Investigating the Far-Right Online: Using Text Data to Understand Online Subcultures

This contribution provides an introduction for social science researchers on the use of computational methods within investigative research for analysing large text corpora to develop an understanding of online communities and subcultures. It offers a case study of the MineChans project, which utilised such methods in investigating the relationship between the online discussions on a collection of anonymous image-board forums, including 4chan and 8chan, and real-world, offline, attacks by right-wing extremists, making these forums a radicalising milieu. While these analytical techniques are new, they are actually fairly easy for social researchers to implement due to the nature of contemporary high-level programming languages such as Python

NCRM Text Data Workshop- part 2: Scraping web pages

In this second of three videos, Dr Lewys Brace builds upon a basic introduction to Python. He looks at how to use knowledge to build a web scraper to scrape simple text data from a website. He also does some basic analysis about text data

NCRM Text Data Workshop- part 3: Basic text data analysis

In this third and final part of the series on text data workshops, Dr Lewys Brace looks at how to carry out some basic text data analysis

NCRM Text Data Workshop- part 1: Quick introduction to Python

In this video, the first of a series of three, Dr Lewys Brace introduces the programming language Python. He provides a brief overview and explains key elements of the software needed to complete the tasks in the second and third video

An investigation into the influence of population structures and dynamics on the emergence of linguistic systems through iterated learning

Human language pervades in a complex and ever-changing social milieu, and although the tendency and ability to learn languages are clearly innate, given the rate at which lexical items change, it is clear that social-cultural factors and ontogenetic development play a significant role in the way in which languages change over time. This has resulted in research concerned with human language evolution being dominated by two, umbrella-like, research questions. First, to what extent is the human language faculty the result of genetic endowment, and to what extent might it result from non-evolutionary factors such as constraints imposed by the fundamental nature of observational learning and social interaction? Second, to what extent are the observed characteristics of human language the result of evolutionary selection on language users, and to what extent are they the result of individuals shaping languages during their usage? This thesis is concerned with both questions, and focuses specifically on the role of social learning inshaping language.There is now a growing body of work which indicates that much of the contemporary linguistic form seen in languages around the world is the result of said languages being influenced by the population structure and social dynamics of their language communities. This, combined with emerging evidence that suggests a strong association between the origins of human language and a coincidental, and dramatic, shift in social structure, means that investigating the nature of the relationship between linguistic form and social structure has the potential to offer powerful insights into the nature of human language evolution.This thesis explores this notion of a relationship between the structure of a language community and the linguistic structures that their language exhibits by modelling language changes as arising within the context of a social-coordination problem. In doing so, it utilises a specific form of expression/induction simulations known as iterated learning models. The key principle of these models is that the training data offered to a language learner is, itself, the result of training and learning on the part of another language user.Four different models are presented here. The first introduces the concept of iterated learning, and explores how compositional languages emerge in a population of language users. The second adopts the principles of Roth-Erev reinforcement learning to look at the evolution of term-based languages; again, in a population of language users. The third, uses both the iterated learning framework and the principles of Roth-Erev reinforcement learning in order to explore the nature of linguistic change in a situation whereby agents create their own signals and syntactic rules while their population size is in a state of flux. The final model is adapted from the third, and explores the emergence of contact languages that tend to arise when independent language communities interact.All four models demonstrate that the structure and make-up of a population influences the dynamics of language change over generational time. Specifically, it is shown that, by increasing the number of trainers from which an agent learns, the agent in question tends to learn a more expressive and stable language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds, even if a learner's trainers include other agents that do not yet possess full linguistic competence.Importantly, the findings presented here demonstrate that it is not population size per se that dictates how long, if at all, a fully expressive and stable linguistic system takes to emerge. Rather, it is how proportionally interconnected a given agent is to other agents in the social group that dictates the success of said population's language.In addition, the final model, which looks at the nature of pidgin and creole language emergence, presents two key findings. First, and in contrast to the common claim within the pidgin and creole literature, social power need not play a key role in pidgin emergence. Here, the pidginisation process needed to be a bilateral process, with both parties contributing to the subsequent pidgin in order for a successful contact language to exist between the two different populations. Secondly, this model looked at the concept of tertiary hybridisation; the belief that a pidgin will have to be used as the lingua franca between two groups who do not possess a common language, and whose speakers are not native speakers of the original target language. The data from these model runs indicated that, when two groups without any common language come together, tertiary hybridisation is necessary in order for a creole to emerge; otherwise, the resulting language is an entirely new linguistic system.In summary, the results of these models demonstrate that the evolution of language does indeed have an intimate relationship with population structure and social dynamics. In that linguistic variations and systems become more stable in situations where language users have a higher level of interconnectivity with the rest of the population. The reason for this is shown to be due to the way in which languages themselves evolve in response to individual learner biases so as to become easier to learn. In other words, as language users learn the linguistic system of their particular social group, the language is essentially exposed to a refinement process as it is past on from one generation to the next. Furthermore, although it has been argued that, in order for a language to be learnable, its structure has to adhere to certain constraints placed upon its structure, and that any language that violates such a 'linguistic blueprint' would not exist because it would be unlearnable, the findings presented here demonstrate that this refinement process is highly efficient at producing similar results; even when input is highly variable and inconsistent

Achieving compositional language in a population of iterated learners

Iterated learning takes place when the input into a particular individual’s learning process is itself the output of another individual’s learning process. This is an important feature to capture when investigating human language change, or the dynamics of culturally learned behaviours in general. Over the last fifteen years, the Iterated Learning Model (ILM) has been used to shed light on how the population-level characteristics of learned communication arise. However, until now each iteration of the model has tended to feature a single immature language user learning from their interactions with a single mature language user. Here, the ILM is extended to include a population of immature and mature language users. We demonstrate that the structure and make-up of this population influences the dynamics of language change that occur over generational time. In particular, we show that, by increasing the number of trainers from which an agent learns, the agent in question learns a fully compositional language at a much faster rate, and with less training data. It is also shown that, so long as the number of mature agents is large enough, this finding holds even if a learner’s trainers include other agents that do not yet posses full linguistic competence

CAN TOXIC MASCULINITIES BE DE-RADICALISED?: MAPPING THE DYNAMICS AND SPREAD OF INCEL IDEOLOGY ONLINE

In recent years, male supremacist and anti-women formations have become increasingly prevalent online. In particular, considerable attention has been focused on the incel (involuntary celibate) community due to a number of high-profile mass killings in the United States, Canada and, more recently, the UK. Incel ideology is a misogynistic formation, whose male proponents blame women for their lack of sexual activity. It operates in the broader virtual space of the manosphere, a loose conglomerate of online communities spread across various digital platforms, which are united in their antipathy toward feminism, their belief in evolutionary psychology and their adherence to the Red Pill (a process of enlightenment, whereby one comes to understand the world as a liberal feminist conspiracy that disadvantages men). This research tracks the dynamic pathways by which incel ideology spreads within and across online communities, digital platforms and geographical spaces, with a view to better understanding processes of radicalization, including ‘algorithmic radicalization.’ We also explore the dynamic interplay between incel and alt-right rhetoric. Understanding the contagion dynamics of extremist ideas - how such ideas circulate, gain new audiences, and morph into new ones - is crucial to researchers, educators, platforms and security practitioners: However, theoretical and practical understanding of the online contagion of extremist ideologies is lacking. It is only by understanding these ‘pilling pipelines’ (Ging and Murphy 2021) that effective interventions can be developed, whether educational, technological, legal or platform-governance-related