Offensive Language Detection on Video Live Streaming Chat (2020)

Abstract: This paper presents a prototype of a chat room that detects offensive expressions in a video live streaming chat in real time. Focusing on Twitch, one of the most popular live streaming platforms, we created a dataset for the task of detecting offensive expressions. We collected 2,000 chat posts across four popular game titles with genre diversity (e.g., competitive, violent, peaceful). To make use of the similarity in offensive expressions among different social media platforms, we adopted state-of-the-art models trained on offensive expressions from Twitter for our Twitch data (i.e., transfer learning). We investigated two similarity measurements to predict the transferability, textual similarity, and game-genre similarity. Our results show that the transfer of features from social media to live streaming is effective. However, the two measurements show less correlation in the transferability prediction.

## introduction posting offensive comments is a common problem of abusive behavior not only on representative social media platforms (e.g., twitter) but also on other growing platforms 1 . for example, offensive expressions in live streaming chat could appear more frequently because users communicate with others in real time with less introspection during live streaming. in our preliminary study using crowdsourcing, seven out of 100 users of live streaming platforms answered that they had sent offensive expressions, while ten users answered that they had been the target of offensive behaviors. tackling this problem on social media can provide more protection to users and more inspection methods for service providers. offensive language detection on twitter has already attracted much attention due to twitter's large user base and linguistic idiosyncrasies. for example, semeval-2019 task 6: identifying and categorizing offensive language in social media @xcite was organized as a shared task for offensive language detection on twitter. the task consisted of three sub-tasks: (a) offensive language identification, (b) categorization of offense types, and (c) offense target identification. to provide more diversified characteristics for research on offensive language detection, it is also crucial to focus on video live streaming platforms that share similar features to twitter, such as twitch 2 , which is a popular live streaming platform mainly for gamers. this paper introduces a chat room that detects offensive expressions in real time and filters them out, as illustrated in figure 1 . to detect offensive expressions, we apply a deep learning approach. modern neural architectures for natural language processing are generally highly effective when provided with a large amount of labeled training data @xcite . however, large volumes of labeled data are difficult to obtain, especially for video live streaming, as it is an emerging platform. therefore, we apply a transfer learning approach to solve this problem. domain adaptation, a sub-field of transfer learning, aims to learn a transferable representation from a source domain and apply it to a target domain @xcite . we propose the use of (1) an existing large labeled social media dataset from twitter input: "you little bitch" transformer-based pre-trained model offensive language detection twitter social media dataset twitch live chat dataset output: this post is offensive user a user b user a's interface user b's interface e.g., the replace mode shows the modified post instead of the original post user a's post user a's modified post and (2) combinations of a large dataset and small datasets (live chat datasets from twitch) as the source domain to demonstrate the effectiveness of transferring the obtained features to the target domain. to compare the impact of different small datasets on transferability, we assumed that the more similar the small datasets are, the higher the accuracy of transferability. we considered the textual similarity and game-genre similarity to measure transferability. in our previous paper @xcite , we presented a discussion on the feasibility of both measurements. then, we trained these data on transformer-based pre-trained models and chose the best-performing method to construct the chat room. to address a variety of user needs, our proposed chat room has three modes: hide, replace (figure 1 ), and alert. we selected four games from twitch to collect data and experimented with the effects of combination of various small datasets. ## demonstration system we created an online chat room for offensive expression detection. the best model, roberta, and the training set with the best results over all experiments, the combination of twitter + lc gt + lc mc (0.802 in f1-score), were used to construct the chat room. to make full use of this model, we equipped our chat room with three modes to handle different scenarios and needs. we used sockets for the chat room communication function. the chat room is available online foot_3 . ## conclusion this paper presented a chat room for video live streaming platforms that detects offensive expressions and filters them out via three modes (hide, replace, and alert). we employed transfer learning from twitter to video live streaming posts on twitch. our results demonstrate that the transfer learning approach improves performance on the offensive language detection task for video live streaming. moreover, we investigated two measurements to quantify transferability. one limitation of this research is that the comparison of game-genre (game content) similarity is still challenging. in future work, we will collect more data on a wider variety of games. we will also consider assessing the similarity of the datasets based on embedded vectors rather than surface textual information, such as word occurrence, as used in this research. the idea of updating existing training data for new data (new games) has much room for future study.