D uluth at S em E val-2020 Task 12: Offensive Tweet Identification in E nglish with Logistic Regression (2020)

Abstract: 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.

## introduction the goal of the offenseval-2020 task @xcite is to identify offensive language in tweets, and to determine if specific individuals or groups are being targeted. we relied on traditional machine learning methods implemented in scikit @xcite to build logistic regression classifiers from distantly supervised training examples of offensive tweets @xcite . our methods are well known and so will only be described briefly. instead, our primary focus in this paper is on a post-evaluation qualitative analysis of both our results and the underlying task data. identifying offensive, abusive, and hateful language is a challenging problem that is drawing increasing attention both among the general public and in the research community (e.g., @xcite ). these are difficult problems since what is offensive depends not only on the words being used but also on the situation in which they occur. whether something is offensive may depend on answers to questions like : is the source of such language in a position of power? is the target a member of a marginalized group? is there a difference in age, race, religion, or social status of the source and the target? the number of real-world factors that may determine if language is offensive is impossible to enumerate, and leads to a high degree of polysemy in candidate offensive words that can only be unraveled by considering the social situation in which they are used. for example, words that are usually considered slurs may be used within a marginalized group as a means of bonding or identifying. the same is true of profanity, which when used within an in-group or among friends may not be offensive, while the same language directed at an outsider might well be offensive @xcite @xcite . ## experimental data offenseval-2020 is made up of three tasks that went through the final evaluation stage in late february and early march 2020. task a is to classify a tweet as offensive (off) or not (not). task b takes the tweets identified as off from task a and determines if hey are targeted insults (tin) or not (unt). task c considers the targeted insult tweets from task b and classifies them as being directed against an individual (ind), group (grp) or other entity (oth). there is a cascading relationship between the tasks, where task c requires the output of task b, and task b requires the output of task a. this is the same set of labels and tasks as used in offenseval-2019. offenseval-2020 provided a large corpus of training tweets known as solid that was created via distant supervision @xcite . task a included 9,089,139 tweets, task b provided 188,974 and task c 188,973. the distant supervision used to create solid was seeded with the the manually labeled olid corpus from offenseval-2019 @xcite . olid is a much smaller corpus of 13,240 training tweets and 860 test tweets which was also available to offenseval-2020 participants. solid and olid are different in that tweets in olid are labeled with categories whereas in solid tweets are scored on a continuous scale of 0 to 1 to reflect the collective judgment of the models used as a part of distant supervision. as such the solid data did not provide a specific indication as to the boundaries between categories. ## assigning labels to the experimental data we made a few significant decisions early on regarding the data. first, we elected not to use the 2019 olid data. we participated in offenseval-2019 and observed some potential inconsistencies in the olid training data @xcite . we also felt that since solid was seeded with olid that there would be no particular advantage to also using olid. there is a tradeoff here between using a smaller amount of manually annotated data (olid) versus a much larger sample of potentially noisier distantly supervised data (solid). second, we decided to directly map the 2020 solid training data to categories. this required us to draw somewhat perilous and arbitrary boundaries through a real valued space for each task. we studied the distribution of scores in task a and observed that the median in the solid training data was .25, and that the standard deviation was .185. this suggested that the vast majority of tweets were not considered offensive, and so we selected the value of .8 for our cutoff. based on our manual review of the training data we felt that tweets in task a with a score greater than .8 were very likely to be offensive. we realized that we could set this boundary lower (perhaps .75 or .70) and still include many offensive tweets, but wanted to choose a boundary that might at least give the possibility of high precision results. after making this cutoff, only about 4% (356,811) of the training tweets were labeled as offensive. we used this same threshold with task b and c. for task b, any tweet with a score less than .2 was considered targeted. in task c separate scores were provided for each of the three possible categories, so any tweet with a score greater than .8 was considered to be targeted against an individual, group or other. ## methods the duluth system is a slightly modified version of a tweet emoticon classification system developed for semeval-2018 @xcite . this system does some light pre-processing that largely leaves the tweets intact (but does basic cleaning and regularization of punctuation) and then identifies unigrams and bigrams as candidate features. we took the same approach for each task, where we learnt a logistic regression classifier from the training data where category boundaries were set as described in section 2. ## experimental results the official rankings in offenseval were based on macro-averaged f1, and we also report accuracy and per-class precision, recall, and f 1 scores. the results of the duluth systems were significantly below top ranked systems. for task a our macro f-1 score was .7713 while the top ranked system attained .92226, for task b we obtained .53816 versus