Controversy and Conformity: from Generalized to Personalized Aggressiveness Detection (2021)

Abstract: There is content such as hate speech, offensive, toxic or aggressive documents, which are perceived differently by their consumers. They are commonly identified using classifiers solely based on textual content that generalize pre-agreed meanings of difficult problems. Such models provide the same results for each user, which leads to high misclassification rate observable especially for contentious, aggressive documents. Both document controversy and user nonconformity require new solutions. Therefore, we propose novel personalized approaches that respect individual beliefs expressed by either user conformity-based measures or various embeddings of their previous text annotations. We found that only a few annotations of most controversial documents are enough for all our personalization methods to significantly outperform classic, generalized solutions. The more controversial the content, the greater the gain. The personalized solutions may be used to efficiently filter unwanted aggressive content in the way adjusted to a given person.

## introduction unfortunately, in the pursuit of knowledge on the internet, one may come across content that they consider inappropriate for various reasons, such as being too aggressive. many users notoriously come across content that offends them while surfing the internet. this can cause discomfort and discourage from further expansion of knowledge. to avoid this, it is important to effectively filter out content that a given user may find unwanted. this poses a risk of erroneous assessment of whether a given text is considered inappropriate by a given person. for that purpose, we need to extend commonly applied generalizing solutions and develop personalized methods that take into account beliefs and preferences of the individual user. we expect this information can be obtained from the individual's prior opinions about the offensiveness of some texts. then, it is crucial to select the relevant texts that allow deriving as much information about users preferences as possible. our new idea is to use some known, most controversial texts whose offensiveness is very ambiguous and depends more on subjective personal judgment. we examined how many documents has to be annotated by a given user to encapsulate their beliefs sufficiently and to improve personalized reasoning. independently, we considered personal measures quantifying conformity of each individual. in other words, we measured to what extent a person evaluates documents similarly to others, i.e. "is a part of the mainstream". the conformity measures are used as input features for the classifier. this way, it is possible to find out the user beliefs based on their opinions regarding a relatively small number of texts. in this paper, we present novel methods of personalized aggressive content detection based on the representation of user opinion about aggressive texts. we propose: (1) conformity-based personalization, (2) class-based embeddings, and (3) annotation-based embeddings (sec. 6). our experiments were performed on the only relevant dataset wikipedia talk labels: aggression (sec. 3). having defined and calculated controversy of documents and conformity of users (sec. 4), we validated our methods. the results revealed that additional individualized features: simple user conformity measures computed on few texts or embeddings of even four controversial texts significantly boost our personalized classification (sec. 8). the gain provided by our personalized methods is greater for more controversial documents. this work is based on the results obtained in the article @xcite . in addition, in paper @xcite , we showed that the personalized approach is also effective for other subjective problems in nlp, such as recognizing emotions elicited by text. the source code we used to conduct experiments and evaluation is publicly available in clarin-pl github repository foot_0 . ## related work it is observable a steady increase in the number of offensive @xcite , hate @xcite , aggressive, toxic, cyberbullying @xcite , or simply socially unacceptable online messages @xcite . there are many definitions of offensive speech, which can be summarised as speech that targets specific social groups in a way that is harmful to them @xcite . some countries, such as the usa, protect the rights to use this type of speech as an acceptable form of political expression @xcite . in turn, the law prohibits hate speech in many eu countries @xcite . such laws pose a challenge for operators of social networking sites and other online services to identify and moderate unacceptable content. large companies such as facebook and google are often accused of not doing enough to ensure that their platforms are not used to attack other people @xcite . on the other hand, attempts to automatically control content often lead to the accidental blocking of content that was not intended to offend anyone. ambiguity of the definition of offensiveness is a serious problem. this inconsistency is visible in many reviews related to automatic detection of hate speech @xcite @xcite or more specifically on aggressiveness detection @xcite . automatic recognition of offensive speech is the subject of many nlp workshops, such as semeval 2019 @xcite , germeval 