Literatur Digitale Methoden
Allgemein
- Cao, N., & Cui, W. (2016). Introduction to text visualization. Atlantis briefs in artificial intelligence: Vol. 1. Paris: Atlantis Press. http://dx.doi.org/10.2991/978-94-6239-186-4
- Günther, E., Trilling, D., & Van de Velde, R.N. (2018). But how do we store it? (Big) data architecture in the social-scientific research process. In: Stuetzer, C.M., Welker, M., & Egger, M. (eds.): Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications. Cologne, Germany: Herbert von Halem.
- Ignatow, Gabe; Mihalcea, Rada F. (2017): Text mining. A guidebook for the social sciences. Los Angeles: SAGE.
- Ignatow, G., & Mihalcea, R. F. (2018). An introduction to text mining: Research design, data collection, and analysis. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
- Jünger, J. (2018): Mapping the Field of Automated Data Collection on the Web. Data Types, Collection Approaches and their Research Logic. In: Stützer, Cathleen / Welker, Martin / Egger, Marc (Hg). Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications. Neue Schriften zur Online-Forschung der Deutschen Gesellschaft für Online-Forschung (DGOF). Köln: Halem-Verlag, S. 104-130.
- Manderscheid, Katharina (2019): Text Mining. In Nina Baur & Jörg Blasius (Hrsg.), Handbuch Methoden der empirischen Sozialforschung. Wiesbaden: Springer Fachmedien Wiesbaden, 1103-1116.
- Munzert, Simon; Rubba, Christian; Meißner, Peter; Nyhius, Dominic (2015): Automated Data Collection with R. A Practical Guide to Web Scraping and Text Mining, Chichester: Wiley.
- Salganik, M. J. (2018). Bit by bit: Social research in the digital age.
- Sean Kross, Nick Carchedi, Bill Bauer and Gina Grdina (2017). swirl: Learn R, in R. R package version 2.4.3.
- Silge, Julia; Robinson, David (2017): Text mining with R. A tidy approach. First edition. Beijing: O‘Reilly.
- Welbers, Kasper; van Atteveldt, Wouter; Benoit, Kenneth (2017): Text Analysis in R. In: Communication Methods and Measures 11 (4), S. 245-265.
- Wiedemann, G., & Lemke, M. (2016). Text Mining für die Analyse qualitativer Daten. Auf dem Weg zu einer Best Practise? In M. Lemke & G. Wiedemann (Eds.), Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse (pp. 397–419). Wiesbaden: Springer VS.
- Wieland, M., In der Au, A.M., Keller, C., Brunk, S., Bettermann, T., Hagen, L., & Schlegel, T. (2018). Online Behavior Tracking in Social Sciences: Quality Criteria and Technical Implementation. In C. M. Stützer & M. Welker (Eds.), Neue Schriften zur Online-Forschung: Vol. 15. Computational Social Science in the Age of Big Data. Concepts, Methodologies, Tools, and Applications. S. 131-160. Köln: Herbert v. Halem.
Recht
- Golla, S. J., Hofmann, H., & Bäcker, M. (2018). Connecting the Dots. Datenschutz Und Datensicherheit (DuD), 42(2), 89–100. https://doi.org/10.1007/s11623-018-0900-x
- Golla, S. J., v. Schönfeld, M. (2019). Kratzen und Schürfen im Datenmilieu – Web Scraping in sozialen Netzwerken zu wissenschaftlichen Forschungszwecken, in: Kommunikation & Recht(KR), 15-21
- Raue, B., (2017) Text und Data Mining. Die neue Urheberrechtsschranke des §60d UrhG. Computer und Recht(CR) 10/2017, 656-662
Weiterführendes:
- Blätte, A. (2018). Interaktion und Dialog mit großen Textdaten: Korpusanalyse mit dem „polmineR“. In A. Blätte, J. Behnke, K.-U. Schnapp, & C. Wagemann (Eds.), Computational Social Science: Die Analyse von Big Data (1st ed., pp. 119–138). Baden-Baden: Nomos Verlagsgesellschaft mbH & Co. KG. https://doi.org/10.5771/9783845286556-119
- Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Saint Louis, UNITED STATES: Elsevier Science & Technology.
- Ignatow, G., & Mihalcea, R. F. (2017). Text mining: A guidebook for the social sciences. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
- Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H. & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2-3), 93–118. https://doi.org/10.1080/19312458.2018.1430754
- Papilloud, C., & Hinneburg, A. (Eds.). (2018). Studienskripten zur Soziologie. Qualitative Textanalyse mit Topic-Modellen: Eine Einführung für Sozialwissenschaftler. Wiesbaden: Springer VS.
- Rogers, R. (2013). Digital methods. Cambridge, Massachusetts, London, England: The MIT Press.
