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A common form of sarcasm on Twitter consists of a positive sentiment contrasted with a negative situation. For example, many sarcastic tweets include a positive sentiment, such as " love " or " enjoy " , followed by an expression that describes an undesirable activity or state (e.g., " taking exams " or " being ignored "). We have developed a sarcasm(More)
—We have implemented a social media data mining system capable of forecasting events related to Latin American social unrest. Our method directly extracts a small number of tweets from publicly-available data on twitter.com, condenses similar tweets into coherent forecasts, and assembles a detailed and easily-interpretable audit trail which allows end users(More)
We demonstrate how one can generate predictions for several thousand incidents of Latin American civil unrest, often many days in advance, by surfacing informative public posts available on Twitter and Tumblr. The data mining system presented here runs daily and requires no manual intervention. Identification of informative posts is accomplished by applying(More)
Twitter has become one of the foremost platforms for information sharing. Consequently , it is beneficial for the consumers of Twitter to know the origin of a tweet, as it affects how they view and interpret this information. In this paper, we classify tweets based on their origin, exploiting only the textual content of tweets. Specifically, using a rich,(More)
Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. In-ternet systems are particularly attractive for disease outbreaks because they can provide data in near real-time and can be verified by individuals(More)
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