TikTok’s user suggestion feature, often populated with individuals recognized from one’s broader social circles, operates through an analysis of various data points. These data include phone contacts, connections across other social media platforms if linked to the account, location data (with user permission), and mutual connections on the TikTok platform itself. The algorithm correlates these data points to identify potential acquaintances and suggests them as accounts to follow, thus expanding the user’s network and content discovery pool. For example, if a user’s phone contacts contain several individuals also on TikTok, and those individuals are connected to the user’s friends, the algorithm may propose these contacts as potential follow suggestions.
This system enhances the user experience by facilitating connections with individuals known offline, leading to more engaging content and a greater sense of community within the application. It streamlines the process of finding and connecting with individuals the user already knows, enriching the platform’s social aspect. The historical context involves the evolution of social media algorithms that prioritize connections within established social circles to promote user retention and active participation.
