Article Brapci-Revistas

COVID-19 e a circulação de informações em redes sociais: análise em um grupo brasileiro no Facebook sobre o Coronavírus

COVID-19 and the circulation information on social networks: analysis in a Brazilian Facebook group about the Coronavirus

Douglas Farias Cordeiro
Anelise Souza Rocha
Larissa Machado Vieira
Kátia Kelvis Cassiano
Núbia Rosa da Silva

This article aims to quantify and qualify the information circulating in social media groups about COVID-19, the subjects covered in posts, as well as the possible relations with other subjects, events or social events, in order to generate a representative panorama of perception and social reaction to the coronavirus pandemic. For this, statistical techniques, data mining and machine learning are used to the characterization, pattern detection, and grouping of textual data. The experiments are carried out on a dataset of textual data extracted from a Brazilian public group about COVID-19 of the social network Facebook. Statistical analyzes are crossed with data on the advance of the number of infected, and with specific political-social events, revealing variations and influences in terms of participation and engagement in the analyzed group. In addition, through the results obtained by the clustering method used, two main groups of posts are detected, the first presenting a content pattern geared to governmental issues, and the second to personal issues. The results achieved still allow a reflection on the possible social impacts of the creation or absence of public policies to deal with the COVID-19 pandemic.@en

Este artigo tem como objetivo quantificar e qualificar as informações que circulam nas redes sociais sobre o COVID-19, os assuntos abordados nas postagens, bem como as possíveis relações com outros assuntos, eventos ou eventos sociais, de forma a gerar um panorama representativo da percepção e reação social à pandemia de coronavírus. Para isso, técnicas estatísticas, mineração de dados e aprendizado de máquina são utilizadas para a caracterização, detecção de padrões e agrupamento de dados textuais. Os experimentos são realizados em um conjunto de dados textuais extraídos de um grupo público brasileiro sobre o COVID-19 da rede social Facebook. As análises estatísticas são cruzadas com dados sobre o avanço do número de infectados e com eventos político-sociais específicos, revelando variações e influências em termos de participação e engajamento no grupo analisado. Além disso, através dos resultados obtidos pelo método de agrupamento utilizado, são detectados dois grupos principais de postagens, o primeiro apresentando um padrão de conteúdo voltado para questões governamentais e o segundo para questões pessoais. Os resultados alcançados permitem ainda uma reflexão sobre os possíveis impactos sociais da criação ou ausência de políticas públicas para o enfrentamento da pandemia COVID-19.@pt

. Covid-19 and the circulation information on social networks: analysis in a brazilian facebook group about the coronavirus covid-19 e a circulação de informações em redes sociais: análise em um grupo brasileiro no facebook sobre o coronavírus. Em questão, [????].

