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This paper describes the Duluth systems that participated in SemEval--2020Task 12, Multilingual Offensive Language Identification in Social Media(OffensEval--2020). We participated in the three English language tasks. Oursystems provide a simple Machine Learning baseline using logistic regression.We trained our models on the distantly supervised training data made availableby the task organizers and used no other resources. As might be expected we didnot rank highly in the comparative evaluation: 79th of 85 in Task A, 34th of 43in Task B, and 24th of 39 in Task C. We carried out a qualitative analysis ofour results and found that the class labels in the gold standard data aresomewhat noisy. We hypothesize that the extremely high accuracy (> 90%) of thetop ranked systems may reflect methods that learn the training data very wellbut may not generalize to the task of identifying offensive language inEnglish. This analysis includes examples of tweets that despite being mildlyredacted are still offensive.

Duluth at SemEval-2020 Task 12: Offensive Tweet Identification in English with Logistic Regression