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Computational analysis and modeling of the evolution of trends is an important area of research in Natural Language Processing (NLP) because of its socio-economic impact. However, no large publicly available benchmark for trend detection currently exists, making a comparative evaluation of methods impossible. We remedy this situation by publishing the benchmark TRENDNERT, consisting of a set of gold trends and downtrends and document labels that is available as an unrestricted download, and adoi:10.1609/aaai.v34i05.6372 fatcat:k5rimv3l4ng5lhwvrbz434izjm