Cyberbullying Identification Using Participant-Vocabulary Consistency
This addresses cyberbullying detection for social media analysis, but it appears incremental as it builds on existing methods for vocabulary and network challenges.
The study tackled cyberbullying identification by proposing a model that discovers instigators, victims, and new bullying vocabulary using participant-vocabulary consistency, and demonstrated its effectiveness on Twitter and Ask.fm datasets.
With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important topic in social network analysis. Machine learning methods can potentially help provide better understanding of this phenomenon, but they must address several key challenges: the rapidly changing vocabulary involved in cyber- bullying, the role of social network structure, and the scale of the data. In this study, we propose a model that simultaneously discovers instigators and victims of bullying as well as new bullying vocabulary by starting with a corpus of social interactions and a seed dictionary of bullying indicators. We formulate an objective function based on participant-vocabulary consistency. We evaluate this approach on Twitter and Ask.fm data sets and show that the proposed method can detect new bullying vocabulary as well as victims and bullies.