Abstract

The Men’s Rights Activism (MRA) movement and its sub-movement The Red Pill (TRP), has flourished online, offering support and advice to men who feel their masculinity is being challenged by societal shifts. Whilst some insightful studies have been carried out, the small samples analysed by researchers limits the scope of studies, which is small compared to the large amounts of data that TRP produces. By extracting a significant quantity of content from a prominent MRA website, ReturnOfKings.com (RoK), whose creator is one of the most prominent figures in the manosphere and who has been featured in multiple studies. Research already completed can be expanded upon with topic modelling and neural networked machine learning, computational analysis that is proposed to augment methodologies of open coding by automatically and unbiasedly analysing conceptual clusters. The successes and limitations of this computational methodology shed light on its further uses in sociological research and has answered the question: What can topic modeling demonstrate about the men’s rights activism movement’s prescriptive masculinity? This methodology not only proved that it could replicate the results of a previous study, but also delivered insights into an increasingly political focus within TRP, and deeper perspectives into the concepts identified within the movement.

Highlights

  • The internet permits more taboo and extreme expressions through anonymity (Lyons 2017) and post-geographical connectedness (Sardar 1995)

  • Processing (NLP) methodologies are the solution to this problem

  • This paper aims to add to this work by diving deeper into one of the sites studied, Return of Kings, using topic modeling methodology to attempt to replicate the coded themes identified in “Masculinities in Cyberspace”

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Summary

Introduction

The internet permits more taboo and extreme expressions through anonymity (Lyons 2017) and post-geographical connectedness (Sardar 1995). Using Latent Dirichlet allocation (LDA) (Deerwester et al 1990) and Word2vec (Mikolov et al 2013), topics within documents can be grouped and words can be queried to find similarities to other words, which can give insights into the author’s overall views without having to read a significant portion of their corpus. This methodology could be extended by taking into account more sources, including online forums (Mountford 2015)

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