Deep Learning Approaches to Detecting Safeguarding Concerns in Schoolchildren’s Online Conversations

Franklin, Emma and Ranasinghe, Tharindu (2023) Deep Learning Approaches to Detecting Safeguarding Concerns in Schoolchildren’s Online Conversations. In: Proceedings of the 14th International Conference on Recent Advances in Natural Language Processing : RANLP 2023. INCOMA Ltd, BGR, pp. 364-372. ISBN 9789544520922

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Abstract

For school teachers and Designated Safeguarding Leads (DSLs), computers and other school-owned communication devices are both indispensable and deeply worrisome. For their education, children require access to the Internet, as well as a standard institutional ICT infrastructure, including e-mail and other forms of online communication technology. Given the sheer volume of data being generated and shared on a daily basis within schools, most teachers and DSLs can no longer monitor the safety and wellbeing of their students without the use of specialist safeguarding software. In this paper, we experiment with the use of state-of-the-art neural network models on the modelling of a dataset of almost 9,000 anonymised child-generated chat messages on the Microsoft Teams platform. The data was manually classified into eight fine-grained classes of safeguarding concerns (or false alarms) that a monitoring program would be interested in, and these were further split into two binary classes: true positives (real safeguarding concerns) and false positives (false alarms). For the fine grained classification, our models achieved a macro F1 score of 73.56, while for the binary classification, we achieved a macro F1 score of 87.32. This first experiment into the use of Deep Learning for detecting safeguarding concerns represents an important step towards achieving high-accuracy and reliable monitoring information for busy teachers and safeguarding leads.