Early Prediction of Movie Box Office Success based on Wikipedia Activity Big Data

Use of socially generated "big data" to access information about collective states of the minds in human societies has become a new paradigm in the emerging field of computational social science. A natural application of this would be the prediction of the society's reaction to a new product in the sense of popularity and adoption rate. However, bridging the gap between "real time monitoring" and "early predicting" remains a big challenge. Here we report on an endeavor to build a minimalistic predictive model for the financial success of movies based on collective activity data of online users. We show that the popularity of a movie can be predicted much before its release by measuring and analyzing the activity level of editors and viewers of the corresponding entry to the movie in Wikipedia, the well-known online encyclopedia.Comment: 13 pages, Including Supporting Information, 7 Figures, Download the dataset from: http://wwm.phy.bme.hu/SupplementaryDataS1.zi

Quantifying, Comparing Human Mobility Perturbation during Hurricane Sandy, Typhoon Wipha, Typhoon Haiyan

AbstractClimate change has intensified tropical cyclones, resulting in several recent catastrophic hurricanes and typhoons. Such disasters impose threats on populous coastal urban areas, and therefore, understanding and predicting human movements plays a critical role in disaster evacuation, response and relief. Despite its critical roles, limited research has focused on tropical cyclones and their influence on human mobility. Here, we studied how severe tropical storms could influence human mobility patterns in coastal urban populations using individuals’ movement data collected from Twitter. We selected three significant tropical storms, including Hurricane Sandy, Typhoon Wipha, and Typhoon Haiyan. We analyzed the human movement data before, during, and after each event, comparing the perturbed movement data to movement data from steady states. We also used different statistical analysis approaches to quantify the strength and duration of human mobility perturbation. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions; however, the nature-derived Lévy Walk model still predominantly governs human mobility. Also, human mobility exhibits a surprisingly mild and brief perturbation for Hurricane Sandy and Typhoon Wipha, while the duration of disturbance was much longer for Typhoon Haiyan. Our finding that the Lévy Walk model can still predict human mobility suggests that bio-inspired examinations of human mobility patterns may uncover solutions to improve disaster evacuation, response and relief plans

Comunicação política no Facebook e previsão eleitoral - Análise de big data da eleição presidencial brasileira de 2018 no Brasil: Big data analysis of the 2018 Brazilian presidential election Brazil

This article aims to understand how the quantitative analysis of political communication in social media, through indicators of engagement to the political speech, is able to predict the electoral outcome. This study is a big data analysis of the first round of the 2018 presidential election in Brazil. Specifically, around 10,000 posts were collected from official Facebook campaign pages between June 1 and October 7, 2018. Regarding voting intent, a series of daily representative of the Brazilian population opinion polls were conducted for the same period. In order to understand whether there is a causal relationship between candidate engagement on Facebook and voting intent, predictive analysis was performed by testing two empirical approaches: one with aggregated data and the other with 90 multiple linear regression prediction models. We conclude the analysis by comparing the actual election results with all predictive models. The results showed that both the aggregate and regressive approaches demonstrate that the candidate engagement rate on Facebook is a good electoral predictor. The results reinforce the theories that defend the relevance of data coming from political behaviour in social media as good electoral predictor.O presente artigo busca entender como a análise quantitativa da comunicação política nas mídias sociais, mediante indicadores de engajamento ao discurso de candidatos, é capaz de prever o resultado eleitoral. Trata-se de uma análise de big data do 1º turno do pleito presidencial de 2018 no Brasil. Concretamente, foram coletadas cerca de 10 mil postagens das páginas oficiais de campanha no Facebook entre 1 de junho e 7 de outubro de 2018. No que se refere à intenção de voto, foi realizada para o mesmo período uma série surveys de opinião com frequência diária, representativa da população brasileira. Com o fim de entender se há uma relação causal entre o engajamento ao candidato no Facebook e a sua intenção de voto, a análise preditiva foi realizada testando duas abordagens empíricas: uma com dados agregados e a outra com 90 modelos de previsão mediante regressão linear múltipla. Concluímos a análise comparando os resultados reais das eleições com todos os modelos preditivos. Os resultados mostraram que tanto a abordagem agregada quanto a regressiva demonstram que o índice de engajamento ao candidato no Facebook é um bom preditor eleitoral. Os resultados obtidos reforçam as teorias que defendem a relevância dos dados advindos do comportamento político nas mídias sociais como bons preditores eleitorais