Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online

Detalhes bibliográficos
Ano de defesa: 2019
Autor(a) principal: André Goes Mintz
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: por
Instituição de defesa: Universidade Federal de Minas Gerais
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: https://hdl.handle.net/1843/31956
Resumo: This research seeks to contribute do the field of Digital Methods for studies in Communication and Media by focusing, specifically, the issue of computational analysis of images through machine learning techniques. Aiming to overcome operational methodological perspectives which turn to quantitative approaches, this study proposes to reflect upon how digital images are theoretically considered in those efforts and how computational methods conform particular modes of seeing. The theoretical framework is mainly derived from Science and Technology Studies (STS) and, especially, Actor-Network Theory (ANT). Through concepts from these domains, images are understood as sociotechnical inscriptions, in a condition of ontological multiplicity. An uncertainty is therefore assumed regarding the possibility of treating images individually and, in contrast, it is proposed to approach them as effects of distributed relational materialities. These are central aspects of the conceptual hypothesis of the image-network, which is proposed by this thesis. This formulation is articulated to an effort of describing the operation of machine learning image recognition techniques based on artificial neural networks. Considering the relations among these models and large training data sets harvested from the internet, as well as their infrastructuralization tendencies, it is considered that they are important components of the contemporary visual field, generating computational visualitites with strong participation in datafication and algorithmic mediation processes to which images are subjected in online platforms. The application of these techniques as methodological resources is proposed, therefore, as a critical repurposing which considers methods as integral parts of the objects under scrutiny. This approach is exercised in a case study focusing on images published on Twitter during a media event. In a confluence of the theoretical and methodological discussions, a methodological device named Atlas for Image-Networks is finally proposed. It seeks to afford conditions for heuristic navigational practices through the images, while also attempting to preserve the ontological multiplicity of their instantiations.
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spelling 2020-01-17T11:21:48Z2025-09-08T23:40:42Z2020-01-17T11:21:48Z2019-11-22Tecnologiahttps://hdl.handle.net/1843/31956This research seeks to contribute do the field of Digital Methods for studies in Communication and Media by focusing, specifically, the issue of computational analysis of images through machine learning techniques. Aiming to overcome operational methodological perspectives which turn to quantitative approaches, this study proposes to reflect upon how digital images are theoretically considered in those efforts and how computational methods conform particular modes of seeing. The theoretical framework is mainly derived from Science and Technology Studies (STS) and, especially, Actor-Network Theory (ANT). Through concepts from these domains, images are understood as sociotechnical inscriptions, in a condition of ontological multiplicity. An uncertainty is therefore assumed regarding the possibility of treating images individually and, in contrast, it is proposed to approach them as effects of distributed relational materialities. These are central aspects of the conceptual hypothesis of the image-network, which is proposed by this thesis. This formulation is articulated to an effort of describing the operation of machine learning image recognition techniques based on artificial neural networks. Considering the relations among these models and large training data sets harvested from the internet, as well as their infrastructuralization tendencies, it is considered that they are important components of the contemporary visual field, generating computational visualitites with strong participation in datafication and algorithmic mediation processes to which images are subjected in online platforms. The application of these techniques as methodological resources is proposed, therefore, as a critical repurposing which considers methods as integral parts of the objects under scrutiny. This approach is exercised in a case study focusing on images published on Twitter during a media event. In a confluence of the theoretical and methodological discussions, a methodological device named Atlas for Image-Networks is finally proposed. It seeks to afford conditions for heuristic navigational practices through the images, while also attempting to preserve the ontological multiplicity of their instantiations.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorporUniversidade Federal de Minas Geraishttp://creativecommons.org/licenses/by-nc-nd/3.0/pt/info:eu-repo/semantics/openAccessImagemMétodos digitaisSTSAprendizado de máquinaVisão computacionalComunicaçãoMáquinasVisualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas onlineComputational visualities and the image-network: repurposing machine learning for studying images on online platformsinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisAndré Goes Mintzreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGhttp://lattes.cnpq.br/1784057827010257Carlos Frederico de Brito d'Andréahttp://lattes.cnpq.br/0283817427921969Gabriel Menotti Miglio Pinto GonringPatrícia Moran FernandesCarlos Henrique Rezende FalciEduardo Antônio de JesusEsta pesquisa visa contribuir ao campo dos Métodos Digitais para estudos em Comunicação e Mídia, enfocando especificamente o problema da análise computacional de imagens por técnicas de aprendizado de máquina. Visando superar perspectivas metodológicas operacionais que se voltam a abordagens quantitativas, este estudo propõe refletir sobre o tratamento teórico concedido às imagens digitais nessas investigações e sobre como métodos computacionais conformam modos de ver particulares. O referencial teórico ampara-se principalmente nos Estudos de Ciência e Tecnologia (STS) e, em especial, na Teoria Ator-Rede (TAR). Por meio de conceitos desses domínios, as imagens são compreendidas como inscrições sociotécnicas, em uma condição de multiplicidade ontológica. Assume-se, portanto, a