Analysis of large amounts of political data and complex networks: mining modelling and applications in Computational Political Science

• Grant number: 22/03090-0
• Support Opportunities: Research Grants - Initial Project
• Start date: March 01, 2023
• End date: February 29, 2028
• Field of knowledge: Humanities - Political Science
• Principal Investigator: Sylvia Iasulaitis
• Grantee: Sylvia Iasulaitis
• Host Institution: Centro de Educação e Ciências Humanas (CECH). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated researchers: Alan Demétrius Baria Valejo ; Brett Mylo Drury ; Eanes Torres Pereira ; Eloize Rossi Marques Seno ; Helena de Medeiros Caseli ; Manel Herat ; Márcio Luis Lanfredi Viola ; Soong Moon Kang
• Associated scholarship(s): 24/17208-8 - Detection of offensive comments and hate speech in tweets about Brazilian politics, BP.IC; 24/18523-4 - Identification and Classification of Disinformations during the 2022 Electoral Process in Brazil, BP.IC; 24/00479-9 - Scientific Dissemination of Big Social Data: Popularization of Scientific Research on data collected from Twitter (X) during presidential elections, BP.JC; + associated scholarships 24/00477-6 - Scientific Dissemination of Big Social Data: Popularization of Scientific Research on data collected from Twitter (X) during presidential elections, BP.JC; 23/17214-5 - Analysis of voting dynamics and its themes in the Chamber of Deputies using Natural Language Processing and Complex Networks, BP.IC; 23/03704-0 - Development of a visualization tool for the Chamber of Deputies open data using complex networks., BP.IC - associated scholarships

Abstract

Large volumes of unstructured political data have represented a challenge for scientific research. As a result, the development of tools aimed at extracting scientific and political information from Big Data is highly strategic. The objective of this proposal is to develop computational techniques and tools for the collection, processing and classification of political data, which provide the realization of experiments to analyze complex networks. Combining Machine Learning and Social Network Analysis, this research will develop several applications and modeling of different relations between data from Social Networking Sites, more specifically Twitter, as well as from the Open Data API of the House of Representatives. The intention is that the research generates innovation in the field of political methodology by bringing together Political Science, Computer Science and Data Science, and also contributes to to the development of Computational Political Science in Brazil. In the execution of the project, besides the open-source artifacts that will be made available to the scientific community, it is expected that a proprietary software will be produced, object of patent protection with the host institution, considering the norms and guidelines of FAPESP. (AU)

Scientific publications

(4)

(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)

DOS SANTOS, NICOLAS ROQUE; MINATEL, DIEGO; BARIA VALEJO, ALAN DEMETRIUS; LOPES, ALNEU DE A.. Bipartite Graph Coarsening for Text Classification Using Graph Neural Networks. PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I, v. 14469, p. 16-pg., 2024-01-01. (21/06210-3, 22/03090-0, 22/09091-8, 20/09835-1)

ALTHOFF, PAULO EDUARDO; VALEJO, ALAN DEMETRIUS BARIA; FALEIROS, THIAGO DE PAULO. Coarsening effects on k-partite network classification. APPLIED NETWORK SCIENCE, v. 8, n. 1, p. 21-pg., 2023-12-05. (21/06210-3, 22/03090-0)

PEREIRA, EANES TORRES; IASULAITIS, SYLVIA; GRECO, BRUNO CARDOSO. Analysis of causal relations between vaccine hesitancy for COVID-19 vaccines and ideological orientations in Brazil. Vaccine, v. 42, n. 13, p. 9-pg., 2024-04-25. (22/03090-0)

FALEIROS, THIAGO DE PAULO; ALTHOFF, PAULO EDUARDO; BARIA VALEJO, ALAN DEMETRIUS. Analyzing the Impact of Coarsening on k-Partite Network Classification. INTELLIGENT SYSTEMS, BRACIS 2024, PT I, v. 15412, p. 13-pg., 2025-01-01. (22/03090-0)