The Impact of Recommendation Algorithms in Social Media on Political Opinion Formation in Morocco

Social media plays a major role in our everyday lives and has an impact both on what we think about and on how we think about it. The information we are exposed to, be it on Facebook, Instagram, or TikTok, is tailored to our interests, making us attached to our screens for long periods of time. This interests-based model of scrolling feeds is referred to as a ‘for you’ page or recommended content that are shaped through recommendation algorithms. Recommendation algorithms can be defined as the ‘black boxes’ of social media networks and the main reason why they prove so addictive. Technically, they are based on a machine learning concept called a ‘recommender system’ which uses data to predict, narrow down, and suggest more content (or products) to consumers. Recommendations can range from products or services to news or political content. This chapter study explores how recommendation algorithms shape political beliefs on social media, using Morocco as a context, and their impact on the perception and sharing of political information. Morocco was chosen as a suitable case study given the spike of recommendation algorithms since the last elections in 2021, when political parties used social media to increase their reach and impact voter turnout. The research explores four key aspects using a mixed-methods approach: public opinion, the flux of political information, the understanding of recommendation algorithms, and their impact on the social and political context in the country. The study on Morocco provides key insights into the challenges of algorithmic influence on political discourse and offers recommendations for the ethical regulation of algorithms to safeguard the integrity of political information.