Abstract: Weibo, the most widely-used social media in China, makes researchers highly regard its profound impact in public and gather moods for social computing and analysis, such as financial prediction. Most existing literatures concern excessively on text semantic or sentiment mining techniques, but neglect the procedure of moods dissemination and its factors. This paper proposes an integrated framework of social media moods mining, which creatively focuses on information transmission and propagating factors analysis, to predict stock prices more accurately. For the part of propagating factors on social media, several essential factors are distinguished in the dissemination process, such as emotional absorption of forwarding, influence of content and poster, user categories, release time, etc. to optimize the fitting effect of original model. And the count of forwarding also matters on predicting stock prices. Searching a given finance-related keyword, from Weibo we collected over 500,000 micro-blogs and their user information. Then we adopt the proposed integrated framework to predict stock price fluctuation, as well as the simple neural network method. Experiments demonstrate that the former outperformed the latter. The results also show that user categories and the count of forwarding differ on the lag phase of influence. And more, this paper studies the fitting effect of prediction models for different periods of the stock curve. The results indicate that the model works the best in the rising periods of stock prices curves, relatively well in the declining and the worst in the random fluctuating.
