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Discovering and Categorising Language Biases in Reddit

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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 of these groups. One of the most promising AI approaches to trace linguistic biases in large textual datasets involves word embeddings, which transform text into high-dimensional dense vectors and capture semantic relations between words. Yet, previous studies require predefined sets of potential biases to study, e.g., whether gender is more or less associated with particular types of jobs. This makes these approaches unfit to deal with smaller and community-centric datasets such as those on Reddit, which contain smaller vocabularies and slang, as well as biases that may be particular to that community. This paper proposes a data-driven approach to automatically discover language biases encoded in the vocabulary of online discourse communities on Reddit. In our approach, protected attributes are connected to evaluative words found in the data, which are then categorised through a semantic analysis system. We verify the effectiveness of our method by comparing the biases we discover in the Google News dataset with those found in previous literature. We then successfully discover gender bias, religion bias, and ethnic bias in different Reddit communities. We conclude by discussing potential application scenarios and limitations of this data-driven bias discovery method.
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Discovering and Categorising Language Biases in Reddit
Xavier Ferrer+, Tom van Nuenen+, Jose M. Such+and Natalia Criado+
+Department of Informatics, King’s College London
{xavier.ferrer aran, tom.van nuenen, jose.such, natalia.criado}@kcl.ac.uk
Abstract
We present a data-driven approach using word embeddings
to discover and categorise language biases on the discus-
sion platform Reddit. As spaces for isolated user commu-
nities, platforms such as Reddit are increasingly connected
to issues of racism, sexism and other forms of discrimina-
tion. Hence, there is a need to monitor the language of these
groups. One of the most promising AI approaches to trace
linguistic biases in large textual datasets involves word em-
beddings, which transform text into high-dimensional dense
vectors and capture semantic relations between words. Yet,
previous studies require predefined sets of potential biases to
study, e.g., whether gender is more or less associated with
particular types of jobs. This makes these approaches un-
fit to deal with smaller and community-centric datasets such
as those on Reddit, which contain smaller vocabularies and
slang, as well as biases that may be particular to that com-
munity. This paper proposes a data-driven approach to auto-
matically discover language biases encoded in the vocabulary
of online discourse communities on Reddit. In our approach,
protected attributes are connected to evaluative words found
in the data, which are then categorised through a semantic
analysis system. We verify the effectiveness of our method by
comparing the biases we discover in the Google News dataset
with those found in previous literature. We then successfully
discover gender bias, religion bias, and ethnic bias in differ-
ent Reddit communities. We conclude by discussing potential
application scenarios and limitations of this data-driven bias
discovery method.
1 Introduction
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 English-
speaking communities on Reddit, using these communities’
own forms of expression.
Reddit is a web platform for social news aggregation, web
content rating, and discussion. It serves as a platform for
Author’s copy of the paper accepted at the International AAAI
Conference on Web and Social Media (ICWSM 2021).
Copyright c
2020, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.
multiple, linked topical discussion forums, as well as a net-
work for shared identity-making (Papacharissi 2015). Mem-
bers can submit content such as text posts, pictures, or di-
rect links, which is organised in distinct message boards cu-
rated by interest communities. These ‘subreddits’ are dis-
tinct 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.
Not least because of its topical infrastructure, Reddit has
been a popular site for Natural Language Processing stud-
ies – for instance, to successfully classify mental health
discourses (Balani and De Choudhury 2015), and domes-
tic abuse stories (Schrading et al. 2015). LaViolette and
Hogan have recently augmented traditional NLP and ma-
chine learning techniques with platform metadata, allowing
them to interpret misogynistic discourses in different sub-
reddits (LaViolette and Hogan 2019). Their focus on dis-
criminatory language is mirrored in other studies, which
have pointed out the propagation of sexism, racism, and
‘toxic technocultures’ on Reddit using a combination of
NLP and discourse analysis (Mountford 2018). What these
studies show is that social media platforms such as Reddit
not merely reflect a distinct offline world, but increasingly
serve as constitutive spaces for contemporary ideological
groups and processes.
Such ideologies and biases become especially pernicious
when they concern vulnerable groups of people that share
certain protected attributes – including ethnicity, gender, and
religion (Grgi´
c-Hlaˇ
ca et al. 2018). Identifying language bi-
ases towards these protected attributes can offer important
cues to tracing harmful beliefs fostered in online spaces.
Recently, NLP research using word embeddings has been
able to do just that (Caliskan, Bryson, and Narayanan 2017;
Garg et al. 2018). However, due to the reliance on predefined
concepts to formalise bias, these studies generally make use
of larger textual corpora, such as the widely used Google
News dataset (Mikolov et al. 2013). This makes these meth-
ods less applicable to social media platforms such as Red-
dit, as communities on the platform tend to use language
that operates within conventions defined by the social group
1Subreddits are commonly spelled with the prefix ‘/r/’.
itself. Due to their topical organisation, subreddits can be
thought of as ‘discourse communities’ (Kehus, Walters, and
Shaw 2010), which generally have a broadly agreed set of
common public goals and functioning mechanisms of inter-
communication among its members. They also share discur-
sive expectations, as well as a specific lexis (Swales 2011).
As such, they may carry biases and stereotypes that do not
necessarily match those of society at large. At worst, they
may constitute cases of hate speech, ’language that is used
to expresses hatred towards a targeted group or is intended to
be derogatory, to humiliate, or to insult the members of the
group’ (Davidson et al. 2017). The question, then, is how
to discover the biases and stereotypes associated with pro-
tected attributes that manifest in particular subreddits – and,
crucially, which linguistic form they take.
