Reddit’s men’s rights community (/r/MensRights) has been criticized for the promotion of misogynistic language, toxic masculinity and discourses that reinforce alt-right ideologies. Conversely, the men’s liberation (/r/MensLib) community integrates inclusive politics, intersectionality and masculinity within a broad umbrella of self-reflection that suggests toxic masculinity harms men as well as women. We use machine learning text classifiers, keyword frequencies, and qualitative approaches first to distinguish these two subreddits, and second to interpret the differences ideologically rather than topically. We further integrate platform metadata (referred to as ‘platform signals’) to distinguish the subreddits. These signals help us understand how similar terms can be used to arrive at different interpretations of gender and discrimination. Where /r/MensLib tends to see masculinity as an adjective and women as peers, /r/MensRights views being a man as an essential quality, men as the target of discrimination, and women as sources of personalized grievances.

The power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts. This idea is specifically invoked by noting that "you shall know a word by the company it keeps," a quote from British linguist J.R. Firth who, along with his American colleague Zellig Harris, is often credited with the invention of "distributional semantics." While both Firth and Harris are cited in all major NLP textbooks and many foundational papers, the content and differences between their theories is seldom discussed. Engaging in a close reading of their work, we discover two distinct and in many ways divergent theories of meaning. One focuses exclusively on the internal workings of linguistic forms, while the other invites us to consider words in new company-not just with other linguistic elements, but also in a broader cultural and situational context. Contrasting these theories from the perspective of current debates in NLP, we discover in Firth a figure who could guide the field towards a more culturally grounded notion of semantics. We consider how an expanded notion of "context" might be modeled in practice through two different strategies: comparative stratification and syntagmatic extension. tics, Cambridge Handbooks in Language and Linguistics, pages 11-34.

The power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts. This idea is specifically invoked by noting that "you shall know a word by the company it keeps," a quote from British linguist J.R. Firth who, along with his American colleague Zellig Harris, is often credited with the invention of "distributional semantics." While both Firth and Harris are cited in all major NLP textbooks and many foundational papers, the content and differences between their theories is seldom discussed. Engaging in a close reading of their work, we discover two distinct and in many ways divergent theories of meaning. One focuses exclusively on the internal workings of linguistic forms, while the other invites us to consider words in new company-not just with other linguistic elements, but also in a broader cultural and situational context. Contrasting these theories from the perspective of current debates in NLP, we discover in Firth a figure who could guide the field towards a more culturally grounded notion of semantics. We consider how an expanded notion of "context" might be modeled in practice through two different strategies: comparative stratification and syntagmatic extension.

Despite the increasing popularity of NLP in the humanities and social sciences, advances in model performance and complexity have been accompanied by concerns about interpretability and explanatory power for sociocultural analysis.One popular model that balances complexity and legibility is Word Mover's Distance (WMD). Ostensibly adapted for its interpretability, WMD has nonetheless been used and further developed in ways which frequently discard its most interpretable aspect: namely, the word-level distances required for translating a set of words into another set of words. To address this apparent gap, we introduce WMDecompose: a model and Python library that 1) decomposes document-level distances into their constituent word-level distances, and 2) subsequently clusters words to induce thematic elements, such that useful lexical information is retained and summarized for analysis. To illustrate its potential in a social scientific context, we apply it to a longitudinal social media corpus to explore the interrelationship between conspiracy theories and conservative American discourses. Finally, because of the full WMD model's high time-complexity, we additionally suggest a method of sampling document pairs from large datasets in a reproducible way, with tight bounds that prevent extrapolation of unreliable results due to poor sampling practices.

In recent years, the concept of “misogynistic extremism” has emerged as a subject of interest among scholars, governments, law enforcement personnel, and the media. Yet a consistent understanding of how misogynistic extremism is defined and conceptualized has not yet emerged. Varying epistemological orientations may contribute to the current conceptual muddle of this topic, reflecting long-standing and on-going challenges with the conceptualization of its individual components. To address the potential impact of misogynistic extremism (i.e., violent attacks), a more precise understanding of what this phenomenon entails is needed. To summarize the existing knowledge base on the nature of misogynistic extremism, this scoping review analyzed publications within English-language peer-reviewed and gray literature sources. Seven electronic databases and citation indexes were systematically searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist and charted using the 2020 PRISMA flow diagram. Inclusion criteria included English peer-reviewed articles and relevant gray literature publications, which contained the term “misogynistic extremism” and other closely related terms. No date restrictions were imposed. The search strategy initially yielded 475 publications. After exclusion of ineligible articles, 40 publications remained for synthesis. We found that misogynistic extremism is most frequently conceptualized in the context of misogynistic incels, male supremacism, far-right extremism, terrorism, and the black pill ideology. Policy recommendations include increased education among law enforcement and Countering and Preventing Violent Extremism experts on male supremacist violence and encouraging legal and educational mechanisms to bolster gender equality. Violence stemming from misogynistic worldviews must be addressed by directly acknowledging and challenging socially embedded systems of oppression such as white supremacy and cisheteropatriarchy.