Towards Identification, Classification and Analysis of Hate Speech on Social Media

Abstract

The use of Internet and Social Media has increased exponentially during the last few years globally giving the opportunity to interact with other people and share ideas, thoughts, opinions etc. Large-scale data are shared every day through social media platforms with enormous speed and reach an incredibly huge number of people. Additionally, the possibility of writing anonymous posts and comments makes it even more easy to express and spread hate speech. Social media platforms, in order to improve the experience of their users, are trying to eliminate comments expressing hatred. In this thesis, we mainly focus on developing automated techniques to identify hate-speech. We start with Traditional techniques used for hate-speech detection, followed by advanced techniques for identifying hate-speech on social media. Lastly we attempt to identify cases where these automated systems can fail miserable and propose technique to make these systems robust. Traditionally, various social media platforms, such as Wikipedia, Facebook, YouTube etc, employ hundreds of staff members as part of their human review team which manually read every reported post and decide if it is inappropriate for the users. Nowadays, having administrators detecting which comments are offensive or relying on user reports are ineffective methods not only due to the large-scale of data produces through social media and also due to the fact that during the last years an increase of hatred is noticed in the modern societies. As of 2018, there are about 317,000 status updates on Facebook every 60 seconds making it harder than finding a needle in a haystack every 60 seconds! We commence our research work with the problem of hate-speech detection on social media. Popular techniques like handcrafted feature-based Machine Learning and rule-based approaches have their own disadvantages. The former method is hard to scale and generalize, while the latter fails to provide good results making it unreliable for use in production level systems. Motivated by the improvements in the computational resources and availability of large annotated datasets, we work towards building deep learning based approaches for hate-speech detection. We propose composite models which utilizes the capability of deep models for generating concise representations along with the power of traditional machine learning classifiers. Every word has at least one meaning when it stands alone. But the meaning can change depending on context, or even over time. A sentence full of neutral words can be hostile (“Only whites should have rights”), and a sentence packed with potentially hostile words (“F**k what, f**k whatever y’all been wearing”) can be neutral when you recognize it as a Kanye West lyric. Motivated by this idea, we then move towards building techniques that utilize the contextual information instead of using just the content for identifying hate-speech. We propose a Context-based neural network architecture that utilizes the relationship between the content & context for identifying hate-speech text. Modeling relationship between the content & the neighboring text (known as context), helps in resolving disambiguity in cases where its very difficult to identify if the text is abusive or not just from the content. Finally we identify issues with the automated hate-speech detection systems and propose novel techniques to build robust models. Use of social-media data for training automated systems poses risk of it learning biases which are abundant on social media. Almost every forum is biased in some form, either women, gender, race, right-wing groups, gay/lesbians, religion etc. Learning powerful systems that can change the user experience requires the system to be fair towards every religion, group or gender. The artificial intelligence can not yet detect/understand these lingual nuances. We propose techniques that identify a type of bias, stereotypical bias, in an arbitrary model and then propose techniques on we can learn unbiased-artificially intelligent systems by observations from the biased social media. We carry out extensive experiments to establish the effectiveness of our methods. We also perform experiments to prove the efficacy of our model on a real-world API, the Perspective API, and show its effectiveness in identifying biased systems.