Making big data, in theory

Boellstorff, Tom

doi:10.5210/fm.v18i10.4869

Cited by 141 publications

(120 citation statements)

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“…"Social data" são os dados extraídos das mídias sociais num formato legível para computadores, sendo complementado por metadata, de forma a fornecer não apenas o conteúdo, mas o contexto do dado. Metadata é a inclusão de certos elementos de dado de apoio em relação a um dado específico, como informação sobre localização, engajamento e links (BOELLSTORFF, 2013).…”

Section: Big Data E Análise De Sentimento Nas Redes Sociais: Debate Tunclassified

O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

2017

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Resumo Este artigo se propõe a ampliar a metodologia perspectivista (MALINI, 2016) de análise de redes sociais, incorporando um procedimento de análise dos sentimentos das mensagens postadas em redes de controvérsias políticas, em particular, em dois momentos distintos da campanha pelo impeachment da presidenta Dilma. O primeiro é o período da eclosão das manifestações antipetistas, no dia 15 de março de 2015. O segundo, dia 27 de agosto de 2016, quando a presidenta é deposta do cargo. Realiza uma revisão sobre a análise de sentimentos em megadados do Twitter e constrói uma metodologia que combina classificação humana de textos com aplicação de algoritmos genéticos de análise de textos, no intuito de analisar sentimentos genéricos (baseado na polarização positivo/negativos) e sentimento específicos, baseados nas seguintes emoções: Alegria, Raiva, Medo, Antecipação, Desgosto, Tristeza, Surpresa e Confiança. Conclui demonstrando que os movimentos pró e anti-Dilma são marcados pelo predomínio de sentimento de raiva, medo e ansiedade, confirmando a hipótese que a trolagem ofensiva demarca o estilo da indignação propagada em redes políticas no Twitter brasileiro. Palavras-Chave: Análise de Sentimento; Big Data; Redes; Política; Twitter.Abstract This article aims to expand the perspectivist methodology (Malini, 2016) of social networks analysis, incorporating a proceeding of sentiment analysis of the messages posted in networks of political controversies, in particular, in two distinct moments of the campaign for the impeachment of President Dilma. The first is the period of the outbreak of PT protests, on March 15, 2015. The second, on August 27, 2016, when the president is deposed. We will be doing a theoretical review about sentiment analysis in Big Data on Twitter to build a methodology that combines human classification of texts with the application of genetic algorithms of text analysis and to analyze generic sentiments (based on positive / negative polarization) and specific sentiment, based on emotions like Joy, Anger, Fear, Anticipation, Disgust, Sadness, Surprise and Trust. It concludes by demonstrating that pro and anti-Dilma movements are marked by a predominance of anger, fear and anxiety, confirming the hypothesis that an offensive trolling demarcates the style of indignation propagated by political networks in Brazilian Twitter.Keywords: Sentiment Analysis; Big Data; Social Network; Politics; Twitter.

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Section: Big Data E Análise De Sentimento Nas Redes Sociais: Debate Tunclassified

O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

2017

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“…Due to its real time nature, and rich information enabled by new technologies, Big Data science has potential to offer a higher form of intelligence and knowledge with the aura of truth, objectivity, and accuracy [61,62]. Currently, there is a good understanding that addressing researcher's subjectivity with Big Data sciences could make research more scientific, robust, and ethical.…”

Section: Big Data Science Debates Challenges and Opportunitiesmentioning

confidence: 99%

Big Data Science and its Applications in Healthcare and Medical Research: Challenges and Opportunities

Liang¹

2016

Austin Biom and Biostat

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Recently, Big Data science has been a hot topic in the scientific, industrial and the business worlds. The healthcare and biomedical sciences have rapidly become data-intensive as investigators are generating and using large, complex, high dimensional and diverse domain specific datasets. This paper provides a general survey of recent progress and advances in Big Data science, healthcare, and biomedical research. Big Data science impacts, important features, infrastructures, and basic and advanced analytical tools are presented in detail. Additionally, various challenges, debates, and opportunities inside this quickly emerging scientific field are explored. The human genome and omics research, one of the most promising medical and health areas as an example and application of Big Data science, is discussed to demonstrate how the adaptive advanced computational analytical tools could be utilized for transforming millions of data points into predictions and diagnostics for precision medicine and personalized healthcare with better patient outcomes.

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“…Humanist critiques of big data acknowledge that coming to the world through correlation has merit if the overall purpose is acting in the world, but point out that the cost is 'a massive reduction of what it means to Bknow^' (Bowker 2014). Moreover, the imaginaries that arise alongside big data place all their analytical value on the idea of anticipation, in part, because they are 'shaped by positivist traditions that equate scientific value with predictive laws' (Boellstorff 2013).…”

Section: The Problem With Data Sciencementioning

confidence: 99%

Data Science as Machinic Neoplatonism

McQuillan

2017

Philos. Technol.

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Data science is not simply a method but an organising idea. Commitment to the new paradigm overrides concerns caused by collateral damage, and only a counterculture can constitute an effective critique. Understanding data science requires an appreciation of what algorithms actually do; in particular, how machine learning learns. The resulting 'insight through opacity' drives the observable problems of algorithmic discrimination and the evasion of due process. But attempts to stem the tide have not grasped the nature of data science as both metaphysical and machinic. Data science strongly echoes the neoplatonism that informed the early science of Copernicus and Galileo. It appears to reveal a hidden mathematical order in the world that is superior to our direct experience. The new symmetry of these orderings is more compelling than the actual results. Data science does not only make possible a new way of knowing but acts directly on it; by converting predictions to pre-emptions, it becomes a machinic metaphysics. The people enrolled in this apparatus risk an abstraction of accountability and the production of 'thoughtlessness'. Susceptibility to data science can be contested through critiques of science, especially standpoint theory, which opposes the 'view from nowhere' without abandoning the empirical methods. But a counterculture of data science must be material as well as discursive. Karen Barad's idea of agential realism can reconfigure data science to produce both non-dualistic philosophy and participatory agency. An example of relevant praxis points to the real possibility of 'machine learning for the people'.

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Making big data, in theory

Cited by 141 publications

References 0 publications

O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

O sentimento político em redes sociais: big data, algoritmos e as emoções nos tweets sobre o impeachment de Dilma Rousseff | Political sentiment in social networks: big data, algorithims and emotions in tweets about the impeachment of Dilma Rousseff

Big Data Science and its Applications in Healthcare and Medical Research: Challenges and Opportunities

Data Science as Machinic Neoplatonism

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