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