incerteza quanto à possibilidade de tratamento individual da imagem, considerando-a, de outro modo, como efeito de materialidades relacionais distribuídas. Estes são aspectos centrais da hipótese conceitual da imagem-rede, proposta por esta tese. Esta formulação é articulada a um esforço de descrição da operação de técnicas de reconhecimento de imagem por aprendizado de máquina baseadas em redes neurais artificiais. Em vista da vinculação desses modelos a amplas bases de treinamento colhidas da internet, bem como de sua tendência de infraestruturalização, considera-se que eles seriam parte importante do campo visual contemporâneo, engendrando visualidades computacionais com forte participação em processos de datificação e mediação algorítmica das imagens em plataformas online. A aplicação dessas técnicas como recursos metodológicos é proposta, portanto, como uma reapropriação crítica que considera os métodos como partes integrantes do objeto investigado. Esse gesto é exercitado em um estudo de caso voltado a imagens publicadas no Twitter durante um evento midiático. Em uma confluência das discussões teórico-metodológicas elaboradas ao longo do trabalho, propõe-se, ao final, um dispositivo metodológico denominado Atlas para Imagens-Redes. Ele visa proporcionar condições para práticas de navegação heurística pelas imagens, enquanto também busca resguardar a multiplicidade ontológica de suas instanciações.0000-0003-2740-3224BrasilFAFICH - FACULDADE DE FILOSOFIA E CIENCIAS HUMANASPrograma de Pós-Graduação em Comunicação SocialUFMGORIGINALVisualidades_computacionais_e_a_imagem-rede.pdfapplication/pdf18362761https://repositorio.ufmg.br//bitstreams/3a67e2ca-9d18-4a70-903e-8e5a5c4b633d/download34ea5ad6420833499e3404b38296b760MD51trueAnonymousREADCC-LICENSElicense_rdfapplication/octet-stream811https://repositorio.ufmg.br//bitstreams/f33985c3-8de6-477f-9513-ffb83c22bf97/downloadcfd6801dba008cb6adbd9838b81582abMD52falseAnonymousREADLICENSElicense.txttext/plain2119https://repositorio.ufmg.br//bitstreams/7ea9b228-a235-4eaa-8d8f-297728ea13ce/download34badce4be7e31e3adb4575ae96af679MD53falseAnonymousREADTEXTVisualidades_computacionais_e_a_imagem-rede.pdf.txttext/plain774221https://repositorio.ufmg.br//bitstreams/fdf27ae7-6d60-4660-975a-43f67499c784/download4bd58947cdf2481cc6393eed57c034ccMD54falseAnonymousREAD1843/319562025-09-08 20:40:42.592http://creativecommons.org/licenses/by-nc-nd/3.0/pt/Acesso Abertoopen.accessoai:repositorio.ufmg.br:1843/31956https://repositorio.ufmg.br/Repositório InstitucionalPUBhttps://repositorio.ufmg.br/oairepositorio@ufmg.bropendoar:2025-09-08T23:40:42Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)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
dc.title.none.fl_str_mv Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
dc.title.alternative.none.fl_str_mv Computational visualities and the image-network: repurposing machine learning for studying images on online platforms
title Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
spellingShingle Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
André Goes Mintz
Comunicação
Máquinas
Imagem
Métodos digitais
STS
Aprendizado de máquina
Visão computacional
title_short Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
title_full Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
title_fullStr Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
title_full_unstemmed Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
title_sort Visualidades computacionais e a imagem-rede: reapropriações do aprendizado de máquina para o estudo de imagens em plataformas online
author André Goes Mintz
author_facet André Goes Mintz
author_role author
dc.contributor.author.fl_str_mv André Goes Mintz
dc.subject.por.fl_str_mv Comunicação
Máquinas
topic Comunicação
Máquinas
Imagem
Métodos digitais
STS
Aprendizado de máquina
Visão computacional
dc.subject.other.none.fl_str_mv Imagem
Métodos digitais
STS
Aprendizado de máquina
Visão computacional
description This research seeks to contribute do the field of Digital Methods for studies in Communication and Media by focusing, specifically, the issue of computational analysis of images through machine learning techniques. Aiming to overcome operational methodological perspectives which turn to quantitative approaches, this study proposes to reflect upon how digital images are theoretically considered in those efforts and how computational methods conform particular modes of seeing. The theoretical framework is mainly derived from Science and Technology Studies (STS) and, especially, Actor-Network Theory (ANT). Through concepts from these domains, images are understood as sociotechnical inscriptions, in a condition of ontological multiplicity. An uncertainty is therefore assumed regarding the possibility of treating images individually and, in contrast, it is proposed to approach them as effects of distributed relational materialities. These are central aspects of the conceptual hypothesis of the image-network, which is proposed by this thesis. This formulation is articulated to an effort of describing the operation of machine learning image recognition techniques based on artificial neural networks. Considering the relations among these models and large training data sets harvested from the internet, as well as their infrastructuralization tendencies, it is considered that they are important components of the contemporary visual field, generating computational visualitites with strong participation in datafication and algorithmic mediation processes to which images are subjected in online platforms. The application of these techniques as methodological resources is proposed, therefore, as a critical repurposing which considers methods as integral parts of the objects under scrutiny. This approach is exercised in a case study focusing on images published on Twitter during a media event. In a confluence of the theoretical and methodological discussions, a methodological device named Atlas for Image-Networks is finally proposed. It seeks to afford conditions for heuristic navigational practices through the images, while also attempting to preserve the ontological multiplicity of their instantiations.
publishDate 2019
dc.date.issued.fl_str_mv 2019-11-22
dc.date.accessioned.fl_str_mv 2020-01-17T11:21:48Z
2025-09-08T23:40:42Z
dc.date.available.fl_str_mv 2020-01-17T11:21:48Z
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dc.identifier.other.none.fl_str_mv Tecnologia
identifier_str_mv Tecnologia
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