Information extracted from social media streams has been leveraged to forecast the outcome of a large number of real-world events, from political elections to stock market fluctuations. An increasing amount of studies demonstrates how the analysis of social media conversations provides cheap access to the wisdom of the crowd. However, extents and contexts in which such forecasting power can be effectively leveraged are still unverified at least in a systematic way. It is also unclear how social-media-based predictions compare to those based on alternative information sources. To address these issues, here we develop a machine learning framework that leverages social media streams to automatically identify and predict the outcomes of soccer matches. We focus in particular on matches in which at least one of the possible outcomes is deemed as highly unlikely by professional bookmakers. We argue that sport events offer a systematic approach for testing the predictive power of social media conversations, and allow to compare such power against the rigorous baselines set by external sources. Despite such strict baselines, our framework yields above 8 marginal profit when used to inform simple betting strategies. The system is based on real-time sentiment analysis and exploits data collected immediately before the game start, allowing for bets informed by its predictions. We first discuss the rationale behind our approach, then describe the learning framework, its prediction performance and the return it provides as compared to a set of betting strategies. To test our framework we use both historical Twitter data from the 2014 FIFA World Cup games (10 sample), and real-time Twitter data (full stream) collected by monitoring the conversations about all soccer matches of the four major European tournaments (FA Premier League, Serie A, La Liga, and Bundesliga), and the 2014 UEFA Champions League, during the period between October, 25th 2014 and November, 26th 2014.
To prove the idea that social media data convey predictive power, Asur and Huberman @cite_3 designed a system that uses Twitter to forecast the box-office revenues of upcoming movies: simple signals such as the buzz around a given movie seem indicative of its future popularity. DiGrazia et al @cite_18 used a similar framework to show that there exists a statistically significant association between tweets that mention a political candidate for the U.S. House of Representatives and his or her subsequent electoral performance. Bermingham and Smeaton @cite_2 illustrated a similar case study for the recent Irish General Election, modeling political sentiment by mining social media conversations. They combined sentiment analysis using supervised learning and volume-based measures and found that this signals are highly predictive of election results. Bollen et al @cite_6 analyzed the textual content of the daily Twitter stream to show that Twitter mood is predictive of the daily fluctuations in the closing values of the Dow Jones Industrial Average (DJIA). Xue Zhang et al @cite_24 collected Twitter data for six months and found that the percentage of emotional tweets significantly negatively correlates with Dow Jones, NASDAQ and S &P 500 fluctuations, but displays a significant positive correlation to VIX.
@cite_18 @cite_6 @cite_3 @cite_24 @cite_2
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Large-scale data from social media have a significant potential to describe complex phenomena in the real world and to anticipate collective behaviors such as information spreading and social trends. One specific case of study is represented by the collective attention to the action of political parties. Not surprisingly, researchers and stakeholders tried to correlate parties' presence on social media with their performances in elections. Despite the many efforts, results are still inconclusive since this kind of data is often very noisy and significant signals could be covered by (largely unknown) statistical fluctuations. In this paper we consider the number of tweets (tweet volume) of a party as a proxy of collective attention to the party, identify the dynamics of the volume, and show that this quantity has some information on the election outcome. We find that the distribution of the tweet volume for each party follows a log-normal distribution with a positive autocorrelation of the volume over short terms, which indicates the volume has large fluctuations of the log-normal distribution yet with a short-term tendency. Furthermore, by measuring the ratio of two consecutive daily tweet volumes, we find that the evolution of the daily volume of a party can be described by means of a geometric Brownian motion (i.e., the logarithm of the volume moves randomly with a trend). Finally, we determine the optimal period of averaging tweet volume for reducing fluctuations and extracting short-term tendencies. We conclude that the tweet volume is a good indicator of parties' success in the elections when considered over an optimal time window. Our study identifies the statistical nature of collective attention to political issues and sheds light on how to model the dynamics of collective attention in social media. The body of content available on Twitter undoubtedly contains a diverse range of political insight and commentary. But, to what extent is this representative of an electorate? Can we model political sentiment effectively enough to capture the voting intentions of a nation during an election capaign? We use the recent Irish General Election as a case study for investigating the potential to model political sentiment through mining of social media. Our approach combines sentiment analysis using supervised learning and volume-based measures. We evaluate against the conventional election polls and the final election result. We find that social analytics using both volume-based measures and sentiment analysis are predictive and wemake a number of observations related to the task of monitoring public sentiment during an election campaign, including examining a variety of sample sizes, time periods as well as methods for qualitatively exploring the underlying content. Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, Twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on Twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from Twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from Twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets. Behavioral economics tells us that emotions can profoundly affect individual behavior and decision-making. Does this also apply to societies at large, i.e. can societies experience mood states that affect their collective decision making? By extension is the public mood correlated or even predictive of economic indicators? Here we investigate whether measurements of collective mood states derived from large-scale Twitter feeds are correlated to the value of the Dow Jones Industrial Average (DJIA) over time. We analyze the text content of daily Twitter feeds by two mood tracking tools, namely OpinionFinder that measures positive vs. negative mood and Google-Profile of Mood States (GPOMS) that measures mood in terms of 6 dimensions (Calm, Alert, Sure, Vital, Kind, and Happy). We cross-validate the resulting mood time series by comparing their ability to detect the public's response to the presidential election and Thanksgiving day in 2008. A Granger causality analysis and a Self-Organizing Fuzzy Neural Network are then used to investigate the hypothesis that public mood states, as measured by the OpinionFinder and GPOMS mood time series, are predictive of changes in DJIA closing values. Our results indicate that the accuracy of DJIA predictions can be significantly improved by the inclusion of specific public mood dimensions but not others. We find an accuracy of 87.6 in predicting the daily up and down changes in the closing values of the DJIA and a reduction of the Mean Average Percentage Error by more than 6 . Index Terms—stock market prediction — twitter — mood analysis.