@article{lapesa20:_analy_polit_debat_newsp_repor,
  abstract = {Discourse network analysis is an aspiring development in political science which analyzes political debates in terms of bipartite actor/claim networks. It aims at understanding the structure and temporal dynamics of major political debates as instances of politicized democratic decision making. We discuss how such networks can be constructed on the basis of large collections of unstructured text, namely newspaper reports. We sketch a hybrid methodology of manual analysis by domain experts complemented by machine learning and exemplify it on the case study of the German public debate on immigration in the year 2015. The first half of our article sketches the conceptual building blocks of discourse network analysis and demonstrates its application. The second half discusses the potential of the application of NLP methods to support the creation of discourse network datasets.},
  added-at = {2020-05-29T15:45:59.000+0200},
  author = {Lapesa, Gabriella and Blessing, Andre and Blokker, Nico and Dayanik, Erenay and Haunss, Sebastian and Kuhn, Jonas and Padó, Sebastian},
  biburl = {https://puma.ub.uni-stuttgart.de/bibtex/24226ed780f206d3d17058c3482f81bf1/sp},
  interhash = {cfd5940a96a17ad172311fe643cff81b},
  intrahash = {4226ed780f206d3d17058c3482f81bf1},
  journal = {Datenbank-Spektrum},
  keywords = {article myown},
  number = 2,
  timestamp = {2022-08-17T06:13:41.000+0200},
  title = {Analysis of Political Debates through Newspaper Reports: Methods and Outcomes},
  url = {http://dx.doi.org/10.1007/s13222-020-00344-w},
  volume = 20,
  year = 2020
}