a top rank of .74618, and for task c the duluth system scored .57437 against .71450. that said, our qualitative analysis of our results suggests that there is some noise in the class labels of the gold data, and so we are uncertain if significantly higher accuracy would translate into meaningful performance on the task of identifying offensive language in english. in the confusion matrices that follow, the distribution of gold answers (ground truth) are shown on the columns, and the system predictions are on the rows. ## discussion the qualitative analysis described above suggests that any use of profanity often resulted in a tweet being labeled as offensive. this had a negative effect on the downstream tasks which took such tweets and tried to determine if the offense was targeted, and who was the target. we wonder if the use of .5 as the boundary between offensive or not, targeted or not, etc. may have been too lenient. by contrast, the duluth system used a cutoff of .8. our manual inspection suggested that tweets that scored above .8 tended to be somewhat harsher and more offensive than those with lower scores, although this is more of an intuition at this point rather than a conclusive finding. ## ethical considerations identifying offensive language is a problem without a clear definition. the challenge is that a particular statement may or may not be offensive depending on the context in which it is used, and that depends on the nature of the source and target of such a statement. to further complicate matters, offensive statements may also be true statements. a totalitarian leader may be deeply offended by statements detailing human rights abuses committed by their regime, or a celebrity may be offended that a recent criminal charge is widely reported. there are also legitimately unclear boundaries. there are people who may be genuinely offended for religious or cultural reasons by any use of profanity. since their preferences are genuine, should that be the standard that offensive language detection relies on? if so, the problem reduces to the simpler task of identifying profanity, which would then unfairly classify common everyday even friendly uses of profanity as offensive and subject it to flagging, removal, or other sanctions. it seems clear that we can't make blanket assumptions about what is offensive. instead, we need to be very specific as to what our boundaries are for a particular corpus or task. in particular, we should be mindful of who is the target of such a language (as was done in both @xcite ) and ( zampieri et al., 2020) ), but we should also consider the source, and the context in which the language occurs. on a broader scale, we should reflect on who holds power @xcite , and who gains and loses power if a statement is flagged as offensive. while evaluation scores are important to advancing progress in nlp, there are some serious problems with making decisions about how well a problem is being solved simply by looking at such measures. we believe that in-depth qualitative analyses of mismatches between gold standard data and system predictions must be carried out in addition to providing more quantitative results. state of the art methods for many nlp problems have been shown to reach high levels of accuracy simply by learning spurious patterns in the training data without making a dent in the underlying problem we'd like to solve (e.g., @xcite ). we must acknowledge the possibility of this occurring in offensive language and hate speech detection and carry out qualitative analyses of our results in addition to the more typical quantitative ones. the danger of simply focusing on high accuracy is that we can be lulled into a false sense of success. in offenseval-2020, 59 teams reached a macro f-1 score of .90 or better. does this mean the problem of offensive language detection has been solved? we do not believe this is the case, nor do we think that any of the task participants would draw this conclusion. however, an outsider viewing such results might wrongly conclude that these methods and models are ready for use in the wild. this could result in a flawed offensive language detector being deployed which would over or under identify problematic language, most likely to the disadvantage of already marginalized populations. ## conclusion this is the second year a duluth system participated in offenseval. last year in offenseval-2019 we took a relatively simple machine learning and rule-based approach and carried out an analysis of the results from the task. our findings @xcite were similar to this year, where the duluth system did not rank particularly high. also, we observed both in 2019 and 2020 that there appear to be some limitations in the gold standard annotations. in particular, in both years it seemed like there were quite a few false positives in the gold data, where tweets were labeled as offensive in task a and potentially targeting in task b when in fact they were not. it is not surprising that the 2019 olid data and 2020 solid data would show similar characteristics, since olid was the seed used for the distant supervision that created solid. in general it would appear virtually impossible to reliably annotate data without some background knowledge about the participants in the dialogue, as well as larger cultural contexts that might be at work (e.g, @xcite ). that said we believe that annotated corpora is an important resource for this problem, and we need to continue to refine our processes for the creation of the same. in the creation of our own corpora we are working to develop data statements @xcite and plan to incorporate domain experts in the annotation process. a3321 are an ugly i hate straight your hetero frick fake a3410 literally everyone wants a piece of my girlfriends ass. i need to treat her better smh. a2257 @user get the gains or die alone b**ch boy. a2793 @user what a liar. white terrorists are his followers. a406 @user f*ck off deb. the planet is dying and you don't care a171 waht the f**ck they are coming to la f*ck this shut im going to work a945 off??