This paper aims to bridge NLP research in social media,
which thus far has not connected discriminatory language
to protected attributes, and research tracing language biases
using word embeddings. Our contribution consists of de-
veloping a general approach to discover and categorise bi-
ased language towards protected attributes in Reddit com-
munities. We use word embeddings to determine the most
biased words towards protected attributes, apply k-means
clustering combined with a semantic analysis system to la-
bel the clusters, and use sentiment polarity to further spec-
ify biased words. We validate our approach with the widely
used Google News dataset before applying it to several Red-
dit communities. In particular, we identified and categorised
gender biases in /r/TheRedPill and /r/dating advice, religion
biases in /r/atheism and ethnicity biases in /r/The Donald.
2 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 per-
petuation of stereotypes that people carry with them. Stereo-
types can be understood as ideas about how (groups of) peo-
ple 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, un-
derlying, 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 geom-
etry of the vectors captures semantic relations between the
words – e.g. vectors being closer together correspond to dis-
tributionally 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 ex-
ample is the Google News word2vec model, a word embed-
dings model trained on the Google News dataset (Mikolov
et al. 2013).
Recently, several studies have shown that word embed-
dings are strikingly good at capturing human biases in large
corpora of texts found both online and offline (Bolukbasi et
al. 2016; Caliskan, Bryson, and Narayanan 2017; van Mil-
tenburg 2016). In particular, word embeddings approaches
have proved successful in creating analogies (Bolukbasi
et al. 2016), and quantifying well-known societal biases
and stereotypes (Caliskan, Bryson, and Narayanan 2017;
Garg et al. 2018). These approaches test for predefined bi-
ases and stereotypes related to protected attributes, e.g., for
gender, that males are more associated with a professional
career and females with family. In order to define sets of
words capturing potential biases, which we call ‘evaluation
sets’, previous studies have taken word sets from Implicit
Association Tests (IAT) used in social psychology. This test
detects the strength of a person’s automatic association be-
tween mental representations of objects in memory, in or-
der to assess bias in general societal attitudes. (Greenwald,
McGhee, and Schwartz 1998). The evaluation sets yielded
from IATs are then related to ontological concepts repre-
senting protected attributes, formalised as a ‘target set’. This
means two supervised word lists are required; e.g., the pro-
tected attribute ‘gender’ is defined by target words related
to men (such as {‘he’, ‘son’, ‘brother’, . . . }) and women
({‘she’, ‘daughter’, ‘sister’, ...}), and potential biased con-
cepts are defined in terms of sets of evaluative terms largely
composed of adjectives, such ‘weak’ or ‘strong’. Bias is
then tested through the positive relationship between these
two word lists. Using this approach, Caliskan et al. were
able to replicate IAT findings by introducing their Word-
Embedding Association Test (WEAT). The cosine similar-
ity between a pair of vectors in a word embeddings model
proved analogous to reaction time in IATs, allowing the au-
thors to determine biases between target and evaluative sets.
The authors consider such bias to be ‘stereotyped’ when it
relates to aspects of human culture known to lead to harmful
behaviour (Caliskan, Bryson, and Narayanan 2017).
Caliskan et al. further demonstrate that word embeddings
can capture imprints of historic biases, ranging from morally
neutral ones (e.g. towards insects) to problematic ones (e.g.
towards race or gender) (Caliskan, Bryson, and Narayanan
2017). For example, in a gender-biased dataset, the vec-
tor for adjective ‘honourable’ would be closer to the vec-
tor for the ‘male’ gender, whereas the vector for ‘submis-
sive’ would be closer to the ‘female’ gender. Building on
this insight, Garg et.al. have recently built a framework
for a diachronic analysis of word embeddings, which they
show incorporate changing ‘cultural stereotypes’ (Garg et
al. 2018). The authors demonstrate, for instance, that during
the second US feminist wave in the 1960, the perspectives
on women as portrayed in the Google News dataset funda-
mentally changed. More recently, WEAT was also adapted
to BERT embeddings (Kurita et al. 2019).
What these previous approaches have in common is a re-
liance on predefined evaluative word sets, which are then
tested on target concepts that refer to protected attributes
such as gender. This makes it difficult to transfer these
approaches to other – and especially smaller – linguistic
datasets, which do not necessarily include the same vocab-
ulary as the evaluation sets. Moreover, these tests are only
useful to determine predefined biases for predefined con-
cepts. Both of these issues are relevant for the subreddits we
are interested in analysing here: they are relatively small,
are populated by specific groups of users, revolve around
very particular topics and social goals, and often involve spe-
cialised vocabularies. The biases they carry, further, are not
necessarily representative of broad ‘cultural stereotypes’; in
fact, they can be antithetical even to common beliefs. An
example in /r/TheRedPill, one of our datasets, is that men
in contemporary society are oppressed by women (Marwick
and Lewis 2017). Within the transitory format of the online
forum, these biases can be linguistically negotiated – and
potentially transformed – in unexpected ways.
Hence, while we have certain ideas about which kinds of
protected attributes to expect biases against in a community
(e.g. gender biases in /r/TheRedPill), it is hard to tell in ad-
vance which concepts will be associated to these protected
attributes, or what linguistic form biases will take. The ap-
proach we propose extracts and aggregates the words rele-
vant within each subreddit in order to identify biases regard-
ing protected attributes as they are encoded in the linguistic
forms chosen by the community itself.
3 Discovering language biases
In this section we present our approach to discover linguistic
biases.
3.1 Most biased words
Given a word embeddings model of a corpus (for instance,
trained with textual comments from a Reddit community)
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.
Let S1={wi, wi+1, ..., wi+n}be a set of target words w
related to a concept (e.g {he, son, his, him, father, and male}
for concept male), and #»
c1the centroid of S1estimated by
averaging the embedding vectors of word wS1. Similarly,
let S2={wj, wj+1, ..., wj+m}be a second set of target
words with centroid #»
c2(e.g. {she, daughter, her, mother, and
female}for concept female). A word wis biased towards S1
with respect to S2when the cosine similarity2between the
embedding of #»
w, is higher for #»
c1than for #»
c2.