References

  • AHSAN, M.., KUMARI, M.., SHARMA, T. Rumors detection, verification and controlling mechanisms in online social networks: A survey. Online Social Networks and Media, [s.l.], v. 14, p. 100050, 2019.
  • ARNABOLDI, V.., CONTI, M.., PASSARELLA, A.., DUNBAR, R.I. Online social networks and information diffusion: The role of ego networks. Online Social Networks and Media, [s.l.], v. 1, p. 44 – 55, 2017.
  • BELK, R. Sharing Versus Pseudo-Sharing in Web 2.0. The Anthropologist, [s.l.], v. 18, n. 1, p. 7-23, 2014.
  • BOURICHE, B. L’analyse de similitude. In: ABRIC, J. (ed.) Mé ésentations Sociales. Toulose, France: Eres, 2005. p. 221252.
  • BRASIL. Decreto-lei nº 6, de 20 de março de 2020. Reconhece, para os fins do art. 65 da Lei Complementar nº 101, de 4 de maio de 2000, a ocorrência do estado de calamidade pública, nos termos da solicitação do Presidente da República encaminhada por meio da Mensagem nº 93, de 18 de março de 2020. Disponible in: https://legis.senado.leg.br/norma/31993957/publicacao/31994188/. Access in: Jul. 31, 2020.
  • CERCEL, D.., TRAUSAN-MATU, S. Opinion propagation in online social networks: A survey. In: International Conference on Web Intelligence, Mining and Semantics, 4, Thessaloniki, Greece, 2014. Proceedings […]. New York,
  • NY, USA: Association for Computing Machinery, 2014. p. 1-10. DRAGOVIĆ, N., VASILJEVIC, D.., STANKOV, U.., VUJICI, M. Go social for your own safety! Review of social networks use on natural disasters – case studies from worldwide. Open Geosciences, [s. l.], v. 11, n. 1, p. 352-366, 2019.
  • FAYYAD, U.., PIATETSKY-SHAPIRO, G.., SMITH, P. From data mining to knowledge discovery in databases. AI Magazine, Palo Alto, CA, USA, v. 17, n. 3, p. 37–54, 1996.
  • FERRARA, E. Disinformation and social bot operations in the run up to the 2017 French presidential election. First Monday, Chicago, IL, USA, v. 22, n. 8, 2017.
  • FISHER, E.., MEHOZAY, Y. How algorithms see their audience: media epistemes and the changing conception of the individual. Media, Culture and Society, [s. l.], v. 41, n. 8, p. 1176–1191, 2019.
  • GREENELTCH, N. Python Data Mining Quick Start Guide. Birmingham, UK: Packt Publishing, 2019.
  • GRUEBNER, O.., LOWE, S.R.., SYKORA, M.., SHANKARDASS, K.., SUBRAMANIAN, S.., GALEA, S. Spatio-temporal distribution of negative emotions in New York city after a natural disaster as seen in social media. International Journal of Environmental Research and Public Health, Basel, Switzerland, v. 15, p. 1-12, 2018.
  • HAN, J.., KAMBER, M.., PEI, J. Data Mining: concepts and techniques. Burlington, Massachusetts, USA: Morgan Kaufmann Publishers, 2016.
  • HUSSAIN, M.N.., TOKDEMIR, S.., AGARWAL, N.., AL-KHATEEB, S. Analyzing disinformation and crowd manipulation tactics on Youtube. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Barcelona, Spain, 2018. Proceedings […]. Piscataway, NJ, USA: IEEE, 2018. p. 1092-1095.
  • JARMUL, K.., LAWSON, R. Python Web Scraping. Birmingham, UK: Packt Publishing, 2017.
  • JUST, N.., LATZER, M. Governance by algorithms: reality construction by algorithmic selection on the Internet. Media, Culture and Society, [s. l.], v. 39, n. 2, p. 238-258, 2017.
  • KANE, G.C.., ALAVI, M.., LABIANCA, G.., BORGATTI, S.P. What’s different about social media networks? A framework and research agenda. MIS Quarterly, Minnesota, USA, v. 38, n. 1, p. 274-304, 2014.
