Métodos Digitais Para Análise de Imagens

Uma Revisão Sistemática

Auteurs

  • Eduardo Vasconcelos Universidade Federal da Bahia

DOI :

https://doi.org/10.22409/73z8th51

Mots-clés :

Métodos Digitais, Análise de Imagens, Imagens Digitais, Revisão Sistemática

Résumé

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.

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Références

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

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Publiée

2025-12-22