Responding to Policy Signals? An Experimental Study on Information about Policy Adoption and Data Retention Policy Support in Germany

Objective: We analyze whether and how individuals react to information about the adoption of a particular policy, with a focus on the role of conservatism. Methods: We conducted an online survey experiment on support for data retention in Germany. A recent law on this issue allowed us to test the effects of two policy signals, information about the adoption of a new law (law signal) and information that this followed a Constitutional Court decision (law and court signal), on separate groups of respondents. Results: Our results show a positive effect of each policy signal on support for data retention. The effect of the law signal was even slightly stronger for individuals with conservative beliefs. Conclusion: Illustrating how lock-in effects of policies can work, our study contributes to research on attitudinal policy feedback: creating new legislation also means legitimizing the policy position in question and stating that this norm should be accepted

Fine-Grained Classification of Offensive Language

Social media platforms receive massive amounts of user-generated content that may include offensive text messages. In the context of the GermEval task 2018, we propose an approach for fine-grained classification of offensive language. Our approach comprises a Naive Bayes classifier, a neural network, and a rule-based approach that categorize tweets. In addition, we combine the approaches in an ensemble to overcome weaknesses of the single models. We cross-validate our approaches with regard to macro-average F1-score on the provided training dataset