2018 @xcite , fire/hasoc 2019 @xcite or poleval 2019 @xcite . classic methods do not consider context and word order, e.g. the bag-of-words model @xcite or tf-idf @xcite . the representation may be extended with additional ontologies @xcite or wordnets @xcite @xcite @xcite and used with svm @xcite or logistic regression models @xcite @xcite . new methods often use word embeddings @xcite @xcite @xcite mixed with character embeddings @xcite , together with deep neural networks, e.g. cnn @xcite or lstm @xcite . the current state-of-the-art are transformer-based architectures such as bert @xcite , albert @xcite , xlnet @xcite or roberta @xcite . nevertheless all these methods focus solely on the text itself. any wider context has been considered very rarely, e.g. as time, thread or author's social network features @xcite . in articles focused on detection of aggressiveness @xcite @xcite , the most often used were datasets shared at the workshops on trolling, aggression and cyberbullying (trac) @xcite at lrec. few others also used the wikipedia talk labels: aggression @xcite , where all individual annotations are available, not just the majority vote. unfortunately, we have not found any other aggression dataset, for which this information would also be given. moreover the authors focus mainly on the multilingual aspect of the aggression detection @xcite @xcite . in addition to deep neural models, less complex methods such as logistic regression are also used @xcite . to the best of our knowledge, there are no work that dealt with the subjective problem of aggressiveness detection in the personalized way. the disagreement between annotators is usually measured by a single value, e.g. using cohen's kappa or krippendorf's alpha, and not investigated further. the researchers prefer a higher agreement level rather than controversy. therefore, majority annotation is used in modeling, which to some extent leads to the loss of valuable information. there are several studies focusing on the problem of the disagreement in data annotations. this provides valuable information not only about the annotators, but also about the instances by reflecting their ambiguity @xcite . there may be no single right label for every text. the disagreement was used to divide annotators into polarized groups @xcite or to filter out the spammers @xcite . in @xcite , attention was also drawn to the problem of conformity bias, where the reviewers tend to issue similar opinions. less frequently, the disagreement is examined at the instance level, to measure its controversy or ambiguity, as in @xcite . for example, @xcite used confusion matrices in word sense tagging task to create and explore coarse sense clusters. ## dataset: wikipedia talk labels we used the wikipedia talk labels: aggression data, gathered in the wikipedia detox project @xcite . unlike other collections, it provides information about all annotations given by crowdflower workers (not only the majority vote) for 100k+ comments from english wikipedia. the assigned aggression score ranged from very aggressive (-3), via neutral (0), to very friendly (3). it was binarized to '1 -aggressive' for negative scores or '0 -nonaggressive' for neutral or friendly annotations. the dataset contained a suggested data split into train, dev and test set. to enable our experiments, we removed annotations assigned by workers with less than 100 annotations in the train set, <20 in the dev set @xmath0. otherwise, we would not have data to extract user beliefs from and to perform personalization. we also removed users who did not assign any aggressive label in the dev set. information about at least one text, that a specific user considered aggressive was crucial to model his individual perception of such content. finally, there were 2,450 annotators left (tab. 1), so we randomly divided them into 10 equal-sized folds. the train set is used to calculate the representations (embeddings) of documents being classified. this is the only data exploited in the classic, generalizing approach (our baseline). the dev set provides information about user beliefs, i.e. their previous annotations. individualized input features are extracted from dev data: (1) conformity measures and (2) personal embeddings in class-based and annotation-based personalization. personalizationrelated calculations on the dev set refer to both training and testing procedure. the documents from the test set are embedded and classified by the trained model for the validation purposes. ## controversy and conformity measures for training and testing purposes, both controversy contr for documents and conformity gconf, wconf for users are calculated within the dev set. ## controversy analysis to have some insight into our data, we calculated controversy contr(d) on each dataset (train/dev/test). fig. 2 presents the distribution of annotations for controversy measure in the dev and test set. in both, the ratio of aggressive to nonaggressive documents is increasing and reaching 0.5 for the most controversial documents, i.e. contr(d) = 1 resulting from the same number of aggressive and nonaggressive votes. the examples of such texts are following: "your behaviour is inappropriate and your reaction is ludicrous. do they give out admin rights in cornflake packets now?", n 0 @xmath1. "far from being ridiculous, it is the recommended approach to follow on wikipedia. we don't simply state what either side claims, rather we report on how they are viewed by neutral 3rd party sources. take it to wp:npovn if you don't believe me, rather than indulging in your continued disruptive habit of always having the wp:lastword.", n 0 @xmath2. we learned that classic methods based solely on content analysis (not personalized) perform worse, the more controversial the documents being tested, fig. 6 . it was the main inspiration for our personalized methods. we also checked contribution of aggressive texts for the consecutive most controversial documents included in the personal user embeddings, fig. 3 . 