This is a report, where preliminary work regarding the topic of voting intention inference from Social Media - such as Twitter - is presented. Our case study is the UK 2010 General Election and we are focusing on predicting the percentages of voting intention polls (conducted by YouGov) for the three major political parties - Conservatives, Labours and Liberal Democrats - during a 5-month period before the election date (May 6, 2010). We form three methodologies for extracting positive or negative sentiment from tweets, which build on each other, and then propose two supervised models for turning sentiment into voting intention percentages. Interestingly, when the content of tweets is enriched by attaching synonymous words, a significant improvement on inference performance is achieved reaching a mean absolute error of 4.34 + - 2.13 ; in that case, the predictions are also shown to be statistically significant. The presented methods should be considered as work-in-progress; limitations and suggestions for future work appear in the final section of this script.
Moreover, an interesting paper conducted a further analysis of the aforementioned methodologies and proposed a triplet of necessary standards that any theory aiming to provide a consistent prediction of elections using content from the Social Media should follow @cite_10 . The authors recommend that firstly the prediction theory should be formed as a well defined algorithm, secondly the analysis should be aware of the different characteristics arising in the Social Web and thirdly that the method must include experimental justification on why it works.
@cite_10
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The widespread adoption of social media for political communication creates unprecedented opportunities to monitor the opinions of large numbers of politically active individuals in real time. However, without a way to distinguish between users of opposing political alignments, conflicting signals at the individual level may, in the aggregate, obscure partisan differences in opinion that are important to political strategy. In this article we describe several methods for predicting the political alignment of Twitter users based on the content and structure of their political communication in the run-up to the 2010 U.S. midterm elections. Using a data set of 1,000 manually-annotated individuals, we find that a support vector machine (SVM) trained on hash tag metadata outperforms an SVM trained on the full text of users' tweets, yielding predictions of political affiliations with 91 accuracy. Applying latent semantic analysis to the content of users' tweets we identify hidden structure in the data strongly associated with political affiliation, but do not find that topic detection improves prediction performance. All of these content-based methods are outperformed by a classifier based on the segregated community structure of political information diffusion networks (95 accuracy). We conclude with a practical application of this machinery to web-based political advertising, and outline several approaches to public opinion monitoring based on the techniques developed herein.