Métodos Digitais Para Análise de Imagens
Uma Revisão Sistemática
DOI:
https://doi.org/10.22409/73z8th51Palavras-chave:
Métodos Digitais, Análise de Imagens, Imagens Digitais, Revisão SistemáticaResumo
Este artigo investiga o uso de métodos digitais para análise de imagens. Através de uma revisão sistemática da literatura de 41 estudos publicados entre 2013 e 2023 em inglês, espanhol e português, a pesquisa aborda três questões-chave: (1) Quais recursos são utilizados para a análise de imagens e coleções de imagens?; (2) Quando esses recursos são aplicados?; e (3) Como eles são implementados? A revisão, realizada em quatro bases de dados acadêmicas, identificou 76 recursos distintos, categorizados por etapa da pesquisa (coleta de dados, refinamento de datasets, análise de imagens e construção de visualizações) e pela exigência de habilidades de programação. O estudo destaca as principais abordagens para a análise de imagens digitais, a natureza dinâmica das ferramentas digitais e a adaptabilidade dos pesquisadores em reaproveitar recursos para a análise de imagens.
Downloads
Referências
ARVIDSSON, A.; CALIANDRO, A. Brand Public. Journal of Consumer Research, Chicago, [s. l.], v. 42, n. 5, p. 727-748, 2016 https://doi.org/10.1093/jcr/ucv053
AZEVEDO, A. K. et al. A big data approach to identify the loss of coastal cultural ecosystem services caused by the 2019 Brazilian oil spill disaster. Anais da Academia Brasileira de Ciências, Rio de Janeiro, v. 94, n. 2, p. 1-11, 2022. https://doi.org/10.1590/0001-3765202220210397
BASTIAN, M.; HEYMANN, S.; JACOMY, M. Gephi: an open source software for exploring
and manipulating networks. Proceedings of the International AAAI Conference on Web and Social Media, [s. l.], v. 3, n. 1, p. 361-362, 2009. https://doi.org/10.1609/icwsm.v3i1.13937
BORRA, E.; RIEDER, B. Programmed method: developing a toolset for capturing and analyzing tweets. Aslib Journal of Information Management, [s. l.], v. 66, n. 3, p. 262-278, 2014. https://doi.org/10.1108/ajim-09-2013-0094
BURGOS-THORSEN, S.; MUNK, A. K. Opening alternative data imaginaries in urban
studies: unfolding COVID place attachments through Instagram photos and computational visual methods. Cities, [s. l.], v. 141, p. 1-21, 2023. https://doi.org/10.1016/j.cities.2023.104470
CASTAÑEDA-GARZA, G.; VALERIO-UREÑA, G.; IZUMI, T. Visual Narrative of the loss
of energy after natural disasters. Climate, Basileia, v. 7, n. 10, p. 1-14, 2019. https://doi.org/10.3390/cli7100118
CHAO, J. Memespector GUI: graphical user interface client for computer vision APIs
[software]. Versão 0.2.5 beta, 2021. Disponível em:
https://github.com/jason-chao/memespector-gui. Acesso em: 21 de junho de 2025.
CHAO, J.; OMENA, J. J. Offline Image Query and Extraction Tool [software], 2021.
Disponível em: https://github.com/jason-chao/offline-image-query. Acesso em: 21 de
junho de 2025.
COLOMBO, G.; BOUNEGRU, L.; GRAY, J. Visual models for social media image analysis: groupings, engagement, trends, and rankings. International Journal of Communication, [s.l.], v. 17, p. 1956-1983, 2023. Disponível em:
https://ijoc.org/index.php/ijoc/article/view/18971. Acesso em: 21 de junho de 2025.
D'ANDREA, C.; MINTZ, A. Studying the Live Cross-Platform Circulation of Images With Computer Vision API: an experiment based on a Sports Media Event. International Journal of Communication, [s. l.], v. 13, p. 1825-1845, 2019. Disponível em: https://ijoc.org/index.php/ijoc/article/view/10423. Acesso em: 21 de junho de 2025.
DURAES, A. Cursos grátis (e fakes!): uma análise dos conteúdos enganosos sobre o Senac na internet. 2022. Dissertação (Mestrado em Mídias Criativas) —Escola de Comunicação, Universidade Federal do Rio de Janeiro, Rio de Janeiro, 2022. Disponível em:
https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=13193764. Acesso em: 21 de junho de 2025.
DIGITAL HUMANITIES LAB. PixPlot [Python script]. 2018. Disponível em: https://dhlab.yale.edu/projects/pixplot/. Acesso em: 10 de novembro de 2023.
