Mining social influence in science and vice-versa: A topic correlation approach |
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| Institution: | 1. Programa de Pós-Graduação em Informática, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 – Bl. E – CCMN/NCE, 21.941-590, Cidade Universitária, Rio de Janeiro, RJ, Brazil;2. Departamento de Ciência da Computação, Universidade Federal do Rio de Janeiro, Av. Athos da Silveira Ramos, 274 – Bl. E – Room 1038 (NCE), 21.941-916, Cidade Universitária, Rio de Janeiro, RJ, Brazil;1. Room 1103, Bldg 24, ANU College of Business and Economics, Copland Building 24, The Australian National University, ACT, 2601, Australia;2. Dean, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-051, United Arab Emirates;3. Chair of Marketing and Entrepreneurship, College of Business, Zayed University, Abu Dhabi, Khalifa City, FF1-2-049, United Arab Emirates;1. Department of Industrial Systems and Engineering, Indian Institute of Technology-Kharagpur, Kharagpur, 721302, India;2. Reliability Engineering Centre, Indian Institute of Technology-Kharagpur, Kharagpur 721302, India;1. Fundação Escola de Comércio Álvares Penteado, Campus Liberdade - Avenida da Liberdade, 532 - Liberdade, São Paulo - S.P., 01502-001, Brazil;2. Montpellier Business School, 2300, Avenue Des Moulins, Montpellier – Cédex 4, 34185, France;1. Department of Energy, Systems, Territory and Construction Engineering, University of Pisa, Largo Lucio Lazzarino, 1, 56122 Pisa, Italy;2. Department of Enterprise Engineering, University of Rome Tor Vergata, Via Orazio Raimondo, 00173, Rome, Italy;1. Cooperative Institute for Mesoscale Meteorological Studies (CIMMS), University of Oklahoma, National Weather Center, 120 David L. Boren Blvd., Suite 2100, Norman, Oklahoma 73072, USA;2. Department of Computer Science, Purdue University, 305 N. University Street, West Lafayette, IN, 47907, USA |
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| Abstract: | There is no doubt that scientific discoveries have always brought changes to society. New technologies help solve social problems such as transportation and education, while research brings benefits such as curing diseases and improving food production. Despite the impacts caused by science and society on each other, this relationship is rarely studied and they are often seen as different universes. Previous literature focuses only on a single domain, detecting social demands or research fronts for example, without ever crossing the results for new insights. In this work, we create a system that is able to assess the relationship between social and scholar data using the topics discussed in social networks and research topics. We use the articles as science sensors and humans as social sensors via social networks. Topic modeling algorithms are used to extract and label social subjects and research themes and then topic correlation metrics are used to create links between them if they have a significant relationship. The proposed system is based on topic modeling, labeling and correlation from heterogeneous sources, so it can be used in a variety of scenarios. We make an evaluation of the approach using a large-scale Twitter corpus combined with a PubMed article corpus. In both of them, we work with data of the Zika epidemic in the world, as this scenario provides topics and discussions on both domains. Our work was capable of discovering links between various topics of different domains, which suggests that some of the relationships can be automatically inferred by the sensors. Results can open new opportunities for forecasting social behavior, assess community interest in a scientific subject or directing research to the population welfare. |
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| Keywords: | Topic modeling Social networks Science networks Topic labeling Influence mining Topic similarity |
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