@misc{BrendelMatznerMenzel2021,
  author    = {Brendel, Nina and Matzner, Nils and Menzel, Max-Peter},
  title     = {Geographisches Gezwitscher - Analyse von Twitter-Daten als Methode im GW-Unterricht},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-55061},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-550614},
  pages     = {72 -- 85},
  year      = {2021},
  abstract  = {Soziale Medien sind ein wesentlicher Bestandteil des Alltags von Sch{\"u}ler*innen und gleichzeitig zunehmend wichtig in Wirtschaft, Politik und Wissenschaft. Am Beispiel von Twitter zeigt dieser Beitrag, dass soziale Medien im Unterricht auch f{\"u}r die Beantwortung geographischer Fragestellungen verwendet werden k{\"o}nnen. Hierf{\"u}r eignen sich Twitter-Daten aufgrund ihrer Georeferenzierung und weiterer interessanter Inhalte besonders. Der Beitrag gibt einen {\"U}berblick {\"u}ber die Verwendung von Twitter f{\"u}r sozialwissenschaftliche und humangeographische Fragestellungen und reflektiert die Nutzung von Twitter im Unterricht. F{\"u}r die Unterrichtspraxis werden Beispiele zu den Themen Braunkohle, Flutereignisse und Raumwahrnehmungen sowie Anleitungen zur Auswertung, Anwendung und Reflexion von Twitter-Analysen vorgestellt.},
  language  = {de}
}
@phdthesis{Risch2020,
  author    = {Risch, Julian},
  title     = {Reader comment analysis on online news platforms},
  doi       = {10.25932/publishup-48922},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-489222},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 135},
  year      = {2020},
  abstract  = {Comment sections of online news platforms are an essential space to express opinions and discuss political topics. However, the misuse by spammers, haters, and trolls raises doubts about whether the benefits justify the costs of the time-consuming content moderation. As a consequence, many platforms limited or even shut down comment sections completely. In this thesis, we present deep learning approaches for comment classification, recommendation, and prediction to foster respectful and engaging online discussions. The main focus is on two kinds of comments: toxic comments, which make readers leave a discussion, and engaging comments, which make readers join a discussion. First, we discourage and remove toxic comments, e.g., insults or threats. To this end, we present a semi-automatic comment moderation process, which is based on fine-grained text classification models and supports moderators. Our experiments demonstrate that data augmentation, transfer learning, and ensemble learning allow training robust classifiers even on small datasets. To establish trust in the machine-learned models, we reveal which input features are decisive for their output with attribution-based explanation methods. Second, we encourage and highlight engaging comments, e.g., serious questions or factual statements. We automatically identify the most engaging comments, so that readers need not scroll through thousands of comments to find them. The model training process builds on upvotes and replies as a measure of reader engagement. We also identify comments that address the article authors or are otherwise relevant to them to support interactions between journalists and their readership. Taking into account the readers' interests, we further provide personalized recommendations of discussions that align with their favored topics or involve frequent co-commenters. Our models outperform multiple baselines and recent related work in experiments on comment datasets from different platforms.},
  language  = {en}
}