  • KEIM, M., NOJI, E. Emergent use of social media: A new age of opportunity for disaster resilience. American Journal of Disaster Medicine, Weston, MA, USA, v. 6, n. 1, p. 47-54, 2011.
  • KEMP, S. Digital 2020: Global digital yearbook. [s. l.]: Kepios Pte. Ltd., 2020. Disponible in: https://datareportal.com/reports/digital-2020-global-digitalyearbook. Access in: Jul. 31, 2020.
  • KIM, J.., BAE, J.., HASTAK, M. Emergency information diffusion on online social media during storm Cindy in U.S. International Journal of Information Management, [s. l.], v. 40, p. 153-165, 2018.
  • KRYVASHEYEU, Y., CHEN, H., OBRADOVICH, N., MORO, E., VAN HENTENRYCK, P., FOWLER, J.., CEBRIAN, M. Rapid assessment of disaster damage using social media activity. Science Advances, Washington, DC, USA, v. 2, n. 3, p. 1-11, 2016.
  • LE, Q.., MIKOLOV, T. Distributed representations of sentences and documents. In: International Conference on Machine Learning, 31, Beijing, China, 2014. Proceedings […]. v. 32. Beijing, China, 2014, p. 1-9.
  • LEE, D.., WELSH, M. An empirical evaluation of models of text document similarity. In: Annual Conference of the Cognitive Science Society, 27, Stresa, Italy, 2005. Proceedings […]. Mahwah, NJ, USA: Lawrence Erlbaum Associates Inc., 2005. p. 1254-1259.
  • LEVY, P. Virtual communities and information services: an overview. VINE, [s. l.], v. 7, n. 5, p. 3-9, 1997.
  • LI, N.., DAS, S.K. Efficiently discovering users connectivity with local information in online social networks. Online Social Networks and Media, [s. l.], v. 16, p. 100062, 2020.
  • LI, X.., XIE, Q.., JIANG, J.., ZHOU, Y.., HUANG, L. Identifying and monitoring the development trends of emerging technologies using patent analysis and twitter data mining: the case of perovskite solar cell technology. Technological Forecasting and Social Change, [s. l.], v. 146, p. 687-705, 2019.
  • LIPSCHULTZ, J.H. Free expression in the age of the Internet: social and legal boundaries. Abingdon, UK: Routledge, 2018.
  • LIU, T.., ZHANG, H.., ZHANG, H. The impact of social media on risk communication of disasters - a comparative study based on sina weibo blogs related to tianjin explosion and typhoon pigeon. International Journal of Environmental Research and Public Health, Basel, v. 17, n. 3, p. 1-17, 2020.
  • LIU, Y.., GAYLE, A.., WILDER-SMITH, A.., ROCKLÖV, J. The reproductive number of covid-19 is higher compared to SARS coronavirus. Journal of Travel Medicine, v. 27, n. 2, p. 1-4, 2020.
  • LOGAN, R.K. Understanding new media: Extending Marshall McLuhan. Second edition. Bern, Switzerland: Peter Lang Publishing, 2016.
  • MELO, C.M.L.., SILVA, G.A.S.., MELO, A.R.S.., DE FREITAS, A.C. COVID19 pandemic outbreak: the Brazilian reality from the first case to the collapse of health services. Annals of the Brazilian Academy of Sciences, Rio de Janeiro, Brazil, v. 94, n. 4, p. 1-14, 2020.
  • MCLUHAN, M. Understanding Media: The Extensions of Man. Cambridge, MA, USA: MIT Press, 1994.
  • MIKOLOV, T.., SUTSKEVER, I.., CHEN K.., CORRADO G.., DEAN J. Distributed representations of words and phrases and their compositionality. In: International Conference on Neural Information Processing Systems, 26, Stateline, Nevada, USA, 2013. Proceedings […]. Red Hook, NY, USA: Curran Associates Inc., 2013. p. 3111-3119.
  • O’REILLY, T. What Is Web 2.0 - Design Patterns and Business Models for the Next Generation of Software. Newton, Massachusetts, USA: O’Reilly Publishing, 2005.
