Recently, much attention has been given to models for identifying rumors in social media. Features that are helpful for automatic inference of credibility, veracity, reliability of information have been described. The ultimate goal is to train classification models that are able to recognize future high-impact rumors as early as possible, before the event unfolds. The generalization power of the models is greatly hindered by the domain-dependent distributions of the features, an issue insufficiently discussed. Here we study a large dataset consisting of rumor and non-rumor tweets commenting on nine breakingnews stories taking place in different locations of the world. We found that the distribution of most features are specific to the event and that this bias naturally affects the performance of the model. The analysis of the domain-specific feature distributions is insightful and hints to the distinct characteristics of the underlying social network for different countries, social groups, cultures and others.

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