Musikstreamingtjänster som Spotify erbjuder en omfattande mängd musik online jämfört med innan digitaliseringen. Trots detta förändrade musiklandskap, kvarstår fortfarande önskan att samla, byta ut och dela musik. Däremot har musikstreamingtjänster medfört ett ökat behov av att navigera användarna genom de omfattande låtarkiven. Spotifys spellista Discover Weekly syftar till att tillhandahålla användarna med 30 nya låtar varje måndag. Låtarna är personanpassade utifrån användarnas smakprofiler, som är kalibrerade av rekommendationsalgoritmer. Musikstreamingtjänsten konstaterar att de vill erbjuda en explorativ användarupplevelse med hjälp av rekommendationsalgoritmerna. I denna studie valde vi att utvärdera hur användarna själva upplever de personanpassade låtarna på Discover Weekly. Tidigare studier om Spotify, musikupptäckter i en digital era, rekommendationsalgoritmer, och effekterna av algoritmer ansågs vara relevanta för att utforska hur låtarna på Discover Weekly upplevs vara. Frågeställningen undersöktes genom inledande intervjuer, en veckas lyssningsperiod av Discover Weekly samt uppföljande intervjuer med en testgrupp bestående av studenter från KTH. Resultatet från intervjuerna visade att låtarna på spellistan upplevdes vara ensidiga där användarna gillade låtarna som stack ut från mängden i stil av tempo eller genre. Majoriteten av testpersonerna uppskattar ändå rekommendationsalgoritmen. Flera testpersoner förklarade att de önskade mer explorativa alternativ på spellistan för att stödja det givna löftet om utforskande av nya musikupptäckter vilket namnet, Discover Weekly, indikerar på. Deltagarna ansåg dock fortfarande att icke-datordrivna rekommendationer var att föredra och mer pålitliga. Vårt resultat kan därmed jämföras med tidigare studiers teorier där algoritmer / Music streaming services like Spotify provide a staggering amount of music online compared to before digitalization. Even though the music landscape is changing, the desire to collect, exchange, and share music remains. However, music streaming services have contributed to an increased desire to navigate users through the extensive song archives. Spotify's Discover Weekly playlist aims to provide users with 30 new songs every Monday. The songs are personalized based on the users' taste profiles, which are calibrated by recommendation algorithms. The music streaming service states that they want to offer an exploratory user experience using the recommendation algorithms. In this study, we evaluate how users themselves experience the personalized songs on Discover Weekly. Previous related research regarding Spotify, music discovery in a digital era, recommendation algorithms, and algorithmic effects were considered relevant when exploring how the songs on Discover Weekly are experienced to be. The thesis was investigated through introductory interviews, one week listening of Discover Weekly, and follow-up interviews with a test group consisting of students from KTH Royal Institute of Technology. The results from the interviews showed that the songs on the playlist were perceived as indistinguishable, where the users liked the songs that stood out from the rest in tempo or genre. The majority of the participants still appreciate the recommendation algorithm. Several participants explained that they wished for more exploratory options on the playlist to support the promise of exploring new music discoveries as the name Discover Weekly indicates. However, the participants still considered user recommendations as more reliable than algorithmically driven recommendations. Our findings support previous researchers’ insights where these algorithms can affect the diversification of consumption on Spotify.

The significant growth of the social media platform TikTok over the past years has led to trends being evoked on the platform. One trend, of which this study explores, is content related to ideals of masculinity that includes how men can change themself to become successful and achieve wealth. This study “Boys focus on girls, men focus on money – A qualitative analysis of masculinity ideals on TikTok” aims to disclose how TikTok’s recommendation algorithm contributes to create and maintain ideals of masculinity on the platform, as well to identify discourses within the content of this ideal to gain an understanding on how they may affect gender norms from a societal aspect. This study finds that TikTok’s recommendation algorithm is able to create and maintain an ideal of masculinity by recommending content to the user that is sorted by patterns within the type content of which the user engages with. Three identified patterns that relate to content within the ideal of masculinity that we explore on TikTok are wealth, lifestyle change and motivation as well as world view. These three identified patterns conclude norms within these ideals of which we further research to enlighten the contents discourses. The analysis of these norms from a discursive perspective found that the ideal of masculinity on TikTok is established on men being expected to achieve institutional power and authority. This is problematic from the perspective of male supremacy and female subordination in an historical context of gender norms.