Prediction in social networks attracts more and more attentions since social networks have become an important part of people's lives. Although a few topic or event prediction models have been proposed in the past few years, researches that focus on the single tweet prediction just emerge recently. In this paper, we propose STH-Bass, a Spatial and Temporal Heterogeneous Bass model derived from economic field, to predict the popularity of a single tweet. Leveraging only the first day's information after a tweet is posted, STH-Bass can not only predict the trend of a tweet with favorite count and retweet count, but also classify whether the tweet will be popular in the future. We perform extensive experiments to evaluate the efficiency and accuracy of STH-Bass based on real-world Twitter data. The evaluation results show that STH-Bass obtains much less APE than the baselines when predicting the trend of a single tweet, and an average of 24% higher precision when classifying the tweets popularity.
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