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Because the use of social networks is booming, data from social networks offer valuable insights into what people think and want. Thus, these data have become more and more popular for collecting opinions and for forecasting. Stephens-Davidowitz [1] studied the relation between the voting of American presidential election and racially charged language. The author pointed out that the Google search queries were more useful than the survey data when social censoring issues were investigated. The results showed that there was a relation between voting and the search queries of racial animus. Gunn III and Lester [5] employed Google Trends with three terms to analyze the relation between the three terms and monthly suicide rates. They reported that the information from the Internet search is correlated with the number of suicides, and thus, it is a faster way of monitoring possible suicide trends than compiling suicide statistics. Yang et al. [14] analyzed the relation between Internet search trends and suicide death. The conclusions revealed that suicide-related search terms were related to suicide death, and thus, keyword-driven search results of the Internet are the essential knowledge to reduce suicide deaths. Frijters et al. [4] conducted a study about the relationship between macroeconomic conditions and an indicator of problem drinking data from Google searches. The results showed that the macroeconomic conditions are associated with health in some ways, and the real-time data provided by Google searches are crucial information for policy-makers. Smith [15] investigated the volatility in forecasting foreign currency exchange rates by using three Google search keywords and time-series models. The results demonstrated that the information from Google searches is important in forecasting the market for foreign currency. Fondeur and Karamé [16] used the Google search data to enhance the prediction accuracy of youth unemployment in France. The results indicated that Google search data did improve the prediction of unemployment. Li et al. [17] used both statistical data and Google search data to predict the consumer price index by a mixed-data sampling model. Numerical results revealed that the proposed approach was helpful in forecasting the consumer price index by using data from the user-generated content. Takeda and Wakao [18] studied the relation between the Google search intensity, stock trading volume, and stock prices. It was reported that the positive relationship between Google search intensity and trading volume is stronger than that between Google search intensity and stock prices. Araz et al. [2] used Google Flu Trends data to forecast influenza-like illness, and a strong positive relation between Google Flu Trends data and influenza-like illness was revealed. In addition, using Google Flu Trends data as independent variables can result in accurate forecasting results. Some studies have examined the relation between the Internet search and some diseases, such as disease-related genes [19], kidney stones [20, 21], epilepsy [3, 22], allergy [23], and restless legs [24].

In our poster, we treat daily stock market data as images and apply a convolutional neural network (CNN), which is very powerful at image processing, to the market images in order to predict future stock price changes.

The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. 2b1af7f3a8

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