Proceedings of the 15th Conference on Natural Language Processing (KONVENS 2019) Distributed under a CC BY-NC-SA 4.0 license.
FraunhoferSIT at GermEval 2019: Can Machines Distinguish Between Offensive Language and Hate Speech? Towards a Fine-Grained Classification Inna Vogel Roey Regev Fraunhofer Institute SIT Rheinstrasse 75 64295 Darmstadt [email protected]
Abstract hate speech targets disadvantaged social groups in a manner that is potentially harmful to them (Ja- In this paper, we describe the Fraunhofer- cobs and Potter, 2001). Many studies still tend to SIT submission for the “GermEval 2019 conflate hate speech and offensive language. A key – Shared Task on the Identification of Of- challenge for automatic hate speech detection on fensive Language”. We participated in two social media is the separation of hate speech from subtasks: task 1 is a binary classification other instances of offensive language (Davidson et of German tweets on the identification of al., 2017). offensive language. Task 2 is a fine-grained As there is a pressing demand for methods to classification to distinguish between three automatically identify hate speech and other sus- subcategories of offensive language. Our picious posts, we participate in this year’s “Ger- best model is an SVM classifier based on tf- mEval Task 2019 – Shared Task on the Identifi- idf character n-gram features. Our submit- cation of Offensive Language”1. The task is fo- ted runs in the shared task are: Fraunhofer- cused on detecting offensive comments in a set SIT coarse [1-3].txt for task 1 and Fraun- of German tweets in three subtasks. Task 1 is a hoferSIT fine [1-3].txt for task 2. Our fi- binary classification to distinguish offensive from nal system reaches 0.70 macro-average F1- non-offensive tweets. Task 2 requires a more fine- score for the binary classification and 0.46 grained classification of offensive tweets which are F1-score for the fine-grained classification. divided into three subcategories - profanity, insult The achieved results show that the problem and abuse (Ruppenhofer et al., 2018). The first of automatically distinguishing between of- category “PROFANITY” uses insults and swear fensive language and “Hate Speech” is far words, but not against a person or a group. Tweets from being solved. that are labeled as “INSULT” want to offend some- one. We categorize “ABUSE” as hate speech as 1 Introduction it uses “language that is used to express hatred to- In the pseudonymous environment of social me- wards a targeted group or is intended to be deroga- dia and due to the massive rise of user-generated tory, to humiliate, or to insult the members of the content on the internet, hate speech and offensive group” and can be defined according to Davidson language are easily produced and spread. As the et al. (2017) as hate speech. Subtask 3 focuses on amount of harmful comments and posts is continu- the classification of explicit and implicit offensive ously growing, it is not feasible to manually check language. each text message for suspicious content. Addition- We participated in task 1 and 2 of the competi- ally, hate speech violates more than just feelings. tion. In this paper, we report on the FraunhoferSIT It can be extremely harmful to society, for example system to classify German tweets with respect to by inciting mass violence. Governments and so- their offensiveness. First, we give a short overview cial network platforms can benefit from automatic of the work already conducted in the field of offen- detection and prevention of malicious posts on the sive language detection. In Section 3, we describe net (Fortuna and Nunes, 2018). the competition tasks and the data provided by the But what constitutes hate speech and when does GermEval organizers. Section 4 is dedicated to the it differ from offensive language? A unified def- 1Germeval Task 2, 2019: https://projects.fzai. inition does not exist yet. The consensus is that h-da.de/iggsa/