  • PAN F.., YE, T.., SUN, P.., GUI, S.., LIANG, B.., LI, L.., ZHENG, D.., WANG, J.., HESKETH, R.L., YANG, L.., ZHENG, C. Time course of lung changes on Chest CT during recovery from 2019 novel coronavirus (Covid-19). Radiology, Oak Brook, IL, USA, v. 295, n. 3, p. 715-721, 2020.
  • PENNI, J. The future of online social networks (osn): A measurement analysis using social media tools and application. Telematics and Informatics, [s. l.], v. 34, n. 5, p. 498-517, 2017.
  • PROVOST F.., FAWCETT, T. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Newton, Massachusetts,
  • USA: O’Reilly Media, 2013.
  • RIVEST, R. RFC1321: The MD5 Message-Digest Algorithm. Marina del Rey, CA, USA: RFC Editor, 1992.
  • RUMELHART, D.., HINTON G.., WILLIAMS, R. Learning representations by back-propagating errors. Nature, [s. l.], v. 323, p. 533-536, 1986.
  • SALLOUM, S.., AL-EMRAN, M.., SHAALAN, K. Mining social media text: extracting knowledge from Facebook. International Journal of Computing and Digital Systems, Bahrain, v. 6, n. 2, p. 73-81, 2017.
  • SREEJESH, S.., PAUL, J.., STRONG, C.., PIUS, J. Consumer response towards social media advertising: effect of media interactivity, its conditions and the underlying mechanism. International Journal of Information Management, [s. l.], v. 54, p. 102-155, 2020.
  • STATISTA. Increased media device usage due to the coronavirus outbreak among internet users worldwide as of march 2020, by country. In: STATISTA. [Hamburg: Statista GmbH], 2020a. Disponible in: https://www.statista.com/statistics/1106607/device-usage-coronavirusworldwide-by-country/. Access in: Jul. 31, 2020.
  • STATISTA. Leading countries based on number of Facebook users as of January 2020. In: STATISTA. [Hamburg: Statista GmbH], 2020b. Disponible in: https://www.statista.com/statistics/268136/top-15-countries-based-onnumber-of-facebook-users/. Access in: Jul. 31, 2020.
  • TAN, P.., STEINBACH, M.., KARPATNE, A.., KUMAR, V. Introduction to Data Mining. Second edition. London, UK: Pearson, 2005.
  • TANG, J.., SUN, J.., WANG, C.., YANG, Z. Social influence analysis in largescale networks. In: ACM SIGKDD international conference on Knowledge discovery and data mining, 15, 2009, Paris, France. Proceedings […]. Paris, France: ACM, 2009. p. 807-816.
  • WANG H.., CASTANON, J.A. Sentiment expression via emoticons on social media. In: IEEE International Conference on Big Data, Santa Clara, CA, USA, 2015. Proceedings […]. Piscataway, NJ, USA: IEEE, 2015. p. 2404-2408.
  • WORLD HEALTH ORGANIZATION. Coronavirus disease 2019 (covid-19) situation report – 69. Genève, Switzerland: World Health Organization, 2020a.
  • WORLD HEALTH ORGANIZATION. Who Director-G n al ning remarks at the media briefing on Covid-19 – 11 March 2020. 11 mar. 2020b. Disponible in: https://www.who.int/dg/speeches/detail/who-director-general-sopening-remarks-at-the-media-briefing-on-covid-19---11-march2020?fbclid=IwAR1kA7MQ8c5t-th5B6VoZWiaPDNP6X8QHEK9ICjXPd5tNcvPU3fIH34MT4/. Access in: Jul. 31, 2020.
  • WU, Z.., MCGOOGAN J.M. Characteristics of and Important Lessons from the Coronavirus Disease 2019 (COVID-19) Outbreak in China. JAMA, Chicago,
  • IL, USA, v. 323, n. 1, p. 1239-1242, 2020.
NLP0.29
Visto 19 vezes
sem referências
Array
(
    [dateOfAvailability] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [2021-06-14] => 160666
                        )

