Detecting Online Echo Chambers Gradients with Bidirectional Encoder Representations from Transformers

Social Media and the online world has provided a tool to connect with people and could be considered one of the largest undertakings we have taken as a species, to connect with others in real time, anywhere around the globe. With this expanded conversation and the ability to archive and store these conversations for generations to come, these platforms have provided benefits socially and politically in sharing more information than ever before. However, whilst more people are able to express themselves online, the western world has further become politically divided. During both political and non-political times, the power of influencing voter thoughts is a valuable asset for all political parties that can attain it. Whether this is or is not intentional, the age of big data and recommendation algorithms has changed the way that platforms influence viewers at the cost of balanced viewpoints and the diversity of thought, leading to aggressive and tribal/antisocial behaviour. This paper is not proposing to prevent or lessen viewpoints, but instead to identify groups that lack regular external opinions and instead assimilate views that reinforce their own, causing unfounded alienation and generalisations towards an opposing group or individual. In an attempt to mitigate this problem, it is first necessary to determine where echo chambers are within a network. We propose a novel approach in detecting online echo chambers by applying computer vision edge detection algorithms to determine the size and location of conforming groups. These groups will be based on user's node interactions by using Bidirectional Encoder Representations from Transformers (BERT) models to predict interaction factors such as the topic of conversation, the user's stance on the topic, the user's sentiment and their exposure. These models' values will then map out a network of users where we will assess its accuracy in detecting an echo chamber via ground truth using Artificial Intelligence.