Sentiment Polarization in Online Social Networks: The Flow of Hate Speech

Abstract

The influence of sentiment polarization and exchange in online social networks has been growing and studied by many researchers and organizations worldwide. For example, the sentiments expressed in a text concerning a topic in the discussion tend to influence a community when a Twitter user retweets the original text, causing a chain of reactions within a network. This paper investigates sentiment polarization in Twitter, focusing on tweets with the hashtags #Coronavirus, #ClimateChange, #Immigrants, and #MeToo. Specifically, we collect the tweets mentioned above and classify them into five categories: hate speech, offensive, sexism, positive, and neutral. In this context, we address the problem as a multiclass classification problem by using the pre-trained language models ULMFiT and AWD-LSTM, which achieved a Fmicro of 0.85. Finally, we use the classified dataset to conduct a case study in which we capture the sentiment orientation during the network evolution.