In this paper, we provide a systematic analysis of the Twitter discussion on the 2016 Austrian presidential elections. In particular, we extracted and analyzed a data-set consisting of 343645 Twitter messages related to the 2016 Austrian presidential elections. Our analysis combines methods from network science, sentiment analysis, as well as bot detection. Among other things, we found that: a) the winner of the election (Alexander Van der Bellen) was considerably more popular and influential on Twitter than his opponent, b) the Twitter followers of Van der Bellen substantially participated in the spread of misinformation about him, c) there was a clear polarization in terms of the sentiments spread by Twitter followers of the two presidential candidates, d) the in-degree and out-degree distributions of the underlying communication network are heavy-tailed, and e) compared to other recent events, such as the 2016 Brexit referendum or the 2016 US presidential elections, only a very small number of bots participated in the Twitter discussion on the 2016 Austrian presidential election.
As a new communication paradigm, social media has promoted information dissemination in social networks. Previous research has identified several content-related features as well as user and network characteristics that may drive information diffusion. However, little research has focused on the relationship between emotions and information diffusion in a social media setting. In this paper, we examine whether sentiment occurring in social media content is associated with a user's information sharing behavior. We carry out our research in the context of political communication on Twitter. Based on two data sets of more than 165,000 tweets in total, we find that emotionally charged Twitter messages tend to be retweeted more often and more quickly compared to neutral ones. As a practical implication, companies should pay more attention to the analysis of sentiment related to their brands and products in social media communication as well as in designing advertising content that triggers emotions. We present a generic approach to real-time monitoring of the Twitter sentiment and show its application to the Bulgarian parliamentary elections in May 2013. Our approach is based on building high quality sentiment classification models from manually annotated tweets. In particular, we have developed a user-friendly annotation platform, a feature selection procedure based on maximizing prediction accuracy, and a binary SVM classifier extended with a neutral zone. We have also considerably improved the language detection in tweets. The evaluation results show that before and after the Bulgarian elections, negative sentiment about political parties prevailed. Both, the volume and the difference between the negative and positive tweets for individual parties closely match the election results. The later result is somehow surprising, but consistent with the prevailing negative sentiment during the elections. Social media services have become increasingly popular and their penetration is worldwide. Micro-blogging services, such as Twitter, allow users to express themselves, share their emotions and discuss their daily life affairs in real-time, covering a variety of different points of view and opinions, including political and event-related topics such as immigration, economic issues, tax policy or election campaigns. On the other hand, traditional methods tracking public opinion still heavily rely upon opinion polls, which are usually limited to small sample sizes and can incur in significant costs in terms of time and money. In this paper, we leverage state-of-the-art techniques of sentiment analysis for real-time political emotion tracking. In particular, we analyze mentions of personal names of 18 presidents in Latin America, and measure each political figure's effect in the emotions reflected on the social web. the purpose of this research is to find the opinion on Twitter about the 2014 president candidates and find the correlation between the opinion on Twitter and on digital newspaper. To perform this, tweets are extracted. Some tweets will be labelled president candidates name and the positive and negative sentiment for the training set. A training will be conducted to test whether the training set is enough to perform classification or not. The next step is to calculate the sentiment results and compare to the results from digital newspaper by using a web-based application called Tirto. Deep analysing conducted to analyse the relation between the issues on Twitter and on digital newspaper. This paper analyzes a corpus of Nigerian Tweets collected during the run-up to the 2011 Nigerian Presidential election and compares it with official election returns and polling data. We found that counts of the mentions on Twitter of the two major candidates correlated strongly with polling and election results when compared across the country's geopolitical regions, though the same data over represented two other candidates who did not fare as well in the polls or at the ballot box. Sentiment extracted from Twitter was less accurate in capturing mean levels of support for the two major candidates and, in particular, showed a strong negativity bias against the incumbent president. Twitter sentiment did mirror regional trends in the polling and election results though not as strongly as seen for mention counts. We demonstrate methodologically how to sample these data, extract sentiment, and compare this sentiment with ground-truth polling data and election results. Although social media clearly capture opinion about contentious issues, our results suggest that the opinion represented there may not always accurately reflect true public opinion.