6 methods for personalized ## experimental setup to validate our three personalized methods, we utilized wikipedia talk labels: aggression, see sec. the logistic regression models were optimized during the training process by using the l2 regularization and the early stopping mechanism. both of them aim to prevent overfitting and the early stopping mechanism additionally ensures that the model instance that achieved the best loss function score is preserved. the models were run on intel xeon processor e5-2650 v4. we also compared our personalized methods with the baseline, i.e. the commonly investigated approach generalizing user perception. it exploited only the evaluated text embeddings as the input. we considered classification performance not only for the whole test set but also in its breakdown of 10 percentage buckets according to three independent rankings of test docs: (1) most controversial (contr(d)), (2) with least conformity gconf (a, {0, 1}), averaged over all @xmath3, (3) least w conf (a, {0, 1}). here, the measures were computed for the test set only, not for dev. it was used to investigate where our models more outperform the baseline. in order to generate text embeddings in each personalization method, we used the fasttext library @xcite . it offers pre-trained word vectors for 157 languages, based on the continuous bag of words (cbow) model in a 300-dimensional space, with character n-grams of length 5. ## validation of personalization methods both class-based and annotation-based methods were tested using various rankings while selecting texts for personal embeddings: most controversial, class-balanced most controversial, most aggressive, and random. the conformity-based methods were evaluated in terms of the measure variant used: general conformity, weighted conformity, and both, all with random selection of texts. ## discussion a valuable observation from our experiments is that already one document used to valuate user beliefs is enough to significantly improve reasoning, fig. 5d . anyway, more texts in personalization keep boosting the performance, but about 4-5 previously annotated most controversial documents seem to be a reasonable trade-off between reasoning quality and user annoyance. annotation-based embeddings most precisely express user opinions, but it comes at the cost of linearly longer learning and demand for more samples. they also cannot easily adapt to different number k of personalization documents. we decided to utilize very fast logistic regression model with fasttext embeddings, since we wanted to examine thousands of models related to multiple scenarios, not all are presented here. we belief our personalization methods establish a new research direction: how to effectively and efficiently embed user beliefs? we expect new methods will be developed for that purpose. one of the most important postulate derived from our research is the demand for new datasets collections. we need annotations of individual humans rather than aggregated and agreed general beliefs received by majority voting, by annotator training, or by removal of controversial texts. besides, our personalization methods may be applied to any nlp problem with inconsistencies between people. it especially refers to diverse emotions evoked by textual content, hate speech, detection of cyberbullying or offensive, toxic, abusive, harmful, or socially unaccepted content. the common problem of imbalanced classes in aggressiveness detection (tab. 1, fig. 12 ) will be addressed in future work. ## conclusions the main conclusion from our research is that the natural controversies associated with individual perceptions of contents should not be overlooked or reduced but rather directly exploited in personalized solutions. ultimately, this reflects the diversity in our societies. our three new personalization methods make use of texts previously annotated by a given user by means of conformity measures, class-based or annotation-based embeddings. just a few documents are able to capture individual user beliefs, the more so, the more controversial documents they relate to. as a result, all our methods outperform classic solutions that generalize offensiveness understanding. the gain is greater for more controversial documents. the personalization solutions can also be applied to other nlp problems, where the content tends to be subjectively perceived as hate speech, cyberbullying, abusive or offensive, as well as in prediction of emotions elicited by text @xcite and even in sentiment analysis @xcite . we keep working on testing of our methods on more resource-demanding but also more sota language representations: xlnet @xcite , roberta @xcite , and xlm-roberta @xcite .