2Alternative bias definitions are possible here, such as the direct
bias measure defined in (Bolukbasi et al. 2016). In fact, when com-
pared experimentally with our metric in r/TheRedPill, we obtain a
Jaccard index of 0.857 (for female gender) and 0.864 (for male)
regarding the list of 300 most-biased adjectives generated with the
two bias metrics. Similar results could also be obtained using the
relative norm bias metric, as shown in (Garg et al. 2018).
Bias(w, c1, c2) = cos(#»
w,
c1)cos(#»
w,
c2)(1)
where cos(u, v) = u·v
kuk2kvk2. Positive values of Bias mean
a word wis more biased towards S1, while negative values
of Bias means wis more biased towards S2.
Let Vbe the vocabulary of a word embeddings model. We
identify the kmost biased words towards S1with respect to
S2by ranking the words in the vocabulary Vusing Bias
function from Equation 2:
MostBiased(V, c1, c2) = arg max
wV
Bias(w, c1, c2)(2)
Researchers typically focus on discovering biases and
stereotypes by exploring the most biased adjectives and
nouns towards two sets of target words (e.g. female and
male). Adjectives are particularly interesting since they
modify nouns by limiting, qualifying, or specifying their
properties, and are often normatively charged. Adjectives
carry polarity, and thus often yield more interesting insights
about the type of discourses. In order to determine the part-
of-speech (POS) of a word, we use the nltk3python li-
brary. POS filtering helps us removing non-interesting words
in some communities such as acronyms, articles and proper
names (cf. Appendix A for a performance evaluation of POS
using the nltk library in the datasets used in this paper).
Given a vocabulary and two sets of target words (such as
those for women and men), we rank the words from least
to most biased using Equation 2. As such, we obtain two
ordered lists of the most biased words towards each tar-
get set, obtaining an overall view of the bias distribution in
that particular community with respect to those two target
sets. For instance, Figure 1 shows the bias distribution of
words towards women (top) and men (bottom) target sets in
/r/TheRedPill. Based on the distribution of biases towards
each target set in each subreddit, we determine the threshold
of how many words to analyse by selecting the top words
using Equation 2. All targets sets used in our work are com-
piled from previous experiments (listed in Appendix C).
Figure 1: Bias distribution in adjectives in the r/TheRedPill
3https:www.nltk.org
3.2 Sentiment Analysis
To further specify the biases we encounter, we take the sen-
timent polarity of biased words into account. Discovering
consistently strong negative polarities among the most bi-
ased words towards a target set might be indicative of strong
biases, and even stereotypes, towards that specific popula-
tion4. We are interested in assessing whether the most biased
words towards a population carry negative connotations, and
we do so by performing a sentiment analysis over the most
biased words towards each target using the nltk sentiment
analysis python library (Hutto and Gilbert 2014)5. We esti-
mate the average of the sentiment of a set of words Was
such:
Sent(W) = 1
|W|X
wW
SA(w)(3)
where SA returns a value [1,1] corresponding to the
polarity determined by the sentiment analysis system, -1
being strongly negative and +1 strongly positive. As such,
Sent(W)always returns a value [1,1].
Similarly to POS tagging, the polarity of a word depends
on the context in which the word is found. Unfortunately,
contextual information is not encoded in the pre-trained
word embedding models commonly used in the literature.
As such, we can only leverage the prior sentiment polarity
of words without considering the context of the sentence in
which they were used. Nevertheless, a consistent tendency
towards strongly polarised negative (or positive) words can
give some information about tendencies and biases towards
a target set.
3.3 Categorising Biases
As noted, we aim to discover the most biased terms towards
a target set. However, even when knowing those most biased
terms and their polarity, considering each of them as a sep-
arate unit may not suffice in order to discover the relevant
concepts they represent and, hence, the contextual meaning
of the bias. Therefore, we also combine semantically related
terms under broader rubrics in order to facilitate the com-
prehension of a community’s biases. A side benefit is that
identifying concepts as a cluster of terms, instead of using
individual terms, helps us tackle stability issues associated
with individual word usage in word embeddings (Antoniak
and Mimno 2018) - discussed in Section 5.1.
We aggregate the most similar word embeddings into
clusters using the well-known k-means clustering algorithm.
In k-means clustering, the parameter kdefines the quantity
of clusters into which the space will be partitioned. Equiva-
lently, we use the reduction factor (0,1),r=k
|V|, where
|V|is the size of the vocabulary to be partitioned. The lower
the value of r, the lower the quantity of clusters and their
average intra-similarity, estimated by assessing the average
4Note that potentially discriminatory biases can also be encoded
in a-priori sentiment-neutral words. The fact that a word is not
tagged with a negative sentiment does not exclude it from being
discriminatory in certain contexts.
5Other sentiment analysis tools could be used but some might
return biased analyses (Kiritchenko and Mohammad 2018).
similarity between all words in a cluster for all clusters in
a partition. On the other hand, when ris close to 1, we ob-
tain more clusters and a higher cluster intra-similarity, up to
when r= 1 where we have |V|clusters of size 1, with an
average intra-similarity of 1 (see Appendix A).
In order to assign a label to each cluster, which facilitates
the categorisation of biases related to each target set, we use
the UCREL Semantic Analysis System (USAS)6. USAS is a
framework for the automatic semantic analysis and tagging
of text, originally based on Tom McArthur’s Longman Lexi-
con of Contemporary English (Summers and Gadsby 1995).
