Duluth at SemEval-2019 Task 6: Lexical Approaches to Identify and Categorize Offensive Tweets

This paper describes the Duluth systems that participated in SemEval--2019 Task 6, Identifying and Categorizing Offensive Language in Social Media (OffensEval). For the most part these systems took traditional Machine Learning approaches that built classifiers from lexical features found in manually labeled training data. However, our most successful system for classifying a tweet as offensive (or not) was a rule-based black--list approach, and we also experimented with combining the training data from two different but related SemEval tasks. Our best systems in each of the three OffensEval tasks placed in the middle of the comparative evaluation, ranking 57th of 103 in task A, 39th of 75 in task B, and 44th of 65 in task C.Comment: 7 pages, Appears in the Proceedings of the 13th International Workshop on Semantic Eva luation (SemEval 2019), June 2019, pp. 593-599, Minneapolis, MN (a NAACL-2019 workshop, aka OffenseEval--2019

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

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.Comment: 10 pages, To appear in the Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval--2020), December 12-13, 2020, Barcelona (a COLING-2020 workshop, aka OffensEval--2020