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dc.contributor.authorWiedemann, Gregor
dc.contributor.authorFedtke, Cornelia
dc.date.accessioned2022-12-06T10:27:37Z
dc.date.available2022-12-06T10:27:37Z
dc.date.issued2022
dc.identifier.urihttps://library.oapen.org/handle/20.500.12657/59856
dc.description.abstractText, 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.en_US
dc.languageEnglishen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JM Psychologyen_US
dc.subject.classificationthema EDItEUR::J Society and Social Sciences::JM Psychology::JMB Psychological methodologyen_US
dc.subject.othersurvey 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 dataen_US
dc.titleChapter 21 From Frequency Counts to Contextualized Word Embeddingsen_US
dc.title.alternativeThe Saussurean turn in automatic content analysisen_US
dc.typechapter
oapen.identifier.doi10.4324/9781003025245-25en_US
oapen.relation.isPublishedBy7b3c7b10-5b1e-40b3-860e-c6dd5197f0bben_US
oapen.relation.isPartOfBook866251e4-af21-49fd-a795-9950f3c15530en_US
oapen.relation.isbn9780367457808en_US
oapen.relation.isbn9781032077703en_US
oapen.imprintRoutledgeen_US
oapen.pages21en_US
oapen.remark.publicFunder name: Johannes Kepler University Linz, Institute of Sociology


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