It has a multi-tier structure with 21 major discourse fields
subdivided in more fine-grained categories such as People,
Relationships or Power. USAS has been extensively used for
tasks such as the automatic content analysis of spoken dis-
course (Wilson and Rayson 1993) or as a translator assistant
(Sharoff et al. 2006). The creators also offer an interactive
tool7to automatically tag each word in a given sentence with
a USAS semantic label.
Using the USAS system, every cluster is labelled with the
most frequent tag (or tags) among the words clustered in the
k-means cluster. For instance, Relationship: Intimate/sexual
and Power, organising are two of the most common labels
assigned to the gender-biased clusters of /r/TheRedPill (see
Section 5.1). However, since many of the communities we
explore make use of non-standard vocabularies, dialects,
slang words and grammatical particularities, the USAS au-
tomatic analysis system has occasional difficulties during
the tagging process. Slang and community-specific words
such as dateable (someone who is good enough for dat-
ing) or fugly (used to describe someone considered very
ugly) are often left uncategorised. In these cases, the un-
categorised clusters receive the label (or labels) of the most
similar cluster in the partition, determined by analysing the
cluster centroid distance between the unlabelled cluster and
the other cluster centroids in the partition. For instance, in
/r/TheRedPill, the cluster (interracial) (a one-word cluster)
was initially left unlabelled. The label was then updated to
Relationship: Intimate/sexual after copying the label of the
most similar cluster, which was (lesbian, bisexual).
Once all clusters of the partition are labelled, we rank all
labels for each target based on the quantity of clusters tagged
and, in case of a tie, based on the quantity of words of the
clusters tagged with the label. By comparing the rank of the
labels between the two target sets and combining it with an
analysis of the clusters’ average polarities, we obtain a gen-
eral understanding of the most frequent conceptual biases
towards each target set in that community. We particularly
focus on the most relevant clusters based on rank difference
between target sets or other relevant characteristics such as
average sentiment of the clusters, but we also include the
top-10 most frequent conceptual biases for each dataset (Ap-
pendix C).
6http://ucrel.lancs.ac.uk/usas/, accessed Apr 2020
7http://ucrel-api.lancaster.ac.uk/usas/tagger.html, accessed Apr
2020
4 Validation on Google News
In this section we use our approach to discover gender biases
in the Google News pre-trained model8, and compare them
with previous findings (Garg et al. 2018; Caliskan, Bryson,
and Narayanan 2017) to prove that our method yields rel-
evant results that complement those found in the existing
literature.
The Google News embedding model contains 300-
dimensional vectors for 3 million words and phrases, trained
on part of the US Google News dataset containing about
100 billion words. Previous research on this model reported
gender biases among others (Garg et al. 2018), and we re-
peated the three WEAT experiments related to gender from
(Caliskan, Bryson, and Narayanan 2017) in Google News.
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= 103for career/family,
p= 0.018 for math/arts, and p= 102for science/arts),
indicating relevant gender biases with respect to the particu-
lar 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 con-
firms the findings of previous studies with respect to arts,
science, career and family.
For this task, we follow the method stated in Section 3
and start by observing the bias distribution of the dataset,
in which we identify the 5000 most biased uni-gram adjec-
tives and nouns towards ’female’ and ‘male’ target sets. 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 parti-
tioned the resulting vocabulary in 750 clusters for women
and man. There is no relevant average prior sentiment dif-
ference between male and female-biased clusters.
Table 1 shows some of the most relevant labels used to
tag the female and male-biased clusters in the Google News
dataset, where R.F emale and R.M ale indicate the rank im-
portance of each label among the sets of labels used to tag
each cluster for each gender. Character ‘-’ indicates that the
label is not found among the labels biased towards the tar-
get set. Due to space limitations, we only report the most
pronounced biases based on frequency and rank difference
between target sets (see Appendix B for the rest top-ten la-
bels). Among the most frequent concepts more biased to-
wards women, we find labels such as Clothes and personal
belongings,People: Female,Anatomy and physiology, and
Judgement of appearance (pretty etc.). In contrast, labels re-
lated to strength and power, such as Warfare, defence and the
8We used the Google news model (https://code.google.com/
archive/p/word2vec/), due to its wide usage in relevant literature.
However, our method could also be extended and applied in newer
embedding models such as ELMO and BERT.
Table 1: Google News most relevant cluster labels (gender).
Cluster Label R. Female R. Male
Relevant to Female
Clothes and personal belongings 3 20
People: Female 4 -
Anatomy and physiology 5 11
Cleaning and personal care 7 68
Judgement of appearance (pretty etc.) 9 29
Relevant to Male
Warfare, defence and the army; weapons - 3
Power, organizing 8 4
Sports - 7
Crime 68 8
Groups and affiliation - 9
army; weapons,Power, organizing, followed by Sports, and
Crime, are among the most frequent concepts much more
biased towards men.
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 pro-
vided 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 la-
bels 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 ‘secre-
tarial’ (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). Words
clustered include references to ‘maternity’, ‘birthmother’
(women), and also ‘paternity’ (men). Arts is also biased to-
wards 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 fre-
quent 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. Therefore, this analysis
shows that our method, in addition to finding what are the
most frequent and pronounced biases in the Google News
model (shown in Table 1), could also reproduce the biases
tested9by previous work.
5 Reddit Datasets
The Reddit datasets used in the remainder of this paper
are presented in Table 2, where Wpc means average words
per comment, and Word Density is the average unique new
words per comment. Data was acquired using the Pushshift
data platform (Baumgartner et al. 2020). All predefined sets
of words used in this work and extended tables are included
in Appendixes B and C, and the code to process the datasets
and embedding models is available publicly10. We expect
9Note that previous work tested for arbitrary biases, which were
not claimed to be the most frequent or pronounced ones.
