Twitter as a new form of social media potentially contains useful information that opens new opportunities for content analysis on tweets. This paper examines the predictive power of Twitter regarding the US presidential election of 2012. For this study, we analyzed 32 million tweets regarding the US presidential election by employing a combination of machine learning techniques. We devised an advanced classifier for sentiment analysis in order to increase the accuracy of Twitter content analysis. We carried out our analysis by comparing Twitter results with traditional opinion polls. In addition, we used the Latent Dirichlet Allocation model to extract the underlying topical structure from the selected tweets. Our results show that we can determine the popularity of candidates by running sentiment analysis. We can also uncover candidates popularities in the US states by running the sentiment analysis algorithm on geo-tagged tweets. To the best of our knowledge, no previous work in the field has presented a systematic analysis of a considerable number of tweets employing a combination of analysis techniques by which we conducted this study. Thus, our results aptly suggest that Twitter as a well-known social medium is a valid source in predicting future events such as elections. This implies that understanding public opinions and trends via social media in turn allows us to propose a cost- and time-effective way not only for spreading and sharing information, but also for predicting future events.
Surprisingly, based on the analysis of 50 million tweets they collected, proved that even noisy information in social media such as Twitter can be used as a proxy for public opinions @cite_33 . They found that Twitter data provides more than factual information about public opinions on a specific topic, yielding better results than information from the prediction market. For instance, Twitter data can be used as social sensors for real-time events and to forecast the box-office revenues for movies @cite_13 . The rate at which tweets posted about a particular movie has a strong positive correlation with the box-office gross. In addition, Twitter as a platform can reflect offline political sentiment validly @cite_7 , and it can mirror consumer confidence. Interestingly, even a mere number of Twitter data dominated by a small number of heavy users predicted an election result, such as the results of the 2011 Irish general election @cite_30 , and even came close to traditional election polls @cite_7 . This aptly suggests that social media data, especially Twitter, can replace the costly- and time-intensive polling methods @cite_14 . Thus, we argue that social media can be used as a credible source for predicting the near future.
@cite_30 @cite_14 @cite_33 @cite_7 @cite_13
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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. In recent years several researchers have reported that the volume of Google Trends and Twitter chat over time can be used to predict several kinds of social and consumer metrics. From the success of new movies before their release to the marketability of consumer goods to the prediction of voting results in the recent 2009 German elections, Google Trends and Twitter message volume have been treated as indispensable tools for not only recording current social trends, but even for predicting the future. This is surprising, given the significant differences in the demographics of voters and people who use social networks and Web tools. But is there some underline logic behind these predictions or are they simply a matter of luck? With this work we wanted to test their predictive power against the US elections. One could argue that, following the previous research literature, and given the high utilization that the Web and the social networks have in the US, Google Trends and Twitter volume may be able to predict the outcomes of the US Congressional elections. In this paper we report that Google Trends was, actually, not a good predictor of both the 2008 and 2010 elections, and we offer some explanation on why this may be the case. On a forthcoming paper we report on our analysis on Twitter. We present a contribution to the debate on the predictability of social events using big data analytics. We focus on the elimination of contestants in the American Idol TV shows as an example of a well defined electoral phenomenon that each week draws millions of votes in the USA. This event can be considered as basic test in a simplified environment to assess the predictive power of Twitter signals. We provide evidence that Twitter activity during the time span defined by the TV show airing and the voting period following it correlates with the contestants ranking and allows the anticipation of the voting outcome. Twitter data from the show and the voting period of the season finale have been analyzed to attempt the winner prediction ahead of the airing of the official result. We also show that the fraction of tweets that contain geolocation information allows us to map the fanbase of each contestant, both within the US and abroad, showing that strong regional polarizations occur. The geolocalized data are crucial for the correct prediction of the final outcome of the show, pointing out the importance of considering information beyond the aggregated Twitter signal. Although American Idol voting is just a minimal and simplified version of complex societ al phenomena such as political elections, this work shows that the volume of information available in online systems permits the real time gathering of quantitative indicators that may be able to anticipate the future unfolding of opinion formation events. The widespread use of online social networks (OSNs) to disseminate information and exchange opinions, by the general public, news media, and political actors alike, has enabled new avenues of research in computational political science. In this paper, we study the problem of quantifying and inferring the political leaning of Twitter users. We formulate political leaning inference as a convex optimization problem that incorporates two ideas: (a) users are consistent in their actions of tweeting and retweeting about political issues, and (b) similar users tend to be retweeted by similar audience. We then apply our inference technique to 119 million election-related tweets collected in seven months during the 2012 U.S. presidential election campaign. On a set of frequently retweeted sources, our technique achieves 94 percent accuracy and high rank correlation as compared with manually created labels. By studying the political leaning of 1,000 frequently retweeted sources, 232,000 ordinary users who retweeted them, and the hashtags used by these sources, our quantitative study sheds light on the political demographics of the Twitter population, and the temporal dynamics of political polarization as events unfold.