Research on the Impact of Social Media Keyword-based Recommendation Methods on Users

Abstract

The purpose of this study is to investigate the keyword-based recommendation strategy of social media and the impact of this recommendation strategy on user attitudes. In this study, three representative content creation social media platforms, Weibo, TikTok, and Bilibili, were selected to investigate their recommendation strategies, and a questionnaire was designed to simulate the content recommendation strategies of social media platforms after users input keywords to investigate users' attitudes towards keyword-based content recommendation strategies in social media. Through investigation, the keyword-based recommendation strategy of social media is mainly to recommend content explicitly associated with keywords. After the user returns to the home page, the system occasionally recommends a small amount of content implicitly associated with keywords. After a given keyword, social media users are more willing to click on content closely related to themselves when facing multiple recommended content. After a series of user choices, a series of entries recommended by the system to most users are explicitly associated with a given keyword. The content recommendation strategy with explicit association is still dominant in social media.