Keyword: online comments

  • Haim, M. & Hoven, E. (2022). Hate speech's double damage: A semi-automated approach toward direct and indirect targets. Journal of Quantitative Description: Digital Media, 2, 1-37.

  • Haim, M. (7/2021). Welcher Hass? Inhaltsanalyse politisch ausgerichteter Kommentare bei Online-Medien, Twitter und YouTube. [Which hate? Content analysis of political comments in online media, Twitter, and YouTube.] Invited presentation at "Das Phänomen 'Digitaler Hass' – ein interdisziplinärer Blick auf Ursachen, Erscheinungsformen und Auswirkungen", Leipzig.

  • Haim, M. & Maurus, K. (2021). Stereotypes and sexism? Effects of gender, topic, and user comments on journalists' credibility. Journalism, Advance Online Publication.

  • 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.

  • Haim, M. (11/2018). Demokratie im Netz: Fake News, Hass-Postings, Wahlmanipulation? [Democracy online: Fake news, hatespeech, election manipulation?] Invited presentation at Kodex-E, VVG, Dornbirn.

  • 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.

  • Springer, N. & Haim, M. (3/2018). Identifying the good and the bad: How machine learning is applied and applicable in the moderation of user comments on news. Invited presentation at ThursdAI, MIT Media Lab, Cambridge, MA.