This episode discusses X's recommendation algorithm, which determines the content users see in their timelines. We analyze how Twitter uses data like user interactions, social connections, location, and even phone contacts to personalize recommendations. It relies heavily on graph mining to represent the virtual community as a mirror of the physical world, emphasizing local and popular accounts. There are concerns about information cocoons, where users might be trapped in a bubble of their own predicted interests, potentially limiting exposure to diverse viewpoints. Notably, we further explore how Twitter aims to balance content regulation with freedom of speech, using machine learning to filter inappropriate content while acknowledging potential biases in the training data. Ultimately, the episode praises X's transparency in open-sourcing the algorithm, emphasizing its value for learning about computer architecture and the complex inner workings of social media platforms.

My Blog: https://medium.com/@shiyinw/twitters-recommendation-algorithm-is-now-open-source-what-does-it-tell-us-2d964f79f4e0