GEBOERS, M. A.; DE WIELE, C. T. V. Machine Vision and Social Media Images: why
hashtags matter. Social Media + Society, [s. l.], v. 6, n. 2, p. 1-15, 2020a. https://doi.org/10.1177/2056305120928485
GEBOERS, M. A.; DE WIELE, C. T. V. Regimes of visibility and the affective affordances of
Twitter. International Journal of Cultural Studies, [s. l.], v. 23, n. 5, p. 745-765, 2020b. https://doi.org/10.1177/1367877920923676
GEBOERS, M. A. et al. Why Buttons Matter: repurposing Facebook 's reactions for analysis of the social visual. International Journal of Communication, [s. l.], v. 14, p. 1564-1585, 2020. Disponível em: https://ijoc.org/index.php/ijoc/article/view/11657. Acesso em: 21 de junho de 2025.
GEROSA, A.; GIORGI, G. The memetic cult of personality of politicians during the pandemic. Comunicazione politica, [s. l.], v. 2021, n. 3, p. 357-384, 2021. https://doi.org/https://doi.org/10.3270/102417
GREENE, A. K. Flaws in the highlight real: fitstagram diptychs and the enactment of cyborg embodiment. Feminist Theory, [s. l.], v. 22, n. 3, p. 307-337, 2021. https://doi.org/10.1177/1464700120944794
HIRSBRUNNER, S. D. Negotiating the Data Deluge on YouTube: practices of knowledge
appropriation and articulated ambiguity around visual scenarios of Sea-Level Rise Futures. Frontiers Communication, Lausana, v. 6, p. 1-15, 2021. https://doi.org/10.3389/fcomm.2021.613167
JACOMY, M. et al. ForceAtlas2, a continuous graph layout algorithm for handy network
visualization designed for the Gephi software. PloS one, [s. l.], n. 9, v. 6, p. 1-12, 2014.
https://doi.org/10.1371/journal.pone.0098679
KITCHENHAM, B. A. Guidelines for Performing Systematic Literature Reviews in
Software Engineering. Keele: Keele University, 2007.
MAURI, M. et al. RAWGraphs: a visualisation platform to create open outputs. In:
BIANNUAL CONFERENCE ON ITALIAN SIGCHI CHAPTER, 12., 2017, Nova York. Proceedings [...]. Nova York: Association for Computing Machinery, 2017. p. 1-5. Disponível em: https://dl.acm.org/doi/10.1145/3125571.3125585. Acesso em: 21 de junho de 2025.
MINTZ, A. G. Image Network Plotter [script em Python]. 2018a. Disponível em:
https://github.com/amintz/image-network-plotter. Acesso em: 21 de junho de 2025.
MINTZ, A. G. Memespector Python [script em Python], 2018b. Disponível em:
https://github.com/amintz/memespector-python. Acesso em: 21 de junho de 2025.
MINTZ, A. G. Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online. 2019. Tese (Doutorado em Comunicação Social) — Universidade Federal de Minas Gerais, Belo Horizonte, 2019. Disponível em: https://sucupira.capes.gov.br/sucupira/public/consultas/coleta/trabalhoConclusao/viewTrabalhoConclusao.jsf?popup=true&id_trabalho=8741516#. Acesso em: 21 de junho de 2025.
NIEDERER, S.; COLOMBO, G. Visual methodologies for networked images: designing
visualizations for collaborative research, cross-platform analysis and public participation.
Diseña, Santiago, v. 14, p. 40-67, 2019 https://doi.org/10.7764/disena.14.40-67
OMENA, J. J. et al. The Potentials of Google Vision API-based Networks to Study Natively
Digital Images. Revista Diseña, Santiago, n. 19, p. 1-25, 2021. https://doi.org/10.7764/disena.19.article.1
OMENA, J. J. et al. Cross Vision API-Studies: digital methodologies for understanding
computer vision. Digital Methods Initiative Winter School Report. Amsterdam, 2023.
Disponível em:
https://wiki.digitalmethods.net/Dmi/WinterSchool2023CrossVisionApiStudies. Acesso em: 21 de junho de 2025.
