Análise Temporal de Coesão de Discurso em Mídia Social Durante Grandes Eventos

• João Matheus N. Gonçalves UFRJ
• Jonice Oliveira UFRJ
• Fabio Porto LNCC
• Tiago C. França UFRRJ

DOI: https://doi.org/10.5753/brasnam.2023.230614

Abstract

Events like COVID-19 lead to a large number of publications on social media. Different publications and sub-events are discussed in such a way that the discourse may or may not be aligned, leading to greater or lesser textual cohesion between publications. In this work, a method is proposed for the analysis of textual cohesion and its variation over time. The method was used and evaluated with synthetic databases built with known levels of cohesion and subsequently applied to a database of tweets published during the pandemic. With the results, it was possible to understand the evolution of cohesion over time in tweets written in portuguese related to COVID-19.

References

Antunes, C. (2005). “Lutar com as palavras: coesão e coerência”. São Paulo: Parábola Editorial.

Crossley, S.A., Kyle, K. e Dascalu, M. (2019) “The Tool for the Automatic Analysis of Cohesion 2.0: Integrating semantic similarity and text overlap”. Behav Res 51, 14–2. https://doi.org/10.3758/s13428-018-1142-4.

Fadigas, I.S. e Pereira, H.B.B. (2013) “A network approach based on cliques”. Physica A: Statistical Mechanics and its Applications.

França, T. C.. "ANDARE: um framework para inclusão da análise de dados de mídias sociais no contexto da preparação e resposta à emergência em situações de manifestações de massa", 2019, Tese (Doutorado) - Curso de Pós-graduação em Informática, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 2019, https://tinyurl.com/tmaydae4. Acesso em: 08 Mar. 2023.

Halliday, M. e Hasan, R. (1976) “Cohesion in English”. London: Longman Group Ltd.

Khyani, D., Siddhartha, B. S., Niveditha, N. M., e Divya, B. M. (2021) “An interpretation of lemmatization and stemming in natural language processing”. Journal of University of Shanghai for Science and Technology, 22(10), P. 350-357.

Lachner, A. e Neuburg, C. (2019) “Learning by writing explanations: computer-based feedback about the explanatory cohesion enhances students’ transfer”. Instr Sci 47, 19–37, https://doi.org/10.1007/s11251-018-9470-4.

Neves, J. C. B. ; França, Tiago Cruz ; Bastos, M. P.; Carvalho, P. V. R.; Gomes, J. O. Analysis of government agencies and stakeholders? twitter communications during the first surge of COVID-19 in Brazil. WORK-A Journal of Prevention Assessment & Rehabilitation, v. 73, p. 1-13, 2022.

Sanarmed (2020). “Linha do tempo do coronavirus no Brasil”, https://www.sanarmed.com/linha-do-tempo-do-coronavirus-no-brasil.