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Understanding opinion and language dynamics using massive data

Abstract

Human society has undergone unprecedented changes, driven by the sudden increase in communicating technological devices that surround us, keeping traces of a large amount of our daily activities. This extremely rapid evolution is associated with a fast adaptive dynamics that induces changes in our everyday practices and has consequences for the nature of human social relations. The rapidly increasing amount of data has generated what is known as Big Data, leading to urgent questions concerning the storage, organization, retrieval, and control of this information. Efforts have been concentrated on the technical aspects of these questions, in order to make Big Data ready to be used. From a different perspective, the present project addresses the following questions: How can sensible information be obtained from Big Data and used to help in elaborating explanatory models of human social actions in different specific circumstances? What are the possible ethical consequences of the application of Big Data analyses in the study of self-organized human actions? We are interested in both searching for traces of social behavior in rough data, and studying the impact that manipulation of Big Data might have on social action. Causal explanations of human individual and collective actions have been the object of criticisms by researchers concerned with the social and ethical aspects. It is our hypothesis that the connection between Big Data, which provides massive amounts of data correlations, and causal explanations might bring about promising novelty in the study of human social actions from the Complex Systems perspective. Our research will focus on the study of opinion dynamics and the language evolution process that might affect this dynamics, based, as a study case, on Big Data issued from two different media: The New York Times and Twitter. (AU)

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
GOLZIO, A. C.; PUERTA-DIAZ, M.; MARTINEZ-AVILA, D.. A Fuzzy Logic Model for the Analysis of Social Corporate Responsibility. EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, v. 8, n. 32, . (16/50256-0, 19/08442-9)

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