Methods for detecting echo chambers in social media networks

Abstract

This thesis presents an approach to using Natural Language Processing to detect echo chambers in social media networks and to find identifying terms for those echo chambers. A dataset consisting of posts and user information from the micro-blogging service Twitter related to Sweden’s application to join the North Atlantic Treaty Organization was collected for the year leading up to the Swedish national election of 2022. Tight-knit communities of users on the platform were extracted using the Infomap and Leiden Algorithms based on user connections and interactions. From each community found using these methods, the corpus composed of the text postings of the users in that community was used to train a Word2Vec model to recover vector word embeddings for key words related to the subject of the discussion. Semantic change was quantified by assessing the differences in cosine similarity between pairs of words over time and between communities. Changes in the use of terms related to the subject over time were observed, but patterns representing possible echo chambers arose only with the aid of manual annotation of user positions on the issue. Conclusions could not be drawn about how successful the method is from the results alone, as evidence suggests that the issue was insufficiently polarized to generate strong echo chambers.