Peer-reviewed Journal Articles

(21) Rothenberger, L., & Hase, V. (2024). Biased Social Media Debates about Terrorism? A Content Analysis of Journalistic Coverage of and Audience Reactions to Terrorist Attacks on YouTube. Social Media + Society, 10(4). https://doi.org/10.1177/20563051241290113

(20) Hase, V., Ausloos, J., Boeschoten, L., Pfiffner, N., Janssen, H., Araujo, T., Carrière, T., de Vreese, C., Haßler, J., Loecherbach, F., Kmetty, Z., Möller, J., Ohme, J., Schmidbauer, E., Trilling, D., Welbers, K., & Haim, M. (2024). Fulfilling Data Access Obligations: How Could (and Should) Platforms Facilitate Data Donation Studies? Internet Policy Review, 13(3). https://doi.org/10.14763/2024.3.1793

(19) Hase, V., & Haim, M. (2024). Can We Get Rid of Bias? Mitigating Systematic Error in Data Donation Studies through Survey Design Strategies. Computational Communication Research, 6(2), 1–29. https://doi.org/10.5117/CCR2024.2.2.HASE

(18) TeBlunthuis, N., Hase, V., & Chan, C.-H. (2024). Misclassification in Automated Content Analysis Causes Bias in Regression. Can We Fix It? Yes We Can! Communication Measures and Methods, 18(3), 278–299. https://doi.org/10.1080/19312458.2023.2293713

(17) Haim, M., Hase, V., Schindler, J., Bachl, M., & Domahidi, E. (2023). (Re)Establishing Quality Criteria for Content Analysis: A Critical Perspective on the Field’s Core Method. SCM – Studies in Communication and Media, 12(4), 277–288. https://doi.org/10.5771/2192-4007-2023-4-277

(16) Haim, M., Leiner, D., & Hase, V. (2023). Integrating Data Donations in Online Surveys. Medien & Kommunikationswissenschaft, 71(1-2), 130-137. https://doi.org/10.5771/1615-634X-2023-1-2-130

(15) Hase, V., Boczek, K., & Scharkow, M. (2023). Adapting to Affordances and Audiences? A Cross-Platform, Multi-Modal Analysis of the Platformization of News on Facebook, Instagram, TikTok, and Twitter. Digital Journalism, 11(8), 1499–1520. https://doi.org/10.1080/21670811.2022.2128389

(14) Hase, V., Mahl, D., & Schäfer, M. S. (2023). The “Computational Turn”: An “Interdisciplinary Turn”? A Systematic Review of Text as Data Approaches in Journalism Studies. Online Media and Global Communication. https://doi.org/10.1515/omgc-2023-0003 [translated version of article 9]

(13) Hase, V. (2023). What is Terrorism (according to the News)? How the German Press Selectively Labels Political Violence as “Terrorism”. Journalism, 24(2), 398-417. https://doi.org/10.1177/14648849211017003

(12) Schäfer, M.S., & Hase, V. (2022). Computational Methods for The Analysis of Climate Change Communication: Towards an Integrative and Reflexive Approach. WIREs Climate Change, 14(2), e806. https://doi.org/10.1002/wcc.806

(11) Hase, V., & Engelke, K. (2022). Emotions in Crisis Coverage: How UK News Media Used Fear Appeals to Report on the Coronavirus Crisis. Journalism and Media, 3(4), 633–649. https://doi.org/10.3390/journalmedia3040042

(10) Hase, V., Schäfer, M.S., Metag, J., Bischofberger, M., & Henry, L. (2022). Engaging the Public or Asking Your Friends? Analyzing Science-Related Crowdfunding Using Behavioral and Survey Data. Public Understanding of Science, 39(8), 993-1011. https://doi.org/10.1177/09636625221113134

(9) Hase, V., Mahl, D., & Schäfer, M. S. (2022). Der „Computational Turn“: ein „interdisziplinärer Turn“? Ein systematischer Überblick zur Nutzung der automatisierten Inhaltsanalyse in der Journalismusforschung. Medien & Kommunikationswissenschaft, 70(1-2): 60-78. https://doi.org/10.5771/1615-634X-2022-1-2-60

(8) Hellmüller, L., Hase, V., & Lindner, P. (2022). Terrorist Organizations in the News: A Computational Approach to Measure Media Attention Towards Terrorism. Mass Communication & Society, 25(1), 134–152. https://doi.org/10.1080/15205436.2021.1936068

(7) Hase, V., Mahl, D., Schäfer, M., & Keller, T. (2021). Climate Change in News Media across the Globe: An Automated Analysis of Issue Attention and Themes in Climate Change Coverage in 10 Countries (2006-2018). Global Environmental Change, 70, 102353. https://doi.org/10.1016/j.gloenvcha.2021.102353

(6) Hase, V., Engelke, K., & Kieslich, K. (2020). The Things We Fear. Combining Automated and Manual Content Analysis to Uncover Themes, Topics and Threats in Fear-Related News. Journalism Studies, 21(10), 1384–1402. https://doi.org/10.1080/1461670X.2020.1753092

(5) Keller, T., Hase, V., Thaker, J., Mahl, D., & Schäfer, M. S. (2020). News Media coverage of Climate Change in India 1997-2016. Using Automated Content Analysis to Assess Issue Salience and Topics. Environmental Communication, 14(2), 219-235. https://doi.org/10.1080/17524032.2019.1643383

(4) Wintterlin, F., Engelke, K., & Hase, V. (2020). Can Transparency Preserve Journalism’s Trustworthiness? Recipients' Views on Transparency about Source Origin and Verification regarding User-generated Content in the News. Studies in Communication and Media, 9(2), 218-240. https://doi.org/10.5771/2192-4007-2020-2-218

(3) Grosser, K., Hase, V., & Wintterlin, F. (2019). On Measuring Trust and Distrust in Journalism: Reflection of the Status Quo and Suggestions for the Road ahead. Journal of Trust Research, 9(1), 66-86. https://doi.org/10.1080/21515581.2019.1588741.

(2) Grosser, K., Hase, V., & Wintterlin, F. (2019). Trustworthy or Shady? Exploring the Influence of Verifying and Visualizing UGC on Online Journalism’s Trustworthiness. Journalism Studies, 20(4), 500-522. https://doi.org/10.1080/1461670X.2017.1392255.

(1) Birkner, T., & Hase, V. (2017). Framing German and Global Politics over Three Decades – A Content Analysis of the Journalistic Work of Helmut Schmidt. Medien und Zeit, 32(2), 30-42.