10https://github.com/xfold/LanguageBiasesInReddit
to find both different degrees and types of bias and stereo-
typing in these communities, based on news reporting and
our initial explorations of the communities. For instance,
/r/TheRedPill and /r/The Donald have been widely covered
as misogynist and ethnic-biased communities (see below),
while /r/atheism is, as far as reporting goes, less biased.
For each comment in each subreddit, we first preprocess
the text by removing special characters, splitting text into
sentences, and transforming all words to lowercase. Then,
using all comments available in each subreddit and using
Gensim word2vec python library, we train a skip-gram word
embeddings model of 200 dimensions, discarding all words
with less that 10 occurrences (see an analysis varying this
frequency parameter in Appendix A) and using a 4 word
window.
After training the models, and by using WEAT (Caliskan,
Bryson, and Narayanan 2017), we were able to demon-
strate whether our subreddits actually include any of the
predefined biases found in previous studies. For instance,
by repeating the same gender-related WEAT experiments
performed in Section 4 in /r/TheRedPill, it seems that the
dataset may be gender-biased, stereotyping men as related
to career and women to family (p-value of 0.013). However,
these findings do not agree with other commonly observed
gender stereotypes, such as those associating men with sci-
ence and math (p-value of 0.411) and women with arts (p-
value of 0.366). It seems that, if gender biases are occurring
here, they are of a particular kind – underscoring our point
that predefined sets of concepts may not always be useful to
evaluate biases in online communities.11
5.1 Gender biases in /r/TheRedPill
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 cul-
ture increasingly lacking a positive identity for men’ (Wat-
son 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). The name of the subreddit is a ref-
erence to the 1999 film The Matrix: ‘swallowing the red
pill,’ in the community’s parlance, signals the acceptance of
an alternative social framework in which men, not women,
have been structurally disenfranchised in the west. Within
this ‘masculinist’ belief system, society is ruled by femi-
nine ideas and values, yet this fact is repressed by feminists
and politically correct ‘social justice warriors’. In response,
men must protect themselves against a ‘misandrist’ culture
and the feminising of society (Marwick and Lewis 2017;
LaViolette and Hogan 2019). Red-pilling has become a more
general shorthand for radicalisation, conditioning young
men into views of the alt-right (Marwick and Lewis 2017).
Our question here is to which extent our approach can help
11Due to technical constraints we limit our analysis to the two
major binary gender categories – female and male, or women and
men – as represented by the lists of associated words.
in discovering biased themes and concerns in this commu-
nity.
Table 3 shows the top 7 most gender-biased adjectives
for the /r/TheRedPill, as well as their bias value and fre-
quency in the model. Notice that most female-biased words
are more frequently used than male-biased words, mean-
ing that the community frequently uses that set of words in
female-related contexts. Notice also that our POS tagging
has erroneously picked up some nouns such as bumble (a
dating app) and unicorn (defined in the subreddit’s glossary
as a ‘Mystical creature that doesn’t fucking exist, aka ”The
Girl of Your Dreams”’).
The most biased adjective towards women is casual
with a bias of 0.224. That means that the average user of
/r/TheRedPill often uses the word casual in similar con-
texts as female-related words, and not so often in similar
contexts as male-related words. This makes intuitive sense,
as the discourse in /r/TheRedPill revolves around the pur-
suit of ‘casual’ relationships with women. For men, some of
the most biased adjectives are quintessential,tactician,leg-
endary, and genious. Some of the most biased words towards
women could be categorised as related to externality and
physical appearance, such as flirtatious and fuckable. Con-
versely, the most biased adjectives for men, such as vision-
ary and tactician, are internal qualities that refer to strategic
game-playing. Men, in other words, are qualified through
descriptive adjectives serving as indicators of subjectivity,
while women are qualified through evaluative adjectives that
render them as objects under masculinist scrutiny.
Categorising Biases We now cluster the most-biased
words in 45 clusters, using r= 0.15 (see an analysis of the
effect rhas in Appendix A), generalising their semantic con-
tent. Importantly, due to this categorisation of biases instead
of simply using most-biased words, our method is less prone
to stability issues associated with word embeddings (Anto-
niak and Mimno 2018), as changes in particular words do
not directly affect the overarching concepts explored at the
cluster level and the labels that further abstract their meaning
(see the stability analysis part in Appendix A).
Table 4 shows some of the most frequent labels for the
clusters biased towards women and men in /r/TheRedPill,
and compares their importance for each gender. SentW cor-
responds to the average sentiment of all clusters tagged with
the label, as described in Equation 3. The R.Woman and
R.Male columns show the rank of the labels for the female
and male-biased clusters. ’-’ indicates that no clusters were
tagged with that label.
Anatomy and Physiology,Intimate sexual relationships
and Judgement of appearance are common labels demon-
strating bias towards women in /r/TheRedPill, while the bi-
ases towards men are clustered as Power and organising,
Evaluation, Egoism, and toughness. Sentiment scores indi-
cate that the first two biased clusters towards women carry
negative evaluations, whereas most of the clusters related
to men contain neutral or positively evaluated words. In-
terestingly, the most frequent female-biased labels, such as
Anatomy and physiology and Relationship: Intimate/sexual
Table 2: Datasets used in this research
Subreddit E.Bias Years Authors Comments Unique Words Wpc Word Density
/r/TheRedPill gender 2012-2018 106,161 2,844,130 59,712 52.58 3.99 ·104
/r/DatingAdvice gender 2011-2018 158,758 1,360,397 28,583 60.22 3.48 ·104
/r/Atheism religion 2008-2009 699,994 8,668,991 81,114 38.27 2.44 ·104
/r/The Donald ethnicity 2015-2016 240,666 13,142,696 117,060 21.27 4.18 ·104
Table 3: Most gender-biased adjectives in /r/TheRedPill.
