This paper introduces a corpus of Turkish offensive language. To our knowledge, this is the first corpus of offensive language for Turkish... The corpus consists of randomly sampled micro-blog posts from Twitter. The annotation guidelines are based on a careful review of the annotation practices of recent efforts for other languages. The corpus contains 36 232 tweets sampled randomly from the Twitter stream during a period of 18 months between Apr 2018 to Sept 2019. We found approximately 19 {\%} of the tweets in the data contain some type of offensive language, which is further subcategorized based on the target of the offense. We describe the annotation process, discuss some interesting aspects of the data, and present results of automatically classifying the corpus using state-of-the-art text classification methods. The classifiers achieve 77.3 {\%} F1 score on identifying offensive tweets, 77.9 {\%} F1 score on determining whether a given offensive document is targeted or not, and 53.0 {\%} F1 score on classifying the targeted offensive documents into three subcategories. (read more)