377 machine learning methodology used. We describe 3 GermEval Competition Task 2019 the text preprocessing and the features we used to The organizers of GermEval 2019 provided train- train our SVM models. In Section 5, we evalu- ing and test datasets2 for three offensive language ate the performance of our approaches. Lastly, we detection tasks. The provided datasets for training conclude our paper in Section 6. consist of 12,536 tweets without any user meta- data. Task 1 is a binary classification for deciding 2 Related Work whether a German tweet contains offensive lan- A wide range of machine learning and deep- guage (the category labels and the number of tweets learning approaches have been implemented to au- are “OFFENSE”: 4,177 and “OTHER”: 8,359). tomatically detect offensive language in text data. Task 2 is a multi-class classification and distin- Schmidt and Wiegand (2017) provide in their sur- guishes between three subcategories of offensive vey a short and comprehensive overview of auto- language with the more fine-grained labels “PRO- matic hate speech detection with a focus on fea- FANITY”: 271, “INSULT”: 1,601 and “ABUSE”: ture extraction. Using bag of words or embed- 2,305. While training data contains examples of dings can yield to reasonable classification perfor- all categories, the class distribution is fairly imbal- mance. In their survey, they outlined that character- anced. The majority of tweets are neutral (67%). level-approaches perform better than token-level- Abusive tweets are also relatively often (18.4%), approaches because they reduce the spelling varia- while the class “PROFANITY’ is underrepresented tion problem often faced when working with user- (2.2%). The three classes that contain offensive generated content. Lexical resources, such as a list language are defined as follows (Ruppenhofer et of slurs, can also help to improve the performance al., 2018): but only if they are combined with other types of Profanity: abusive words are used but the features. • tweet does not insult someone (e.g. “ich Nobata et al. (2016) combine lexical features scheiß auf deine Gedenkkultur sie geht mir such as n-grams, as well as syntactic features with am Arsch vorbei”) distributional semantics to detect abusive language in English comments on “Yahoo! Finance and Insult: profanity is directed at an individual • News”. Badjatiya et al. (2017) and Davidson et with the intention to insult the person or group al. (2017) also confirm that word n-grams are well- (e.g. “SPD ihr seit wirklich das Asozialste performing features for the detection of hate speech Pack”) and abusive language. Abuse: the tweet is intended to demean and Supervised approaches like Logistic Regression • (Djuric et al., 2015; Del Vigna et al., 2017) or attack another person or a group with cruel Support Vector Machines (Del Vigna et al., 2017; and derogatory language which we categorize Davidson et al., 2017) have shown to obtain good as hate speech (e.g. “Der verdammte Dreck- results in classifying abusive language. Del Vigna sack und Massenmorder¨ Israel will mit allen et al. (2017) trained their SVM with word embed- Mitteln ein Krieg gegen Russland”) dings and Davidson et al. (2017) used bigram, un- Other: the tweet is neutral and does not • igram and trigram features, each weighted by its contain any assertive or offensive words (e.g. tf-idf. LSTM (Del Vigna et al., 2017; Badjatiya “Ach so vergessen , einen schonen¨ guten Mor- et al., 2017) and Convolutional Neural Network gen”) classifiers employed on word embeddings or other pretrained representations of words and tokens are Subtask 3 focuses on the classification of explicit also highly effective for the task of classifying abu- and implicit offensive language. Explicit tweets sive language (Badjatiya et al., 2017; Ho Park and directly express hatred towards a particular target. Fung, 2017). With implicit tweets, hatred of a target must be Most of the research is focused on English derived from the context. In this paper, we focus datasets. The identification of toxicity in German on the binary classification (task 1) as well as the language messages received less attention by the re- more challenging fine-grained classification task 2. searchers so far. Comparable cross-lingual research 2GermEval 2019 Data and Tasks: https://projects. is sparse. fzai.h-da.de/iggsa/projekt/
378 The competition results are evaluated with re- 10. Hashtags (#) and the attached token were pro- gard to macro-average F1-score, which is the un- cessed in a special way since they are widely weighted mean of the F1-scores of each individual used on Twitter and contain semantic content. category. First, the hash sign was removed. Compound words were separated in a sequence of mean- 4 Methodology ingful terms (e.g. “#NoAfD” - “No AfD”, “#KolnerTreff”¨ - “Kolner¨ Treff”, “#ichwars” In the following, the same approach is applied to - “ich wars”, “#OSZE-Beobachter” - “OSZE both classification tasks. First, the Twitter data was Beobachter”, “#EU-Beitrittsgesprache”¨ - “EU preprocessed to handle idiosyncrasies such as hash- Beitrittsgesprache”).¨ tags, Emojis or irrelevant characters. Afterwards, character n-grams are extracted as features which We also experimented with replacing all hashtags serve as tf-idf weighted input to train a Support and @-Mentions with placeholders (
