Cyber Bullying System

 

Cyberbullying can occur through SMS, Text, and apps, or online in social media, forums, or gaming where people can view, participate in, or share content. Cyberbullying includes sending, posting, or sharing negative, harmful, false, or mean content about someone else. Cyberbullying can harm the online reputations of everyone involved – not just the person being bullied, but those doing the bullying or participating in it. National Crime Prevention Council reported that more than 40% of teenagers in the US have suffered from being cyberbullied [1]. Multiple studies highlight the various negative effects of cyberbullying which include deep emotional trauma, psychological and psychosomatic disorders etc. Hence, to keep the online space safe, it is vital to detect and mitigate cyberbullying.

Cyberbullying is a continuous temporal phenomenon rather than one-off incident. Hence it makes sense to not only characterize cyberbullying event based on the textual content, but also use temporal characteristics of the event. This project aims to combine several temporal and textual context of such cyberbullying events to identify cyberbullying. We use the temporal characteristics given by Soni et al. [2], as they show promising results when combined with textual features. Further, social features, are taken from a highly successful rumor-detection system [3]. These features will be incorporated through an attention mechanism to a hierarchical bi-directional long short-term memory model (H-BLSTM) that is trained using the actual textual content. We use a recurrent neural network (RNN) as they easily learn the latent textual representation in a time-series stream. Thus, through various types of information about the event, we use a neural network to identify a cyberbullying incident. We use an Instagram based dataset that contains 678 bullying and 1540 non-bullying events.

Formally, we define our problem as follows:

Given – A dataset of sessions which are Instagram posts. Each session consists includes the submitted image (as an URL), social information (number of followers and follows for the original poster, and number of shares for the image), and the associated textual comments. Each session is also hand-labelled as representing cyber-bullying or not.

Problem – Train an H-BLSTM with textual data from comments and a combination of social and temporal features as attention mechanisms. Classify new events as showing cyber-bullying. Further, compare the model’s accuracy, precision, recall, and F1-score with scores provided by a related technique on the same dataset [2]. Additionally, get the minimum number of comments required to detect cyber-bullying thus facilitating an early cyberbullying detection.

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