past decade (2010-2020) such as Harvey Weinstein’s scandal and Trump winning the presidential election, examine Twitterstorms related to them and then relate these to the offerings of Netflix in terms of original series produced before and after these occurrences. As for the Twitterstorms, the data is extracted using data mining, and the keywords are analyzed to check whether there is a meaningful connection. The data is content analyzed quantitatively and visually presented.
Details
Presentation Type
Theme
KEYWORDS
SVOD, Netflix, Data-mining, Social Media, Twitterstorms
