Analysing the Spread of Toxicity on Twitter

The spread of hate speech on social media platforms has become a rising concern in recent years. Understanding the spread of hate is crucial for mitigating its harmful effects and fostering a healthier online environment. In this paper, we propose a new model to capture the evolution of toxicity in a network - if a tweet with a certain toxicity (hatefulness) is posted, how much toxic a social network will become after a given number of rounds. We compute a toxicity score for each tweet, indicating the extent of the hatefulness of that tweet. Toxicity spread has not been adequately addressed in the existing literature. The two popular paradigms for modelling information spread, namely the Susceptible-Infected-Recovered (SIR) and its variants, as well as the spreading-activation models (SPA), are not suitable for modelling toxicity spread. The first paradigm employs a threshold and categorizes tweets as either toxic or non-toxic, while the second paradigm treats hate as energy and applies energy-conversion principles to model its propagation. Through analysis of a Twitter dataset consisting of 19.58 million tweets, we observe that the total toxicity, as well as the average toxicity of original tweets and retweets in the network, does not remain constant but rather increases over time. In this paper, we propose a new method for toxicity spread. First, we categorize users into three distinct groups: Amplifiers, Attenuators, and Copycats. These categories are assigned based on the exchange of toxicity by a user, with Amplifiers sending out more toxicity than they receive, Attenuators experiencing a higher influx of toxicity compared to what they generate, and Copycats simply mirroring the hate they receive. We perform extensive experimentation on Barabasi-Albert (BA) graphs, as well as subgraphs extracted from the Twitter dataset. Our model is able to replicate the patterns of toxicity.