- Roberts, M. E., Stewart, B. M., Tingley, D., Airoldi, E. M., & others (2013). The structural topic model and applied social science. In Advances in neural information processing systems workshop on topic models: computation, application, and evaluation (pp. 1–20).
- Rudkowsky E., Haselmayer M., Wastian M., Jenny M., Emrich Š., & Sedlmair M. (2018). More than Bags of Words: Sentiment Analysis with Word Embeddings. Communication Methods and Measures, 12(2-3), 140–157. https://doi.org/10.1080/19312458.2018.1455817
- Stulpe, A., & Lemke, M. (2016). Blended Reading. In M. Lemke & G. Wiedemann (Eds.), Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse (pp. 17–61). Wiesbaden: Springer VS.
- Welker, M. (2018). Computer- und onlinegestützte Methoden für die Untersuchung digitaler Kommunikation. In W. Schweiger & K. Beck (Eds.), Handbuch Online-Kommunikation (pp. 1–43). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-18017-1_21-1
- Yadollahi, A., Shahraki, A. G., & Zaiane, O. R. (2017). Current State of Text Sentiment Analysis from Opinion to Emotion Mining. ACM Computing Surveys, 50(2), 1–33. https://doi.org/10.1145/3057270
Weiterführendes
- Blätte, A. (2018). Interaktion und Dialog mit großen Textdaten: Korpusanalyse mit dem „polmineR“. In A. Blätte, J. Behnke, K.-U. Schnapp, & C. Wagemann (Eds.), Computational Social Science: Die Analyse von Big Data (1st ed., pp. 119–138). Baden-Baden: Nomos Verlagsgesellschaft mbH & Co. KG. https://doi.org/10.5771/9783845286556-119
- Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. Saint Louis, UNITED STATES: Elsevier Science & Technology.
- Ignatow, G., & Mihalcea, R. F. (2017). Text mining: A guidebook for the social sciences. Los Angeles, London, New Delhi, Singapore, Washington DC, Melbourne: Sage.
- Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Häussler, T., Schmid-Petri, H. & Adam, S. (2018). Applying LDA Topic Modeling in Communication Research: Toward a Valid and Reliable Methodology. Communication Methods and Measures, 12(2-3), 93–118. https://doi.org/10.1080/19312458.2018.1430754
- Papilloud, C., & Hinneburg, A. (Eds.). (2018). Studienskripten zur Soziologie. Qualitative Textanalyse mit Topic-Modellen: Eine Einführung für Sozialwissenschaftler. Wiesbaden: Springer VS.
- Rogers, R. (2013). Digital methods. Cambridge, Massachusetts, London, England: The MIT Press.
- Roberts, M. E., Stewart, B. M., Tingley, D., Airoldi, E. M., & others (2013). The structural topic model and applied social science. In Advances in neural information processing systems workshop on topic models: computation, application, and evaluation (pp. 1–20).
- Rudkowsky E., Haselmayer M., Wastian M., Jenny M., Emrich Š., & Sedlmair M. (2018). More than Bags of Words: Sentiment Analysis with Word Embeddings. Communication Methods and Measures, 12(2-3), 140–157. https://doi.org/10.1080/19312458.2018.1455817
- Stulpe, A., & Lemke, M. (2016). Blended Reading. In M. Lemke & G. Wiedemann (Eds.), Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse (pp. 17–61). Wiesbaden: Springer VS.
- Welker, M. (2018). Computer- und onlinegestützte Methoden für die Untersuchung digitaler Kommunikation. In W. Schweiger & K. Beck (Eds.), Handbuch Online-Kommunikation (pp. 1–43). Wiesbaden: Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-18017-1_21-1
- Yadollahi, A., Shahraki, A. G., & Zaiane, O. R. (2017). Current State of Text Sentiment Analysis from Opinion to Emotion Mining. ACM Computing Surveys, 50(2), 1–33. https://doi.org/10.1145/3057270
Software-Tools
- Evert, S., & Hardie, A. (2011). Twenty-first century Corpus Workbench: Updating a query architecture for the new millennium.
- Ferreira, D., Kostakos, V., & Dey, A. K. (2015). AWARE: mobile context instrumentation framework. Frontiers in ICT, 2, 6.
- Menchen-Trevino, E. (2016, March). Web Historian: Enabling multi-method and independent research with real-world web browsing history data. In X. Lin & M. Khoo (Eds.), iConference 2016 Proceedings. iSchools. https://doi.org/10.9776/16611
- Niekler, A., Bleier, A., Kahmann, C., Posch, L., Wiedemann, G., Erdogan, K., Heyer, G., & Strohmaier, M. (2018). ILCM - A Virtual Research Infrastructure for Large-Scale Qualitative Data. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC-2018). European Language Resource Association. Retrieved from http://aclweb.org/anthology/L18-1209