Merging social media mining, artificial intelligence and citizen science to mobilise new data and new communities for environmental stewardship.
Mining environmental observations from social media.
Mapping information flows for environmental and sustainability topics.
Mobilising online communities for environmental monitoring.
scitingly is a personal science initiative to harness social media for environmental monitoring, create public awareness for critical environmental challenges and mobilise communities for citizen science.
Using social media to gather new environmental observations is at the core of this effort; the millions of casually contributed nature observations on platforms like Twitter can augment traditional environmental monitoring and unearth critical warnings for environmental threats.
Most importantly however, the ad-hoc micro-communities forming in Twitter conversations around those casual environmental observations are essentially — albeit unknowingly — embryonic citizen science communities that could be mobilized for an active engagement in environmental challenges.
Finally, given the exponential transformations that are required in the next decade to address the climate crisis, it is crucial that critical research is communicated effectively and can support an informed public debate. “Publish and forget” is thus not an option. Environmental researchers and research organizations must be able to understand and continuously monitor the public online impact of their research. To that end I am working on tools to analyze Twitter conversations on environmental and sustainability topics. Results of the most recent project I participated in show that the backlash on social media can completely drown out even the main message of a long-running, well-funded research project promoted through a professional communications effort.
scitingly aims to provide an effort to combine these three areas and utilise the benefits of social media as an inherently democratic communication and networking tool.
I hold a degree in Forest Sciences which somehow seamlessly led to another degree in Artifical Intelligence. After an extended period working for IT startups and learning how to build software professionally, I returned to academia for a PhD combining ecology, data science and social media to contribute to new solutions addressing environmental challenges.
My projects explore connections between digital technologies and sustainability, with a particular focus on the role of social media for public engagement with environmental challenges. The nature of my work is best characterized as data science, working with large data sets and employing methods such as social network analysis, text mining and machine learning.
University of Göttingen
MSc in Artificial Intelligence, 1998
The University of Edinburgh
MSc in Forest Sciences, 1995
University of Göttingen