                    [1] => Array
                        (
                            [2021-06-30] => 161276
                        )

                )

        )

    [hasAuthor] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [Douglas Farias Cordeiro] => 108449
                        )

                    [1] => Array
                        (
                            [Anelise Souza Rocha] => 160933
                        )

                    [2] => Array
                        (
                            [Larissa Machado Vieira] => 160934
                        )

                    [3] => Array
                        (
                            [Kátia Kelvis Cassiano] => 108450
                        )

                    [4] => Array
                        (
                            [Núbia Rosa da Silva] => 158589
                        )

                )

        )

    [hasFileStorage] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [_repository/47/2022/06/oai_seer_ufrgs_br_article_106683#00002.pdf] => 197431
                        )

                )

        )

    [hasPageEnd] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [67] => 1383
                        )

                )

        )

    [hasPageStart] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [42] => 608
                        )

                )

        )

    [hasSectionOf] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [Artigo] => 3
                        )

                )

        )

    [hasSubject] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [Ciências sociais aplicadas] => 234537
                        )

                    [1] => Array
                        (
                            [Ciência da informação] => 237157
                        )

                    [2] => Array
                        (
                            [Rede social digital] => 52579
                        )

                    [3] => Array
                        (
                            [Grupo social] => 6038
                        )

                    [4] => Array
                        (
                            [Mineração de dado] => 15401
                        )

                    [5] => Array
                        (
                            [Análise descritiva] => 268364
                        )

                    [6] => Array
                        (
                            [Covid-19] => 234442
                        )

                    [7] => Array
                        (
                            [Redes sociais digitais] => 271591
                        )

                    [8] => Array
                        (
                            [Grupos sociais] => 266742
                        )

                    [9] => Array
                        (
                            [Mineração de dados] => 270961
                        )

                    [10] => Array
                        (
                            [Ciências sociais aplicadas] => 9181
                        )

                )

            [es] => Array
                (
                    [0] => Array
                        (
                            [Covid-19] => 227495
                        )

                )

            [en] => Array
                (
                    [0] => Array
                        (
                            [Online social networks] => 234025
                        )

                    [1] => Array
                        (
                            [Online social groups] => 160932
                        )

                    [2] => Array
                        (
                            [Data mining] => 234340
                        )

                    [3] => Array
                        (
                            [Descriptive analysis] => 268363
                        )

                    [4] => Array
                        (
                            [Sars-cov-2] => 145724
                        )

                    [5] => Array
                        (
                            [Covid-19] => 237595
                        )

                )

        )

    [wasPublicationInDate] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [2021-06-30] => 245007
                        )

                )

        )

    [hasLanguageExpression] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [en] => 232773
                        )

                )

        )

    [hasLicense] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [Copyright (c)] => 232755
                        )

                    [1] => Array
                        (
                            [CCBY4.0] => 232756
                        )

                )

        )

    [isPartOfSource] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [Em questão] => 47
                        )

                )

        )

    [hasIssueOf] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [ISSUE:JNL:00002-2021--ONLINE] => 156645
                        )

                    [1] => Array
                        (
                            [ISSUE:JNL:00002-2021-27-3] => 161419
                        )

                )

        )

    [hasAbstract] => Array
        (
            [en] => Array
                (
                    [0] => Array
                        (
                            [This article aims to quantify and qualify the information circulating in social media groups about COVID-19, the subjects covered in posts, as well as the possible relations with other subjects, events or social events, in order to generate a representative panorama of perception and social reaction to the coronavirus pandemic. For this, statistical techniques, data mining and machine learning are used to the characterization, pattern detection, and grouping of textual data. The experiments are carried out on a dataset of textual data extracted from a Brazilian public group about COVID-19 of the social network Facebook. Statistical analyzes are crossed with data on the advance of the number of infected, and with specific political-social events, revealing variations and influences in terms of participation and engagement in the analyzed group. In addition, through the results obtained by the clustering method used, two main groups of posts are detected, the first presenting a content pattern geared to governmental issues, and the second to personal issues. The results achieved still allow a reflection on the possible social impacts of the creation or absence of public policies to deal with the COVID-19 pandemic.] => 0
                        )

                    [1] => Array
                        (
                            [This article aims to quantify and qualify the information circulating in social media groups about COVID-19, the subjects covered in posts, as well as the possible relations with other subjects, events or social events, in order to generate a representative panorama of perception and social reaction to the coronavirus pandemic. For this, statistical techniques, data mining and machine learning are used to the characterization, pattern detection, and grouping of textual data. The experiments are carried out on a dataset of textual data extracted from a Brazilian public group about COVID-19 (SARS-cov-2) of the social network Facebook. Statistical analyzes are crossed with data on the advance of the number of infected, and with specific political-social events, revealing variations and influences in terms of participation and engagement in the analyzed group. In addition, through the results obtained by the clustering method used, two main groups of posts are detected, the first presenting a content pattern geared to governmental issues, and the second to personal issues. The results achieved still allow a reflection on the possible social impacts of the creation or absence of public policies to deal with the COVID-19 pandemic.] => 0
                        )

                )

            [pt] => Array
                (
                    [0] => Array
                        (
                            [Este artigo tem como objetivo quantificar e qualificar as informações que circulam nas redes sociais sobre o COVID-19, os assuntos abordados nas postagens, bem como as possíveis relações com outros assuntos, eventos ou eventos sociais, de forma a gerar um panorama representativo da percepção e reação social à pandemia de coronavírus. Para isso, técnicas estatísticas, mineração de dados e aprendizado de máquina são utilizadas para a caracterização, detecção de padrões e agrupamento de dados textuais. Os experimentos são realizados em um conjunto de dados textuais extraídos de um grupo público brasileiro sobre o COVID-19 da rede social Facebook. As análises estatísticas são cruzadas com dados sobre o avanço do número de infectados e com eventos político-sociais específicos, revelando variações e influências em termos de participação e engajamento no grupo analisado. Além disso, através dos resultados obtidos pelo método de agrupamento utilizado, são detectados dois grupos principais de postagens, o primeiro apresentando um padrão de conteúdo voltado para questões governamentais e o segundo para questões pessoais. Os resultados alcançados permitem ainda uma reflexão sobre os possíveis impactos sociais da criação ou ausência de políticas públicas para o enfrentamento da pandemia COVID-19.] => 0
                        )

                )

        )

    [hasID] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [oai:seer.ufrgs.br:article/106683] => 0
                        )

                )

        )

    [hasRegisterId] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/view/106683] => 0
                        )

                    [1] => Array
                        (
                            [10.19132/1808-524500.%p] => 0
                        )

                    [2] => Array
                        (
                            [10.19132/1808-5245273.42-67] => 0
                        )

                )

        )

    [hasSource] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [Em Questão; Online first] => 0
                        )

                    [1] => Array
                        (
                            [1808-5245] => 0
                        )

                    [2] => Array
                        (
                            [Em Questão; v.27, n.3, jul./set. 2021; 42-67] => 0
                        )

                )

            [nn] => Array
                (
                    [0] => Array
                        (
                            [1807-8893] => 0
                        )

                )

        )

    [hasTitle] => Array
        (
            [en] => Array
                (
                    [0] => Array
                        (
                            [COVID-19 and the circulation information on social networks: analysis in a Brazilian Facebook group about the Coronavirus] => 0
                        )

                )

            [pt] => Array
                (
                    [0] => Array
                        (
                            [COVID-19 e a circulação de informações em redes sociais: análise em um grupo brasileiro no Facebook sobre o Coronavírus] => 0
                        )

                )

        )

    [hasUrl] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/view/106683/61845] => 0
                        )

                    [1] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59788] => 0
                        )

                    [2] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59789] => 0
                        )

                    [3] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59790] => 0
                        )

                    [4] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59791] => 0
                        )

                    [5] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59792] => 0
                        )

                    [6] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/59794] => 0
                        )

                    [7] => Array
                        (
                            [https://seer.ufrgs.br/EmQuestao/article/downloadSuppFile/106683/66192] => 0
                        )

                )

            [nn] => Array
                (
                    [0] => Array
                        (
                            [https://seer.ufrgs.br/index.php/EmQuestao/article/view/106683] => 0
                        )

                    [1] => Array
                        (
                            [https://seer.ufrgs.br/index.php/EmQuestao/article/view/106683/61845] => 0
                        )

                )

        )

    [prefLabel] => Array
        (
            [pt] => Array
                (
                    [0] => Array
                        (
                            [Oai:seer.ufrgs.br:article/106683#00002] => 0
                        )

                )

        )

    [hasDOI] => Array
        (
            [nn] => Array
                (
                    [0] => Array
                        (
                            [10.19132/1808-5245273.42-67] => 0
                        )

                )

        )

)