Driven by advances in digitalization, the volumes of social information have increased beyond comparison. Social networking sites (SNSs) employ personalization algorithms to handle news feeds’ content. This paper conceptualizes SNS feeds as information retrieval systems and adopts the stratified model of relevance – developed in information science discipline for information retrieval systems – to examine user-feed interactions. Thereby, we propose a novel perspective for understanding SNS use. Furthermore, the relevance model guides the interpretation of our qualitative study’s results. Inductive coding of 192 participants’ responses yielded multiple factors influencing users’ relevance judgments. Three parties emerged as responsible for userperceived relevance on SNS: user (through interactions with own feed), personalization algorithm (how it is programmed), and other users (through their content sharing behaviors). Mapping these results on the stratified relevance model, we elaborate on the roles of users and the algorithm. We conclude with implications for research and practice to improve user-feed interactions.
Gladkaya, Margarita and Gundlach, Jana, "A Tale of Relevance in the Feed: Examining SNS Feeds as Social Information Retrieval Systems" (2021). PACIS 2021 Proceedings. 85.
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