In a collection of documents, such as news articles or tweets, various events take place over time. The event detection problem aims at discovering significant events that have not been mentioned before the detection time. When these events occur, we observe that topic distributions of documents will diverge notably. However, event detection from such divergence may be hampered by noises. In this paper, we propose TopicDiver, a novel method to address the event detection problem. TopicDiver models topic distributions of documents over time and filters out noises while capturing the useful divergence between such distributions. The direct exploitation of topic distribution over time sets our work apart from existing studies on event detection. We conduct comprehensive experiments under different settings on news and Twitter data. The experimental results demonstrate that TopicDiver outperforms the baseline models in the measures for accuracy across various settings.