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In this paper we present the work done on social media analysis to predict civil unrest using keyword filtering. The information given on the social media is delivered to every person within the fraction of seconds. This rapid circulation of information and the people opinions through social platform affects or create
We examine whether aggregate daily Twitter keyword volumes over eight months from November 2011 to June 2012 can be used to predict aggregate daily consumer spending as reported by Gallup. We also examine whether Twitter keyword volume improves predictive ability over prediction based solely on current spending
which follows a keyword over multiple social media platforms (e.g. Twitter, Facebook), maintaining the aggregated data in a no-SQL database. Afterwards, in order to choose the most suitable system for our application, we will analyze the no_SQL database systems, define their architecture and data model, and depending on
from twitter can be used to predict the sales of iPhones. Based on a conceptual model of social data consisting of social graph (actors, actions, activities, and artefacts) and social text (topics, keywords, pronouns, and sentiments), we develop and evaluate a linear regression model that transforms iPhone tweets into a
document was published) such as location-based social media data to discover prevalent topics or newly emerging events with respect to an area and a time point. We consider a map view composed of regular grids or tiles with each showing topic keywords from documents of the corresponding region. To this end, we present a
Spatial event forecasting from social media is potentially extremely useful but suffers from critical challenges, such as the dynamic patterns of features (keywords) and geographic heterogeneity (e.g., spatial correlations, imbalanced samples, and different populations in different locations). Most existing approaches
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