Biblio

Sentiment analysis and other practices for text analytics on social media rely on publicly available and editable collections of data for training and evaluation. These data collections are subject to poisoning and data contamination attacks by adversaries having an interest in misleading the results of the performed analysis. We present the problem of adversarial text mining with a focus on decision making and we suggest cross-discipline, cross-application and cross-model strategies for more robust analyses. Our approach is practitioner-centric and is based on broadly-used interpretable models with applications in decision making.