Datenquellen

Mögliche Datenquellen

Praktische Anleitungen

  • Bryce Boe. 2017. PRAW: The Python Reddit Api Wrapper. Read the Docs. https://praw.readthedocs.io/en/v6.4.0/.
  • Michael Bukachi. 2019. “Extracting YouTube Comments with YouTube API & Python”. Gotrained Python Tutorials. https://python.gotrained.com/youtubeapi-extracting-comments/.
  • Matthew J. Connelly u. a. 2019. Diplomatic Documents Data for International Relations: The Computational and Historical Resources on Nations and Organizations for the Social Sciences (CHRONOS) Database. Working Paper. https : //www.nyu.edu/projects/spirling/documents/Chronos_ppr.pdf.
  • Jonathan D. Fitzgerald. 2018. “Working with The New York Times API in R”. Storybench. http://www.storybench.org/working-with-the-new-yorktimes-api-in-r/.
  • Pascal Jürgens und Andreas Jungherr. 2016a. A Tutorial for Using Twitter-Data in the Social Sciences: Data Collection, Preparation, and Analysis. Social Science Research Network (SSRN). doi:10.2139/ssrn.2710146. http://ssrn.com/abstract= 2710146.
  • Dana Lindquist. 2019. “Using New York Times API and jq to collect news data”. Medium. https://medium.com/@danalindquist/using-new-york-timesapi-and-jq-to-collect-news-data-a5f386c7237b.
  • Jörg Matthes und Matthias Kohring. 2008. “The Content Analysis of Media Frames: Toward Improving Reliability and Validity”. Journal of Communication 58 (2): 258–279. doi:10.1111/j.1460-2466.2008.00384.x.
  • Christian Rauh, Pieter De Wilde und Jan Schwalbach. 2017. The ParlSpeech data set: Annotated full-text vectors of 3.9 million plenary speeches in the key legislative chambers of seven European states. Cambridge: Harvard Dataverse. doi:10.7910/DVN/E4RSP9.
  • Felippe Rodrigues. 2018. “How to scrape Reddit with Python”. Storybench. http://www.storybench.org/how-to-scrape-reddit-with-python/.
  • Matthew A. Russell. 2019. Mining the Social Web. 3. Aufl. Sebastopol: O’Reilly Media.
  • Zachary C. Steinert-Threkeld. 2018. Twitter as Data. Cambridge: Cambridge University Press.