5. Deleting irrelevant symbols and characters, The best performance was achieved by training b). e.g. “+,*,/,”. When building the vocabulary, terms that have a document frequency lower than 2 were ignored. 6. Sequences of the same characters with a length greater than three are removed. 4.3 SVM Implementation To train the two classifiers, we used scikit-learn’s 7. Removing words with less than three charac- SVM with tf-idf character n-grams in the range 1 ters. to 7 as features. Task 1 is a binary classification with the classes “OTHER” and “OFFENSE”. Task 8. Removing stopwords by using the NLTK (Nat- 2 is a multi-class classification problem with the ural Language Toolkit) list of stopwords for four classes “OTHER”, “PROFANITY”, “INSULT” the German language. and “ABUSE”. For both models OVR (“One-vs.- Rest”) was used as a decision function. To solve a 9. To tokenize the words we used the TwitterTok- multiclass classification task, OVR combines mul- enizer from the NLTK library. The TwitterTo- tiple binary SVM classifiers (Huang et al., 2005). kenizer is adapted for Twitter and other forms Each SVM classifies samples into the correspond- of casual speech used in social networks. It ing class against all the other classes (Hong and contains some regularization and normaliza- Cho, 2006). tion features (e.g. converting tweets to lower- When experimenting with the training set, we case and vice-versa, removing username men- split the data provided by the organizers into two tions and reducing the length of words in the tweet with repeated characters). 3http://scikit-learn.org
379 parts. For training, we used 70% of the data. The sification task 2. For each task, we submitted three remaining 30% were used as test set. We experi- runs. mented with hyperparameter tuning, manually and The GermEval task defines the macro-average by employing scikit-learn’s grid search function. F1-score as its evaluation measure. The results of The performance improved slightly by using Sig- Tasks 1 and 2 have shown that there are only minor moid Function instead of the Radial Basis Function differences in performance. Our best model, an (RBF) kernel. SVM classifier, using tf-idf weighted character n- For the fine-grained classification (task 2) we gram features on the entire training data provided initially trained two models. The first model is a bi- by the organizers achieved the best performance nary classifier (the same approach as used for task among the three runs submitted for the tasks. For 1) distinguishing between the two classes “OTHER” task 1 a macro F1-score of 0.70 could be achieved. and “OFFENSE” - the latter containing all three Our best performance for task 2 was a F1-score of the abusive language classes described above. of 0.46. In our experiments, character n-grams The second model was trained to differentiate be- outperformed token n-gram features. The best n- tween the three classes of offensive language “PRO- grams at the character level range from 1 to 7. We FANITY”, “INSULT” and “ABUSE”. In a second expect our model’s performance to improve further approach to task 2, a multi-class SVM was used, with another set of features and more training data. which was trained on all four classes simultane- As shown in our submitted runs, this would be ously. Our second approach achieved slightly better particularly helpful for the second, fine-grained performance results. task, where our classifiers performed really poorly. Finally, each model was performed on the of- The performance results for the best runs of task 1 ficial GermEval 2019 test set. For every task we and 2 are displayed in the following tables 2 and 3. submitted three runs, each tested with different data sets. The first run was trained on the entire dataset Task 1: SVM with tf-idf character n-grams provided by the GermEval organizers. The second Category Performance Measure was trained only on this year’s dataset (2019), the PR F1 third on text data from 2018. The best classification Other 81.24 78.62 80.42 results in both tasks were achieved using the whole Offense 58.46 60.93 59.67 data to train the model. The classification results Average 69.85 70.27 70.06 are provided in the following section. Table 2: Official evaluation results for each cate- gory of task 1 with the metrics Precision (P), Recall 5 Evaluation (R), and F1 In this chapter, we report on runs using our SVM models trained on