A Multimodal Dataset of Images and Text to Study Abusive Language
Authors: Stefano Menini, Alessio Palmero Aprosio, Sara Tonelli (Fondazione Bruno Kessler, 2020)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
In this paper, we present a novel dataset composed of images and comments in Italian, created with teenagers in classes using a simulated scenario to raise awareness on cyberbullying phenomena. Potentially offensive comments have been collected for more than 1,000 images and manually assigned to a semantic category. Our analysis shows that the presence of human subjects, as well as the gender of the people present in the pictures trigger different types of comment, and provides novel insight into the connection between images posted on social media and offensive messages. We also compare our corpus with a similar one obtained with WhatsApp, showing that comments to images show different characteristics compared to text-only interactions.

Introduction
In order to study abusive language online, the availability of datasets containing the linguistic phenomena of interest are of crucial importance. However, when it comes to specific target groups, for example teenagers, collecting such data may be problematic due to issues with consent and privacy restrictions. Furthermore, while text-only datasets for abusive language detection have been widely developed and used by the NLP community, limitations set by image-based social media platforms like Instagram make it difficult for researchers to experiment with multimodal data. We therefore present a novel corpus containing images and potentially offensive Italian comments and we analyse it from different perspectives, to investigate whether the subject of the images plays a role in triggering a comment. %We find that users judge the same images in different ways, although the presence of a person in the picture increases the probability to get an offensive comment. The data collection was carried out in several school classes, being part of a `living lab' to raise awareness on cyberbullying and, more generally, on the use of social media by teenagers. The dataset is freely available on Github and, since the comments were collected with the written consent of parents and teachers, they can be freely used for research purposes, without the ethical implications that would derive from using real data posted by teenage users. The images, instead, are released as a ResNet-18 neural network trained on ImageNet, similar to recent NLP works AUTHOR, since they were taken from Instagram and cannot be shared as pictures.

Conclusion
In this work we present a multimodal dataset in the abusive language domain created by teenage participants who, during online annotation sessions, judged whether images may trigger an offensive comment, left a possible comment and also assigned to it a trigger category. The analysis of the collected data gives interesting insights into how Instagram-like platforms work. First of all, images containing persons are more likely to trigger potentially offensive comments than those without a human subject. Both female and male subjects are offended but the reasons may differ: the former are targeted more because of the pose and of the clothing, while the latter for the pose and the facial expression. In general, the reasons why a comment is triggered seems to be subjective, depending on the user leaving the comment rather on some actual characteristics of the person portrayed in the picture. We conducted our data collection using Instagram pictures randomly taken from this platform, because it is the social network that is most used by teenagers, including those involved in our annotation sessions. However, this makes the release of the full dataset impossible, because of a very restrictive policy concerning images. We therefore adopt a strategy already used within the NLP research community (Kruk et al., 2019), releasing the images as a layer of a ResNet-18 neural network trained on ImageNet. The comments, instead, are freely available without restrictions due to the consent signed by all parents and by the anonymity granted to participants. This represents a very interesting dataset from a research point of view, since it includes comments written by underage students that are usually difficult to obtain because of privacy reasons. In the future, we plan to extend our study to compare the judgements given by single users to those given by groups of peers. In a preliminary study, we observed that, when students are given the possibility to discuss with a small group of peers whether they would like to write an offensive comment, they tend to be more aggressive and are more likely to select ‘yes’. While the comments collected so far with groups of annotators are not enough to allow a fair comparison between the two settings (single vs. group), we plan to extend them in the future and pursue also this interesting research line. Finally, we plan to train a classifier able to detect offensive messages by merging visual and textual features with the goal to integrate it in a monitoring tool like the one introduced in (Menini et al., 2019). This would enable a more holistic, context-aware understanding of offensive communication online.