OMENA, J. J.; GRANADO, A. Call into the platform! Merging platform grammatisation and
practical knowledge to study digital networks. Icono14, Revista de comunicación y
tecnologías emergentes, [s. l.], v. 18, n. 1, p. 89-122, 2020. https://doi.org/10.7195/ri14.v18i1.1436
OMENA, J. J.; RABELLO, E. T.; MINTZ, A. Digital Methods for Hashtag Engagement
Research. Social Media + Society, [s. l.], v. 1, n. 18, p. 1-18, 2020. https://doi.org/10.1177/2056305120940697
PEARCE, W.; DE GAETANO, C. Google Images, Climate Change, and the Disappearance of Humans. Diseña, Santiago, v. 19, p. 1-8, 2021. https://doi.org/10.7764/disena.19.article.3
PEARCE, W. et al. Visual cross-platform analysis: digital methods to research social media images. Information, Communication & Society, [s. l.], v. 23, n. 2, p. 161-180, 2020. https://doi.org/10.1080/1369118x.2018.1486871
PETTICREW, M.; ROBERTS, H. Systematic Reviews in the Social Sciences: a practical
guide. Blackwell Publishing: Oxford, 2006.
RABELLO, E. T. et al. Mapping online visuals of shale gas controversy: a digital methods approach. Information, Communication & Society, [s. l.], v. 25, n. 12, p. 2264-2281, 2022. https://doi.org/10.1080/1369118x.2021.1934064
RIEDER, B. Memespector [script PHP], 2018. Disponível em:
https://github.com/bernorieder/memespector. Acesso em: 21 de junho de 2025.
ROGERS, R. Digital Methods. Cambridge: The MIT Press, 2013.
ROGERS, R. Doing Digital Methods. SAGE Publications: Londres, 2019.
ROGERS, R. Visual media analysis for Instagram and other online platforms. Big Data & Society, [s. l.], v. 1, n. 23, p. 1-23, 2021. https://doi.org/10.1177/20539517211022370
SÁNCHEZ-QUERUBÍN, N.; ROGERS, R. Connected routes: migration studies with digital
devices platforms. Social Media + Society, [s. l.], v. 4, n. 1, p. 1-13, 2018. https://doi.org/10.1177/2056305118764427
SCHMØKEL, R.; BOSSETTA, M. Pykognition: Python wrapper for AWS Rekognition API [script], 2020. Disponível em: https://github.com/schmokel/pykognition. Acesso em: 21 de junho de 2025.
SCHMØKEL, R.; BOSSETTA, M. FBAdLibrarian and Pykognition: open science tools for the collection and emotion detection of images in Facebook political ads with computer vision. Journal of Information Technology & Politics, [s. l.], v. 19, n. 1, p. 118-128, 2022. https://doi.org/10.1080/19331681.2021.1928579
THORSEN, S.; ASTRUPGAARD, C. Bridging the computational and visual turn: re-tooling visual studies with image recognition and network analysis to study online climate images. Nordic Journal of Media Studies, [s. l.], v. 3, n. 1, p. 141-163, 2021. https://doi.org/10.2478/njms-2021-0008
VALERIO-UREÑA, G.; ROGERS, R. Characteristics of the Digital Content about Energy-Saving in Different Countries around the World. Sustainability, Basileia, v. 11, n. 17, p. 1-14, 2019. https://doi.org/10.3390/su11174704
VALERIO-UREÑA, G. et al. A digital look at the fracking controversy in Mexico. Espacios,
Caracas, v. 40, n. 34, p. 1-9, 2019. Disponível em: https://repositorio.tec.mx/handle/11285/636058. Acesso em: 21 de junho de 2025.
VISUAL COMPUTING GROUP. Image sorter [software]. 2018. Disponível em: https://visual-computing.com/project/imagesorter/. Acesso em: 21 de junho de 2025.
WALLER, L.; GUGGANIG, M. Re-visioning public engagement with emerging technology: a digital methods experiment on ‘vertical farming’. Public Understanding of Science, [s. l.], v. 30, n. 5, p. 588-604, 2021. https://doi.org/https://doi.org/10.1177/0963662521990977
WILLIAMS, N. W.; CASAS, A.; WILKERSON, J. D. Images as data for social science research: an introduction to convolutional neural nets for image classification. Cambridge: Cambridge University Press, 2020.
WITT, A.; SUZOR, N.; HUGGINS, A. The rule of law on Instagram: an evaluation of the moderation of images depicting women's bodies. UNSW Law Journal, [s. l.], v. 42, n. 2, p. 557-596, 2019. https://doi.org/10.53637/ipmc9544
Downloads
Publicado
Edição
Secção
Licença
Direitos de Autor (c) 2025 Eduardo Vasconcelos
Este trabalho encontra-se publicado com a Licença Internacional Creative Commons Atribuição 4.0.
Os autores retêm os direitos autorais e concedem à revista o direito de publicar o seu trabalho pela primeira vez sob a licença Creative Commons (CC-BY), que permite o intercâmbio de obras e reconhecimento de autoria na revista.
