GruPaTo at SemEval-2020 Task 12:Retraining mBERT on Social Media and Fine-tuned Offensive Language Models

We introduce an approach to multilingual Offensive Language Detection based on the mBERT transformer model. We download extra training data from Twitter in English, Danish, and Turkish, and use it to re-train the model. We then fine-tuned the model on the provided training data and, in some configurations, implement transfer learning approach exploiting the typological relatedness between English and Danish. Our systems obtained good results across the three languages (.9036 for EN, .7619 for DA, and .7789 for TR)

Smart detection of offensive words in social media using the soundex algorithm and permuterm index

Offensive posts in the social media that are inappropriate for a specific age, level of maturity, or impression are quite often destined more to unadult than adult participants. Nowadays, the growth in the number of the masked offensive words in the social media is one of the ethically challenging problems. Thus, there has been growing interest in development of methods that can automatically detect posts with such words. This study aimed at developing a method that can detect the masked offensive words in which partial alteration of the word may trick the conventional monitoring systems when being posted on social media. The proposed method progresses in a series of phases that can be broken down into a pre-processing phase, which includes filtering, tokenization, and stemming; offensive word extraction phase, which relies on using the soundex algorithm and permuterm index; and a post-processing phase that classifies the users’ posts in order to highlight the offensive content. Accordingly, the method detects the masked offensive words in the written text, thus forbidding certain types of offensive words from being published. Results of evaluation of performance of the proposed method indicate a 99% accuracy of detection of offensive words

A Hybrid Model for Monolingual and Multilingual Toxic Comment Detection

Social media provides a public and convenient platform for people to communicate. However, it is also open to hateful behavior and toxic comments. Social networks, like Facebook, Twitter, and many others, have been working on developing effective toxic comment detection methods to provide better service. Monolingual language model focuses on a single-language and provides high accuracy in detection. Multilingual language model provides better generalization performance. In order to improve the effectiveness of detecting toxic comments in multiple languages, we propose a hybrid model, which fuses monolingual model and multilingual model. We use labeled data to fine-tune the monolingual pre-trained model. We use masked language modeling to semi-supervise the fine-tuning of multilingual pre-trained model on unlabeled data and then use labeled data to fine-tune the model. Through this way, we can fully utilize the large amount of unlabeled data; reduce dependence on labeled comment data; and improve the effectiveness of detection. We also design several comparative experiments. The results demonstrate the effectiveness and advantage of our proposed model, especially compared to the XLM-RoBERTa multilingual fine-tuning model

Twitter-based Polarised Embeddings for Abusive Language Detection

We present a method to generate polarised word embeddings using controversial topics as search terms in Twitter as proxies for interactions among social media communities that may be liable to use abusive language. We investigate to what extent models trained with these embeddings perform with respect to generic embeddings across four data sets of abusive language, both in the same domain and out of domain, using simple linear classifiers. Our results show that the polarised embeddings are competitive in the same domain data sets, and perform better in out of domain one

Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key

As the role of online platforms has become increasingly prominent for communication, toxic behaviors, such as cyberbullying and harassment, have been rampant in the last decade. On the other hand, online toxicity is multi-dimensional and sensitive in nature, which makes its detection challenging. As the impact of exposure to online toxicity can lead to serious implications for individuals and communities, reliable models and algorithms are required for detecting and understanding such communications. In this paper We define toxicity to provide a foundation drawing social theories. Then, we provide an approach that identifies multiple dimensions of toxicity and incorporates explicit knowledge in a statistical learning algorithm to resolve ambiguity across such dimensions