Algorithms currently have direct implications in our democracies and societies, but they also define mostly all our daily activities as users, defining our decisions and promoting different behaviors. In this context, it is necessary to define and think about how to design the different implications that these algorithms have from a user centered perspective, particularly in social media platforms that have such relevance in our information sources and flow. Therefore, the current thesis provides an introduction to the concept of algorithmic experience, trying to study how to implement it for social media services in cellphone devices. Using a Research through Design methodology supported by interface analysis, document analysis and user design workshops, the present paper provides results grouped in five different areas: algorithmic profiling transparency, algorithmic profiling management, algorithmic awareness, algorithmic user-control and selective algorithmic remembering. These five areas provide a framework capable of promoting requirements and guide the evaluation of algorithmic experience in social media contexts.

After decades of black-boxing the existence of algorithms in technologies of daily need, users lack confidence in handling them. This thesis study investigates the use situation of intelligent music recommendation systems and explores how understandability as a principle drawn from sociology, design, and computing can enhance the algorithmic experience. In a Research-Through-Design approach, the project conducted focus user sessions and an expert interview to explore first-hand insights. The analysis showed that users had limited mental models so far but brought curiosity to learn. Explorative prototyping revealed that explanations could improve the algorithmic experience in music recommendation systems. Users could comprehend information the best when it was easy to access and digest, directly related to user behavior, and gave control to correct the algorithm. Concluding, trusting users with more transparent handling of algorithmic workings might make authentic recommendations from intelligent systems applicable in the long run.