FALCON: Detecting and classifying abusive language in social networks using context features and unlabeled data

Abstract

Social networks have grown into a widespread form of communication that allows a large number of users to participate in conversations and consume information at any time. The casual nature of social media allows for nonstandard terminology, some of which may be considered rude and derogatory. As a result, a significant portion of social media users is found to express disrespectful language. This problem may intensify in certain developing countries where young children are granted unsupervised access to social media platforms. Furthermore, the sheer amount of social media data generated daily by millions of users makes it impractical for humans to monitor and regulate inappropriate content. If adolescents are exposed to these harmful language patterns without adequate supervision, it could negatively impact their behavior and well-being. This paper presents FALCON, a framework for abusive language detection that utilizes context features and unlabeled data through a co-training approach to improve classification performance in low-resource settings.