Obama in Cuba: polarização e participação ativa no twitter

A histórica visita do presidente americano Barack Obama a Cuba, esta semana, impulsionou a presença dos cubanos nas redes sociais – e o debate sobre antigas questões políticas envolvendo a ilha e a difícil relação com os Estados Unidos, que apresenta contornos de conciliação, finalmente, após mais de cinco décadas. A Diretoria de Análise de Políticas Públicas (FGV/DAPP) identificou das 08h de segunda-feira, 21 de março, às 11h de terça (22) mais de 1,6 milhão de menções no Twitter sobre o assunto no mundo inteiro, sendo 800 mil entre as 10h e as 18h de segunda (horários de Brasília), quando Obama iniciou um passeio por Havana e fez uma coletiva de imprensa com o presidente cubano, Raúl Castro.

Stochastic dynamic programming for election timing: A game theory approach

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as "boosts" to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity.

Making political capital:the behaviour of UK financial markets during Election 97

This article examines the behaviour of the UK capital markets during the overnight trading period that coincided with the announcement of the results of the UK general election in May 1997. Evidence that the financial markets responded to the evolving pattern of results is found. In addition, the consensus move experienced as the markets opened the next trading day was influenced by the extent of the moves that had already occurred overnight.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

Semantic sentiment analysis of Twitter

Sentiment analysis over Twitter offer organisations a fast and effective way to monitor the publics' feelings towards their brand, business, directors, etc. A wide range of features and methods for training sentiment classifiers for Twitter datasets have been researched in recent years with varying results. In this paper, we introduce a novel approach of adding semantics as additional features into the training set for sentiment analysis. For each extracted entity (e.g. iPhone) from tweets, we add its semantic concept (e.g. Apple product) as an additional feature, and measure the correlation of the representative concept with negative/positive sentiment. We apply this approach to predict sentiment for three different Twitter datasets. Our results show an average increase of F harmonic accuracy score for identifying both negative and positive sentiment of around 6.5% and 4.8% over the baselines of unigrams and part-of-speech features respectively. We also compare against an approach based on sentiment-bearing topic analysis, and find that semantic features produce better Recall and F score when classifying negative sentiment, and better Precision with lower Recall and F score in positive sentiment classification.