A critical analysis of the negative consequences caused by recommender systems used on social media platforms

In a world where social media has a very broad reach and impact on society, it is important to stay alert and keep monitoring content that gets spread so easily. Machine learning algorithms used by social media networks do not always function like they should which can lead to undesirable situations. Social media platforms use recommender systems to personalize content according to the user’s preference and therefore tailor the enormous amount of content available on the internet. This thesis will shed light on the limitations of recommender systems used on social media platform and the possible negative consequences that these systems will bring with them. Recommendations on how to either avoid or deal with these consequences will also be provided.