Abstract: A machine-learned news aggregation system provides interfaces displaying trending topics and associated content items across a plurality of news sources and geographies. In one embodiment, the news aggregation system mines content from multiple news sources, in each of multiple geographies, using a crawling engine and accesses a plurality of social networking platforms to identify user sentiment data associated with mined content. A hybrid supervised and unsupervised machine-learned model is used to identify keywords and characteristics of the mined content, and a second hybrid unsupervised and reinforcement learning model is applied to the keywords to generate clusters of information.