Female Male
Adjective Bias Freq (FreqR) Adjective Bias Freq (FreqR)
bumble 0.245 648 (8778) visionary 0.265 100 (22815)
casual 0.224 6773 (1834) quintessential 0.245 219 (15722)
flirtatious 0.205 351 (12305) tactician 0.229 29 (38426)
anal 0.196 3242 (3185) bombastic 0.199 41 (33324)
okcupid 0.187 2131 (4219) leary 0.190 93 (23561)
fuckable 0.187 1152 (6226) gurian 0.185 16 (48440)
unicorn 0.186 8536 (1541) legendary 0.183 400 (11481)
Table 4: Comparison of most relevant cluster labels between
biased words towards women and men in /r/TheRedPill.
Cluster Label SentW R. Female R. Male
Relevant to Female
Anatomy and physiology -0.120 1 25
Relationship: Intimate/sexual -0.035 2 30
Judgement of appearance (pretty etc.) 0.475 3 40
Evaluation:- Good/bad 0.110 4 2
Appearance and physical properties 0.018 10 6
Relevant to Male
Power, organizing 0.087 61 1
Evaluation:- Good/bad 0.157 4 2
Education in general 0.002 - 4
Egoism 0.090 - 5
Toughness; strong/weak -0.004 - 7
(second most frequent), are only ranked 25th and 30th for
men (from a total of 62 male-biased labels). A similar differ-
ence 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 in-
tended for users to ‘Share (their) favorite tips, ask for ad-
vice, and encourage others about anything dating’. The sub-
reddit’s About-section notes that ‘[t]this is a positive com-
munity. Any bashing, hateful attacks, or sexist remarks will
be removed’, and that ‘pickup or PUA lingo’ is not ap-
preciated. As such, the community shows similarities with
/r/TheRedPill in terms of its focus on dating, but the gen-
dered binarism is expected to be less prominently present.
Table 5: Comparison of most relevant cluster labels between
biased words towards women and men in /r/dating advice.
Cluster Label SentW R. Female R. Male
Relevant to Female
Quantities 0.202 1 6
Geographical names 0.026 2 -
Religion and the supernatural 0.025 3 -
Language, speech and grammar 0.025 4 -
Importance: Important 0.227 5 -
Relevant to Female
Evaluation:- Good/bad -0.165 14 1
Judgement of appearance (pretty etc.) -0.148 6 2
Power, organizing 0.032 51 3
General ethics -0.089 - 4
Interest/boredom/excited/energetic 0.354 - 5
As expected, the bias distribution here is weaker than
in /r/TheRedPill. The most biased word towards women in
/r/dating advice is floral with a bias of 0.185, and molest
(0.174) for men. Based on the distribution of biases (follow-
ing the method in Section 3.1), we selected the top 200 most
biased adjectives towards the ‘female’ and ‘male’ target sets
and clustered them using k-means (r= 0.15), leaving 30
clusters for each target set of words. The most biased clus-
ters towards women, such as (okcupid, bumble), and (exotic),
are not clearly negatively biased (though we might ask ques-
tions about the implied exoticism in the latter term). The bi-
ased clusters towards men look more conspicuous: (poor),
(irresponsible, erratic, unreliable, impulsive) or (pathetic,
stupid, pedantic, sanctimonious, gross, weak, nonsensical,
foolish) are found among the most biased clusters. On top of
that, (abusive),(narcissistic, misogynistic, egotistical, arro-
gant), and (miserable, depressed) are among the most sen-
timent negative clusters. These terms indicate a significant
negative bias towards men, evaluating them in terms of un-
reliability, pettiness and self-importance.
No typical bias can be found among the most common
labels for the k-means clusters for women. Quantities and
Geographical names are the most common labels. The most
relevant clusters related to men are Evaluation and Judge-
ment of Appearance, together with Power, organizing. Table
5 compares the importance between some of the most rele-
vant biases for women and men by showing the difference in
the bias ranks for both sets of target words. The table shows
that there is no physical or sexual stereotyping of women
as in /r/TheRedPill, and Judgment of appearance, a strongly
female-biased label in /r/TheRedPill, is more frequently bi-
ased here towards men (rank 2) than women (rank 6). In-
stead we find that some of the most common labels used
to tag the female-biased clusters are Quantities,Language,
Table 6: Comparison of most relevant labels between Islam
and Christianity word sets for /r/atheism
Cluster Label SentW R. Islam R. Chr.
Relevant to Islam
Geographical names 0 1 39
Crime, law and order: Law and order -0.085 2 40
Groups and affiliation -0.012 3 20
Politeness -0.134 4 -
Calm/Violent/Angry -0.140 5 -
Relevant to Christianity
Religion and the supernatural 0.003 13 1
Time: Beginning and ending 0 - 2
Time: Old, new and young; age 0.079 - 3
Anatomy and physiology 0 22 4
Comparing:- Usual/unusual 0 - 5
speech and grammar or Religion and the supernatural. This,
in conjunction with the negative sentiment scores for male-
biased labels, underscores the point that /r/Dating Advice
seems slightly biased towards men.
5.2 Religion biases in /r/Atheism
In this next experiment, we apply our method to discover
religion-based biases. The dataset derives from the subred-
dit /r/atheism, a large community with about 2.5 million
members that calls itself ‘the web’s largest atheist forum’,
on which ‘[a]ll topics related to atheism, agnosticism and
secular living are welcome’. Are monotheistic religions con-
sidered as equals here? To discover religion biases, we use
target word sets Islam and Christianity (see Appendix B).
In order to attain a broader picture of the biases related to
each of the target sets, we categorise and label the clusters
following the steps described in Section 3.3. Based on the
distribution of biases we found here, we select the 300 most
biased adjectives and use an r= 0.15 in order to obtain
45 clusters for both target sets. We then count and compare
all clusters that were tagged with the same label, in order
to obtain a more general view of the biases in /r/atheism for
words related to the Islam and Christianity target sets.
Table 6 shows some of the most common clusters labels
attributed to Islam and Christianity (see Appendix B for the
full table), and the respective differences between the rank-
ing of these clusters, as well as the average sentiment of all
words tagged with each label. The ‘-’ symbol means that
a label was not used to tag any cluster of that specific tar-
get set. Findings indicate that, in contrast with Christianity-
biased clusters, some of the most frequent cluster labels bi-
ased towards Islam are Geographical names,Crime, law and
order and Calm/Violent/Angry. On the other hand, some of
the most biased labels towards Christianity are Religion and
the supernatural,Time: Beginning and ending and Anatomy
and physiology.
All the mentioned biased labels towards Islam have an
average negative polarity, except for Geographical names.
Labels such as Crime, law and order aggregate words with
evidently negative connotations such as uncivilized, misogy-
nistic, terroristic and antisemitic.Judgement of appearance,
General ethics, and Warfare, defence and the army are also
found among the top 10 most frequent labels for Islam, ag-
gregating words such as oppressive, offensive and totalitar-
ian (see Appendix B). However, none of these labels are
relevant in Christianity-biased clusters. Further, most of the
words in Christianity-biased clusters do not carry negative
connotations. Words such as unitarian, presbyterian, episco-
palian or anglican are labelled as belonging to Religion and
the supernatural,unbaptized and eternal belong to Time re-
lated labels, and biological, evolutionary and genetic belong
to Anatomy and physiology.
Finally, it is important to note that our analysis of con-
ceptual biases is meant to be more suggestive than conclu-
sive, especially on this subreddit in which various religions
are discussed, potentially influencing the embedding distri-
butions of certain words and the final discovered sets of con-
ceptual biases. Having said this, and despite the commu-
nity’s focus on atheism, the results suggest that labels bi-
ased towards Islam tend to have a negative polarity when
compared with Christian biased clusters, considering the set
of 300 most biased words towards Islam and Christianity
in this community. Note, however, that this does not mean
that those biases are the most frequent, but that they are
the most pronounced, so they may be indicative of broader
socio-cultural perceptions and stereotypes that characterise
the discourse in /r/atheism. Further analysis (including word
frequency) would give a more complete view.
5.3 Ethnic biases in /r/The Donald
In this third and final experiment we aim to discover ethnic
biases. Our dataset was taken from /r/The Donald, a sub-
reddit in which participants create discussions and memes
supportive of U.S. president Donald Trump. Initially cre-
ated in June 2015 following the announcement of Trump’s
presidential campaign, /r/The Donald has grown to become
one of the most popular communities on Reddit. Within the
wider news media, it has been described as hosting conspir-
acy theories and racist content (Romano 2017).
For this dataset, we use target sets to compare white
last names, with Hispanic names,Asian names and Rus-
sian names (see Appendix C). The bias distribution for all
three tests is similar: the Hispanic,Asian and Russian tar-
get sets are associated with stronger biases than the white
names target sets. The most biased adjectives towards white
target sets include classic,moralistic and honorable when
compared with all three other targets sets. Words such as
undocumented,undeported and illegal are among the most
biased words towards Hispanics, while Chinese and inter-
national are among the most biased words towards Asian,
and unrefined and venomous towards Russian. The average
sentiment among the most-biased adjectives towards the dif-
ferent targets sets is not significant, except when compared
with Hispanic names, i.e. a sentiment of 0.0018 for white
names and -0.0432 for Hispanics (p-value of 0.0241).
Table 7 shows the most common labels and average senti-
ment for clusters biased towards Hispanic names using r=
0.15 and considering the 300 most biased adjectives, which
is the most negative and stereotyped community among the
ones we analysed in /r/The Donald. Apart from geograph-
ical names, the most interesting labels for Hispanic vis-`
a-
Table 7: Most relevant labels for Hispanic target set in
/r/The Donald
Cluster Label SentW R. White R. Hisp.
Geographical names 0 - 1
General ethics -0.349 25 2
Wanting; planning; choosing 0 - 3
Crime, law and order -0.119 - 4
Gen. appearance, phys. properties -0.154 21 10
vis white names are General ethics (including words such
as abusive, deportable, incestual, unscrupulous, undemo-
cratic), Crime, law and order (including words such as un-
documented, illegal, criminal, unauthorized, unlawful, law-
ful and extrajudicial), and General appearance and physi-
cal properties (aggregating words such as unhealthy, obese
and unattractive). All of these labels are notably uncom-
mon among clusters biased towards white names – in fact,
Crime, law and order and Wanting; planning; choosing are
not found there at all.
6 Discussion
Considering the radicalisation of interest-based communi-
ties 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 embed-
dings and similarity metrics, which leverage the vocabulary
used within specific communities, we are able to discover
biased concepts towards different social groups when com-
pared against each other. This allows us to forego using fixed
and predefined evaluative terms to define biases, which cur-
rent approaches rely on. Our approach enables us to evaluate
the terms and concepts that are most indicative of biases and,
hence, discriminatory processes.
As Victor Hugo pointed out in Les Miserables, slang is
the most mutable part of any language: ‘as it always seeks
disguise so soon as it perceives it is understood, it trans-
forms itself.’ Biased words take distinct and highly mutable
forms per community, and do not always carry inherent neg-
ative bias, such as casual and flirtatious in /r/TheRedPill.
