@conference {333, title = {A Socio-linguistic Model for Cyberbullying Detection}, booktitle = {International Conference on Advances in Social Networks Analysis and Mining (ASONAM)}, year = {2018}, abstract = {

Cyberbullying is a serious threat to both the short and long-term well-being of social media users. Addressing this problem in online environments demands the ability to automatically detect cyberbullying and to identify the roles that participants assume in social interactions. As cyberbullying occurs within online communities, it is also vital to understand the group dynamics that support bullying behavior. To this end, we propose a socio-linguistic model which jointly detects cyberbullying content in messages, discovers latent text categories, identifies participant roles and exploits social interactions. While our method makes use of content that is labeled as bullying, it does not require category, role or relationship labels. Furthermore, as bullying labels are often subjective, noisy and inconsistent, an important contribution of our paper is effective methods for leveraging inconsistent labels. Rather than discard inconsistent labels, we evaluate different methods for learning from them, demonstrating that incorporating uncertainty allows for better generalization. Our proposed socio-linguistic model achieves an 18\% improvement over state-of-the-art methods.

}, author = {Tomkins, Sabina and Lise Getoor and Chen, Yunfei and Zhang, Yi} } @conference {tomkins:lld2017, title = {Detecting Cyber-bullying from Sparse Data and Inconsistent Labels}, booktitle = {Learning with Limited Labeled Data (LLD) NIPS Workshop}, year = {2017}, author = {Tomkins, Sabina and Lise Getoor and Chen, Yunfei and Zhang, Yi} }