  Recent works have shown that social media platforms are able to influence the
trends of stock price movements. However, existing works have majorly focused
on the U.S. stock market and lacked attention to certain emerging countries
such as China, where retail investors dominate the market. In this regard, as
retail investors are prone to be influenced by news or other social media,
psychological and behavioral features extracted from social media platforms are
thought to well predict stock price movements in the China's market. Recent
advances in the investor social network in China enables the extraction of such
features from web-scale data. In this paper, on the basis of tweets from
Xueqiu, a popular Chinese Twitter-like social platform specialized for
investors, we analyze features with regard to collective sentiment and
perception on stock relatedness and predict stock price movements by employing
nonlinear models. The features of interest prove to be effective in our
experiments.
