Matthias Häberle


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Changes in Twitter geolocations: Insights and suggestions for future usage
Anna Kruspe | Matthias Häberle | Eike J. Hoffmann | Samyo Rode-Hasinger | Karam Abdulahhad | Xiao Xiang Zhu
Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)

Twitter data has become established as a valuable source of data for various application scenarios in the past years. For many such applications, it is necessary to know where Twitter posts (tweets) were sent from or what location they refer to. Researchers have frequently used exact coordinates provided in a small percentage of tweets, but Twitter removed the option to share these coordinates in mid-2019. Moreover, there is reason to suspect that a large share of the provided coordinates did not correspond to GPS coordinates of the user even before that. In this paper, we explain the situation and the 2019 policy change and shed light on the various options of still obtaining location information from tweets. We provide usage statistics including changes over time, and analyze what the removal of exact coordinates means for various common research tasks performed with Twitter data. Finally, we make suggestions for future research requiring geolocated tweets.


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Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic
Anna Kruspe | Matthias Häberle | Iona Kuhn | Xiao Xiang Zhu
Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020

In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people’s moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span.