Keyword: machine learning

  • Springer, N. & Haim, M. (9/2020). Maschinelles Lernen in der Journalismusforschung: Rechtliche, ethische und praktische Herausforderungen eines interdisziplin√§ren Lehr-/Forschungsprojekts. [Machine learning in journalism studies: Legal, ethical, and practical challenges in interdisciplinary teaching and research.] Presented at the Presented at the Annual Conference of the Journalism Division of the DGPuK, Hamburg.

  • Haim, M. (2019). Die Orientierung von Online-Journalismus an seinen Publika. Anforderung, Antizipation, Anspruch. [The orientation of online journalism toward its audiences. Demand, anticipation, claim.] Wiesbaden: Springer VS. https://doi.org/10.1007/978-3-658-25546-6.

  • Haim, M., Heinzel, I., Lankheit, S., Niagu, A.-M., & Springer, N. (5/2019). Identifying the good and the bad: Using machine learning to moderate user commentary on news. Presented at the 69th Annual Conference of the International Communication Association, Washington D.C.

  • Springer, N. & Haim, M. (11/2018). Machines in moderation: A theoretical framework for the application of machine learning in the management of user commentary. Presented at the 7th Annual ECREA Conference, Lugano.