Skip to main content English


Hannah Metzler
Mag. Dr. Hannah MetzlerResearcher & Project Lead

Center for Medical Data Science (Institute of the Science of Complex Systems)
Position: Research Associate (Postdoc)

ORCID: 0000-0001-9254-3675

Further Information


Data Analysis; Emotions; Machine Learning; Mental Health; Online Systems; Psychology, Experimental; Social Behavior; Statistics; Suicide

Research group(s)

  • Complexity Science Hub Vienna
    Head: Stefan Thurner
    Research Area: Understanding complexity to tackle present and future challenges The Complexity Science Hub Vienna was founded with the vision to become the focal point of complexity science in Europe. The aim is to provide an exciting, creative environment for open-minded visionaries who are brave enough to step out of mainstream science. The Hub will be an incubator and playground for radically new ideas. Outreach activities share the excitement for complexity science with a wider public.

Research interests

My research focuses on social media as a tool to better understand social behavior and mental health. I use social media as a data source, study their effects, and investigate social behavior in digital environments. I apply methods like text analysis and machine learning to capture digital traces of emotions, to investigate their validity, as well as their effects on misinformation spreading on social media. I have further explored the potential contributions of news and social media content to suicide prevention. I am part of the Open Science and the Effective Altruism community and strive to increase the transparency, reproducibility, and social impact of my work.

Techniques, methods & infrastructure

Social media data analysis, online experiments, machine learning, statistics

Selected publications

  1. Metzler, H. and Garcia, D. (2023) ‘Social Drivers and Algorithmic Mechanisms on Digital Media’, Perspectives on Psychological Science. Available at:
  2. Niederkrotenthaler, T. et al. (2022) ‘Association of 7 million+ tweets featuring suicide-related content with daily calls to the Suicide Prevention Lifeline and with suicides, United States, 2016–2018’, Australian & New Zealand Journal of Psychiatry, 57(7), pp. 994–1003. Available at:
  3. Metzler, H. et al. (2022) ‘Detecting Potentially Harmful and Protective Suicide-Related Content on Twitter: Machine Learning Approach’, Journal of Medical Internet Research, 24(8), p. e34705. Available at:
  4. Pellert, M. et al. (2022) ‘Validating daily social media macroscopes of emotions’, Scientific Reports, 12(1). Available at:
  5. Metzler, H. et al. (2023) ‘Collective emotions during the COVID-19 outbreak.’, Emotion, 23(3), pp. 844–858. Available at: