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Considering the radicalisation of interest-based communities outside of mainstream culture, the ability to trace linguistic biases on platforms such as Reddit is of importance

Discovering and Categorising Language Biases in Reddit

ICWSM, pp.140-151, (2021)

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Abstract

We present a data-driven approach using word embeddings to discover and categorise language biases on the discussion platform Reddit. As spaces for isolated user communities, platforms such as Reddit are increasingly connected to issues of racism, sexism and other forms of discrimination. Hence, there is a need to monitor the language o...More
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Introduction
  • This paper proposes a general and data-driven approach to discovering linguistic biases towards protected attributes, such as gender, in online communities.
  • Reddit is a web platform for social news aggregation, web content rating, and discussion
  • It serves as a platform for multiple, linked topical discussion forums, as well as a network for shared identity-making (Papacharissi 2015).
  • Members can submit content such as text posts, pictures, or direct links, which is organised in distinct message boards curated by interest communities
  • These ‘subreddits’ are distinct message boards curated around particular topics, such as /r/pics for sharing pictures or /r/funny for posting jokes1.
  • Contributions are submitted to one specific subreddit, where they are aggregated with others
Highlights
  • This paper proposes a general and data-driven approach to discovering linguistic biases towards protected attributes, such as gender, in online communities
  • Through the use of word embeddings and the ranking and clustering of biased words, we discover and categorise biases in several Englishspeaking communities on Reddit, using these communities’ own forms of expression
  • Given a word embeddings model of a corpus and two sets of target words representing two concepts we want to compare and discover biases from, we identify the most biased words towards these concepts in the community
  • Considering the radicalisation of interest-based communities outside of mainstream culture (Marwick and Lewis 2017), the ability to trace linguistic biases on platforms such as Reddit is of importance
  • Through the use of word embeddings and similarity metrics, which leverage the vocabulary used within specific communities, we are able to discover biased concepts towards different social groups when compared against each other
  • Due to its general nature – word embeddings models can be trained on any natural language corpus – our method can complement previous research on ideological orientations and bias in online communities in general
Results
  • The WEAT tests show significant p-values (p = 10−3 for career/family, p = 0.018 for math/arts, and p = 10−2 for science/arts), indicating relevant gender biases with respect to the particular word sets
Conclusion
  • Considering the radicalisation of interest-based communities outside of mainstream culture (Marwick and Lewis 2017), the ability to trace linguistic biases on platforms such as Reddit is of importance.
  • Through the use of word embeddings and similarity metrics, which leverage the vocabulary used within specific communities, the authors are able to discover biased concepts towards different social groups when compared against each other.
  • This allows them to forego using fixed and predefined evaluative terms to define biases, which current approaches rely on.
  • Due to its general nature – word embeddings models can be trained on any natural language corpus – the method can complement previous research on ideological orientations and bias in online communities in general
Summary
  • Introduction:

    This paper proposes a general and data-driven approach to discovering linguistic biases towards protected attributes, such as gender, in online communities.
  • Reddit is a web platform for social news aggregation, web content rating, and discussion
  • It serves as a platform for multiple, linked topical discussion forums, as well as a network for shared identity-making (Papacharissi 2015).
  • Members can submit content such as text posts, pictures, or direct links, which is organised in distinct message boards curated by interest communities
  • These ‘subreddits’ are distinct message boards curated around particular topics, such as /r/pics for sharing pictures or /r/funny for posting jokes1.
  • Contributions are submitted to one specific subreddit, where they are aggregated with others
  • Objectives:

    This paper aims to bridge NLP research in social media, which far has not connected discriminatory language to protected attributes, and research tracing language biases using word embeddings.
  • The authors aim to discover the most biased terms towards a target set.
  • In this third and final experiment we aim to discover ethnic biases
  • Results:

    The WEAT tests show significant p-values (p = 10−3 for career/family, p = 0.018 for math/arts, and p = 10−2 for science/arts), indicating relevant gender biases with respect to the particular word sets
  • Conclusion:

    Considering the radicalisation of interest-based communities outside of mainstream culture (Marwick and Lewis 2017), the ability to trace linguistic biases on platforms such as Reddit is of importance.
  • Through the use of word embeddings and similarity metrics, which leverage the vocabulary used within specific communities, the authors are able to discover biased concepts towards different social groups when compared against each other.
  • This allows them to forego using fixed and predefined evaluative terms to define biases, which current approaches rely on.
  • Due to its general nature – word embeddings models can be trained on any natural language corpus – the method can complement previous research on ideological orientations and bias in online communities in general
Tables
  • Table1: Google News most relevant cluster labels (gender)
  • Table2: Datasets used in this research Years Authors Comments Unique Words
  • Table3: Most gender-biased adjectives in /r/TheRedPill
  • Table4: Comparison of most relevant cluster labels between biased words towards women and men in /r/TheRedPill
  • Table5: Comparison of most relevant cluster labels between biased words towards women and men in /r/dating advice
  • Table6: Comparison of most relevant labels between Islam and Christianity word sets for /r/atheism
  • Table7: Most relevant labels for Hispanic target set in
Download tables as Excel
Related work
  • Linguistic biases have been the focus of language analysis for quite some time (Wetherell and Potter 1992; Holmes and Meyerhoff 2008; Garg et al 2018; Bhatia 2017). Language, it is often pointed out, functions as both a reflection and perpetuation of stereotypes that people carry with them. Stereotypes can be understood as ideas about how (groups of) people commonly behave (van Miltenburg 2016). As cognitive constructs, they are closely related to essentialist beliefs: the idea that members of some social category share a deep, underlying, inherent nature or ‘essence’, causing them to be fundamentally similar to one another and across situations (Carnaghi et al 2008). One form of linguistic behaviour that results from these mental processes is that of linguistic bias: ‘a systematic asymmetry in word choice as a function of the social category to which the target belongs.’ (Beukeboom 2014, p.313).

    The task of tracing linguistic bias is accommodated by recent advances in AI (Aran, Such, and Criado 2019). One of the most promising approaches to trace biases is through a focus on the distribution of words and their similarities in word embedding modelling. The encoding of language in word embeddings answers to the distributional hypothesis in linguistics, which holds that the statistical contexts of words capture much of what we mean by meaning (Sahlgren 2008). In word embedding models, each word in a given dataset is assigned to a high-dimensional vector such that the geometry of the vectors captures semantic relations between the words – e.g. vectors being closer together correspond to distributionally similar words (Collobert et al 2011). In order to capture accurate semantic relations between words, these models are typically trained on large corpora of text. One example is the Google News word2vec model, a word embeddings model trained on the Google News dataset (Mikolov et al 2013).
Funding
  • This work was supported by EPSRC under grant EP/R033188/1
Study subjects and analysis
cases: 3
These WEAT experiments compare the association between male and female target sets to evaluative sets indicative of gender binarism, including career Vs family, math Vs arts, and science Vs arts, where the first sets include a-priori male-biased words, and the second include female-biased words (see Appendix C). In all three cases, the WEAT tests show significant p-values (p = 10−3 for career/family, p = 0.018 for math/arts, and p = 10−2 for science/arts), indicating relevant gender biases with respect to the particular word sets. Next, we use our approach on the Google News dataset to discover the gender biases of the community, and to identify whether the set of conceptual biases and USAS labels confirms the findings of previous studies with respect to arts, science, career and family

clusters for women: 750
The experiment is performed with a reduction factor r = 0.15, although this value could be modified to zoom out/in the different clusters (see Appendix A). After selecting the most biased nouns and adjectives, the k-means clustering partitioned the resulting vocabulary in 750 clusters for women and man. There is no relevant average prior sentiment difference between male and female-biased clusters

clusters for women: 38
We now compare with the biases that had been tested in prior works by, first, mapping the USAS labels related to career, family, arts, science and maths based on an analysis of the WEAT word sets and the category descriptions provided in the USAS website (see Appendix C), and second, evaluating how frequent are those labels among the set of most biased words towards women and men. The USAS labels related to career are more frequently biased towards men, with a total of 24 and 38 clusters for women and men, respectively, containing words such as ‘barmaid’ and ‘secretarial’ (for women) and ‘manager’ (for men). Family-related clusters are strongly biased towards women, with twice as many clusters for women (38) than for men (19)

clusters for women: 4
Words clustered include references to ‘maternity’, ‘birthmother’ (women), and also ‘paternity’ (men). Arts is also biased towards women, with 4 clusters for women compared with just 1 cluster for men, and including words such as ‘sew’, ‘needlework’ and ‘soprano’ (women). Although not that frequent among the set of the 5000 most biased words in the community, labels related to science and maths are biased towards men, with only one cluster associated with men but no clusters associated with women

users: 300000
The main subreddit we analyse for gender bias is The Red Pill (/r/TheRedPill). This community defines itself as a forum for the ‘discussion of sexual strategy in a culture increasingly lacking a positive identity for men’ (Watson 2016), and at the time of writing hosts around 300,000 users. It belongs to the online Manosphere, a loose collection of misogynist movements and communities such as pickup artists, involuntary celibates (‘incels’), and Men Going Their Own Way (MGTOW)

members: 908000
A similar difference is observed when looking at male-biased clusters with the highest rank: Power, organizing (ranked 1st for men) is ranked 61st for women, while other labels such as Egoism (5th) and Toughness; strong/weak (7th), are not even present in female-biased labels. Comparison to /r/Dating Advice In order to assess to which extent our method can differentiate between more and less biased datasets – and to see whether it picks up on less explicitly biased communities – we compare the previous findings to those of the subreddit /r/dating advice, a community with 908,000 members. The subreddit is intended for users to ‘Share (their) favorite tips, ask for advice, and encourage others about anything dating’

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Author
Xavier Ferrer
Xavier Ferrer
Tom van Nuenen
Tom van Nuenen
Jose M. Such
Jose M. Such
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