the data provided by the orga- Task 2: SVM with tf-idf character n-grams nizers. The outputs of the SVM classifiers were Category Performance Measure submitted to the GermEval competition under the PR F1 submission name FraunhoferSIT (see Table 1). Other 81.37 77.58 79.43 Profanity 50 9.01 15.27 Submitted runs (name) Dataset Task Insult 34.74 39.43 36.94 FraunhoferSIT coarse 1.txt 2018/19 Task 1 Abuse 33.52 44 38.05 FraunhoferSIT coarse 2.txt 2019 Task 1 Average 49.91 42.51 45.91 FraunhoferSIT coarse 3.txt 2018 Task 1 FraunhoferSIT fine 1.txt 2018/19 Task 2 Table 3: Official evaluation results for each cate- FraunhoferSIT fine 2.txt 2019 Task 2 gory of task 2 with the metrics Precision (P), Recall FraunhoferSIT fine 3.txt 2018 Task 2 (R), and F1
Table 1: Submitted runs by FraunhoferSIT 6 Conclusion We participated in task 1, the binary classification In this paper, we have described the system submit- task distinguishing offensive from non-offensive ted to the “Shared Task on Identification of Offen- tweets and the more challenging fine-grained clas- sive Language GermEval 2019” by FraunhoferSIT
380 (Darmstadt). We participated in two tasks. The first Ji Ho Park and Pascale Fung. 2017. One-step and two- task is a binary classification of German tweets step classification for abusive language detection on on the identification of offensive language with twitter. 06. the two classes “OFFENSE” and “OTHER”. The Jin-Hyuk Hong and Sung-Bae Cho. 2006. Multi- second task was a more challenging fine-grained class cancer classification with ovr-support vector classification. In addition to detecting offensive machines selected by na¨ıve bayes classifier. In Irwin King, Jun Wang, Lai-Wan Chan, and DeLiang Wang, tweets, the task was to distinguish between the editors, Neural Information Processing, pages 155– three subcategories: “PROFANITY”, “INSULT” 164, Berlin, Heidelberg. Springer Berlin Heidelberg. and “ABUSE”. We trained an SVM as part of scikit- learn’s library and used tf-idf character n-grams in D.S. Huang, X.P. Zhang, and G.B. Huang. 2005. Ad- vances in Intelligent Computing: International Con- the range 1 to 7 as features. Our model achieves a ference on Intelligent Computing, ICIC 2005, Hefei, macro F1-score on task 1 of 0.70 and on task 2 of China, August 23-26, 2005, Proceedings. Num- 0.46. The relatively poor results, especially for task ber Teil 1 in Lecture Notes in Computer Science. 2, show that our system cannot reliably distinguish Springer Berlin Heidelberg. between offensive language and hate speech and James B. Jacobs and Kimberly Potter. 2001. Hate that more research needs to be done to improve the crimes: criminal law and identity politics. Oxford classification performance. University Press New York. Chikashi Nobata, Joel Tetreault, Achint Thomas, Acknowledgments Yashar Mehdad, and Yi Chang. 2016. Abusive lan- guage detection in online user content. In Proceed- This work was supported by the German Federal ings of the 25th International Conference on World Ministry of Education and Research (BMBF) under Wide Web, WWW ’16, pages 145–153, Republic and the project “X-SONAR” (Extremist Engagement Canton of Geneva, Switzerland. International World Wide Web Conferences Steering Committee. in Social Media Networks: Identifying, Analyzing and Preventing Processes of Radicalization). Fabian Pedregosa, Gael¨ Varoquaux, Alexandre Gram- fort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexan- References dre Passos, David Cournapeau, Matthieu Brucher, ´ Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, Matthieu Perrot, and Edouard Duchesnay. 2011. and Vasudeva Varma. 2017. Deep learning for hate Scikit-learn: Machine learning in python. J. Mach. speech detection in tweets. In Proceedings of the Learn. Res., 12:2825–2830, November. 26th International Conference on World Wide Web Josef Ruppenhofer, Melanie Siegel, and Michael Wie- Companion, WWW ’17 Companion, pages 759– gand. 2018. Guidelines for iggsa shared task on 760, Republic and Canton of Geneva, Switzerland. the identification of offensive language. March, International World Wide Web Conferences Steering 12:2018. Committee. Anna Schmidt and Michael Wiegand. 2017. A survey Thomas Davidson, Dana Warmsley, Michael W. Macy, on hate speech detection using natural language pro- and Ingmar Weber. 2017. Automated hate speech cessing. In Proceedings of the Fifth International detection and the problem of offensive language. In Workshop on Natural Language Processing for So- ICWSM. cial Media, pages 1–10.
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