Skip to content
Snippets Groups Projects

Changes

Page history
  • New page
  • Templates
    • Fixed citation authored by Gallenkamp, Fabian's avatar Gallenkamp, Fabian
    Hide whitespace changes
    Inline Side-by-side
    Showing
    with 4 additions and 0 deletions
    MethodsList.asciidoc
    View page @ 2ebaf02c
    ......@@ -283,6 +283,8 @@ Using methods to predict the future for estimation of current values. (Example:
    - [[[Jünger2018]]] Jünger, Jakob (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.
    - [[[Lemke_etal2016]]] Lemke, M., & Wiedemann, G. (Eds.). (2016). Text Mining in den Sozialwissenschaften: Grundlagen und Anwendungen zwischen qualitativer und quantitativer Diskursanalyse. Wiesbaden: Springer VS.
    - [[[Maier_etal2018]]] Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., . . . 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
    - [[[Niekler_etal2018]]] Niekler, A., Bleier, A., Kahmann, C., Posch, L., Wiedemann, G., Erdogan, K., . . . 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
    ......@@ -297,4 +299,6 @@ Using methods to predict the future for estimation of current values. (Example:
    - [[[Stab_etal2018]]] Stab, C., Daxenberger, J., Stahlhut, C., Miller, T., Schiller, B., Tauchmann, C., . . . Gurevych, I. (2018). ArgumenText: Searching for Arguments in Heterogeneous Sources. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations (pp. 21–25).
    - [[[Stulpe_etal2016]]] Stulpe, A., & Lemke, M. (2016). Blended Reading. In Text Mining in den Sozialwissenschaften (pp. 17–61). Springer.
    - [[[Wickham_etal2017]]] Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, tidy, transform, visualize, and model data. Beijing, Boston, Farnham, Sebastopol, Tokyo: O’Reilly UK Ltd.