Abstract: With the rapid development of the Internet, the government, enterprises and public have paid more and more attentions on net mediated public sentiment. How to effectively monitor and aright guide the public sentiment on the Internet has become an issue that should be coped urgently with. As a basis to solving the issue, it is necessary to have ability of predicting topic's hotness appearing on the Internet As traditional algorithms could not predict aright new topie's hotness,a novel algorithm based on K-nearest neighbors(K-NN) was proposed in this paper. The algorithm prediets the hotness of new topics by using hotness times series of their historical similar topics. The experimental results show that the average accuracies of the hotness prediction during the first 3 days arc 47. 26 0 0，61 0 0 and 67. 7 0 0 rcspcclively with the corresponding relative errors being less than 10 0 o，20"o and 30"0，and the average accuracy of the hotness trends within the first 3 days could be up to 73. 73 0 o. Meanwhile, the results also demonstrate that similar topics approximately have same hotness trends in their early developing stages.

Key words: Hotness prediction, New topic, KNN, Topic similarity, Net mediated public sentiment