| While personalized recommendation algorithm is becoming an infrastructure element of online news distribution,reshaping users’ news consumption in complicated ways,it is difficult for them to fully understand this technology due to its complexity,opacity and concealment by the platforms.Through the concept of folk theory and 39 semi-structured interviews,this study explores how users perceive and interact with the algorithmic system.The results show that algorithmically recommended content is at the core of users’ news consumption.With local platforms’ straightforward application as endogenous clues,and the public discourses around the buzzword “big data” and the relatively transparent traditional journalism as exogenous references,users develop general algorithmic awareness and folk theories.They perceive algorithm as “recommendation of what I like” due to their entertaining news experience.These folk theories imply users’ critical reflection on how algorithms run on their personal information,serve the commercial interests and affect individuals and society through intervening in journalism.They also imply that users are actively understanding algorithms rather than staying totally ignorant.Users further developed complex altitudes and practices about algorithmic news recommendation.However,these coarse and often inaccurate folk theories have not enabled users of real autonomous practices.On account of algorithms’ inherent complexity,deliberate concealment by the designers,the prevalent technology myths and various sources of legitimacy,people struggle to resist its present operation,which finally leads to a strong sense of cognitive dissonance and action dilemma.In conclusion,users’ current folk theories of algorithmic news recommendation are not sufficient to empower themselves.To improve this situation,this study develops a framework for cultivating algorithmic literacy towards action in hope of deepening folk theories and enhancing autonomy. |
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