“…Despite the potential advantages that recommendation algorithms provide to users, they also have the “dark side” ( Salmela-Aro et al, 2017 ; Springer and Whittaker, 2018 ; Ma et al, 2021 ). Previous research has shown that as a structural power that constrains users’ agency ( Reviglio and Agosti, 2020 ; Schwartz and Mahnke, 2021 ), recommendation algorithms pose a range of problems such as visibility hegemony ( Swart, 2021 ), operational opacity ( Kulshrestha et al, 2017 ), bias and discrimination ( Kulshrestha et al, 2017 ), information overload ( Lin et al, 2020 ), disinformation and misinformation ( Clark, 2020 ) and privacy breaches ( Lam and Hsu, 2006 ), which may lead to negative psychological and behavioral responses from platform users, such as social media fatigue ( Bright et al, 2015 ; Dhir et al, 2019 ; Whelan et al, 2020 ; Fan et al, 2021 ; Pang, 2021 ), fear of missing out ( Roberts and David, 2020 ; Tandon et al, 2021 ), and forced disconnection ( Nguyen, 2021 ; Vanden Abeele et al, 2022 ), platform migration ( Maier et al, 2015 ; Luqman et al, 2017 ), etc. Although such researches have explored the operation mechanisms of algorithm from an ontological perspective, focusing on its structuring power (i.e., the structural limitations of data-tracking apps on user information access and platform use) ( Beer, 2017 ; Ford, 2021 ; Morrison, 2021 ; Welch, 2021 ), little attention has been paid to the dynamic processes of how users encounter algorithm and exploit their agency ( DeVito et al, 2017 ; Ettlinger, 2018 ; Klinger and Svensson, 2018 ; Rubel et al, 2020 ; Karizat et al, 2021 ; Velkova and Kaun, 2021 ).…”