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oapen-20.500.12657-598562022-12-07T12:47:51Z Chapter 21 From Frequency Counts to Contextualized Word Embeddings Wiedemann, Gregor Fedtke, Cornelia survey data, data analysis, data science, information technology, AI, socio-robotics, quantitative, survey methodology, ethics, ethical standards, privacy, replication, politics, survey design, social media, big data, social, human-robot interaction, machine learning, open data, data archives, data ownership, digital trace, unstructured data bic Book Industry Communication::J Society & social sciences::JM Psychology bic Book Industry Communication::J Society & social sciences::JM Psychology::JMB Psychological methodology Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categorization (coding) of longer text sequences, qualitative social researchers put more emphasis on systematic ways to generate a deep understanding of social phenomena from text. For the latter, several qualitative research methods such as qualitative content analysis (Mayring, 2010), grounded theory methodology (Glaser & Strauss, 2005), and (critical) discourse analysis (Foucault, 1982) have been developed. Although their methodological foundations differ widely, both currents of empirical research need to rely to some extent on the interpretation of text data against the background of its context. At the latest with the global expansion of the internet in the digital era and the emergence of social networks, the huge mass of text data poses a significant problem to empirical research relying on human interpretation. For their studies, social scientists have access to newspaper texts representing public media discourse, web documents from companies, parties, or NGO websites, political documents from legislative processes such as parliamentary protocols, bills and corresponding press releases, and for some years now micro-posts and user comments from social media. Computational support is inevitable even to process samples of such document volumes that could easily comprise millions of documents. 2022-12-06T10:27:37Z 2022-12-06T10:27:37Z 2022 chapter 9780367457808 9781032077703 https://library.oapen.org/handle/20.500.12657/59856 eng application/pdf Attribution-NonCommercial-NoDerivatives 4.0 International 9781003025245_10.4324_9781003025245-25.pdf Taylor & Francis Handbook of Computational Social Science, Volume 2 Routledge 10.4324/9781003025245-25 10.4324/9781003025245-25 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb 866251e4-af21-49fd-a795-9950f3c15530 9780367457808 9781032077703 Routledge 21 open access
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Text, the written representation of human thought and communication in natural language, has been a major source of data for social science research since its early beginnings. While quantitative approaches seek to make certain contents measurable, for example through word counts or reliable categorization (coding) of longer text sequences, qualitative social researchers put more emphasis on systematic ways to generate a deep understanding of social phenomena from text. For the latter, several qualitative research methods such as qualitative content analysis (Mayring, 2010), grounded theory methodology (Glaser & Strauss, 2005), and (critical) discourse analysis (Foucault, 1982) have been developed. Although their methodological foundations differ widely, both currents of empirical research need to rely to some extent on the interpretation of text data against the background of its context. At the latest with the global expansion of the internet in the digital era and the emergence of social networks, the huge mass of text data poses a significant problem to empirical research relying on human interpretation. For their studies, social scientists have access to newspaper texts representing public media discourse, web documents from companies, parties, or NGO websites, political documents from legislative processes such as parliamentary protocols, bills and corresponding press releases, and for some years now micro-posts and user comments from social media. Computational support is inevitable even to process samples of such document volumes that could easily comprise millions of documents.
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