| Kurzfassung
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Twitter has been identified as a relevant data source for modelling purposes in the last decade. In this work, our goal was to model the conversational dynamics of inflation development in Germany through Twitter Data Mining. To accomplish this, we summarized and compared Twitter data mining techniques for time series data from pertinent research. Then, we constructed five models for generating time series from topic-related tweets and user profiles of the last 15 years. Evaluating the models, we observed that several approaches like modelling for user impact or adjusting for automated twitter accounts show promise. Yet, in the scenario of modelling inflation expectation dynamics, these more complex models could not contribute to a higher correlation between German CPI and the resulting time series compared to a baseline approach.
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