Our method is able to trace these words, as they acquire
bias when contextualised within particular discourse com-
munities. Further, by discovering and aggregating the most-
biased words into more general concepts, we can attain
a higher-level understanding of the dispositions of Reddit
communities towards protected features such as gender. Our
approach can aid the formalisation of biases in these com-
munities, previously proposed by (Caliskan, Bryson, and
Narayanan 2017; Garg et al. 2018). It also offers robust
validity checks when comparing subreddits for biased lan-
guage, such as done by (LaViolette and Hogan 2019). Due to
its general nature – word embeddings models can be trained
on any natural language corpus – our method can comple-
ment previous research on ideological orientations and bias
in online communities in general.
Quantifying language biases has many advantages (Abebe
et al. 2020). As a diagnostic, it can help us to understand
and measure social problems with precision and clarity. Ex-
plicit, formal definitions can help promote discussions on
the vocabularies of bias in online settings. Our approach is
intended to trace language in cases where researchers do not
know all the specifics of linguistic forms used by a commu-
nity. For instance, it could be applied by legislators and con-
tent moderators of web platforms such as the one we have
scrutinised here, in order to discover and trace the sever-
ity of bias in different communities. As pernicious bias may
indicate instances of hate speech, our method could assist
in deciding which kinds of communities do not conform to
content policies. Due to its data-driven nature, discovering
biases could also be of some assistance to trace so-called
‘dog-whistling’ tactics, which radicalised communities of-
ten employ. Such tactics involve coded language which ap-
pears to mean one thing to the general population, but has
an additional, different, or more specific resonance for a tar-
geted subgroup (Haney-L´
opez 2015).
Of course, without a human in the loop, our approach
does not tell us much about why certain biases arise, what
they mean in context, or how much bias is too much. Ap-
proaches such as Critical Discourse Analysis are intended to
do just that (LaViolette and Hogan 2019). In order to provide
a more causal explanation of how biases and stereotypes ap-
pear in language, and to understand how they function, fu-
ture work can leverage more recent embedding models in
which certain dimensions are designed to capture various
aspects of language, such as the polarity of a word or its
parts of speech (Rothe and Sch¨
utze 2016), or other types of
embeddings such as bidirectional transformers (BERT) (De-
vlin et al. 2018). Other valuable expansions could include to
combine both bias strength and frequency in order to iden-
tify not only strongly biased words but also frequently used
in the subreddit, extending the set of USAS labels to obtain
more specific and accurate labels to define cluster biases,
and study community drift to understand how biases change
and evolve over time. Moreover, specific ontologies to trace
each type of bias with respect to protected attributes could
be devised, in order to improve the labelling and characteri-
sation of negative biases and stereotypes.
We view the main contribution of our work as introduc-
ing a modular, extensible approach for exploring language
biases through the lens of word embeddings. Being able to
do so without having to construct a-priori definitions of these
biases renders this process more applicable to the dynamic
and unpredictable discourses that are proliferating online.
Acknowledgments
This work was supported by EPSRC under grant
EP/R033188/1.
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Appendices
A Further experiments on /r/TheRedPill
In this section we perform various analysis on different as-
pects on /r/TheRedPill subreddit. We analyse the effect of
changing parameter rto modify partition granularity, anal-
yse the model stability, and study the performance of two
POS taggers on Reddit.
Partition Granularity The selection of different rvalues
for the k-means clustering detailed in Section 3.3 directly
influences the number of clusters in the resulting partition
of biased words. Low values of rresult in smaller partitions
and hence biases defined by bigger (more general) clusters,
while higher values of rresult in a higher variety of specific
USAS labels allowing a more fine-grained analysis of the
community biases at the expense of conciseness.
Figure 2: Relative importance (left axis) of the top 10 most
frequent labels for women (top) and men (bottom), and num-
ber of unique labels (right axis) using different partition
granularities (r) on /r/TheRedPill
Figure 2 shows the relative importance of the top 10 most
frequent biases in /r/TheRedPill for women and men, pre-
sented in section 5.1, together with the quantity of unique
USAS labels in each partition obtained for different values
of r(see Section 3.3). Both figures show that most of the
top 10 frequent labels for both women and men (see Sec-
tion 5.1), have similar relative frequencies when compared
with the total of labels in each partition for all values of r,
with few exceptions such as Reciprocity and Kin labels for
women and Education in general for men. This indicates
that the set of the most frequent conceptual biases in the
community is consistent among different partitions, usually
aggregating on average between the 22 and 30% of the to-
tal of the clusters for women and men, despite the increase
in the quantity of clusters and unique labels obtained when
using higher values of r. Even considering that the relative
frequencies of the presented labels are similar between par-
titions, the different partitions share, on average, 7 out of
the 10 most frequent labels for women and men. Among the
top 10 most frequent labels for women in all partitions we
find Anatomy and Physiology,Relationship: Intimate/sexual
and Judgement of appearance. For men, some of the most
frequent labels in all partitions contain Power,Evaluation
Good/bad and Geographical Names, among others.
Stability analysis To test the stability of our approach we
created 10 bootstrapped models of /r/TheRedPill in a simi-
lar way as done by (Antoniak and Mimno 2018), including
randomly sampling 50% of the original dataset and averag-
ing the results over the multiple bootstrapped samples. Re-
sults show that the average relative difference between the
ranks of the most frequent labels with respect to male and
female-related target sets remains similar across all ten sub-
datasets. The results show the robustness of our approach to
detect conceptual biases, and demonstrated that the biases
were extended and shared by the community.
Frequency analysis To test the effect that the word fre-
quency threshold (when traininig the word embeddings
model) has on the discovered conceptual biases of a commu-
nity, we trained two new models for /r/TheRedPill changing
the minimum frequency threshold to 100 (f100) and 1000
(f1000). First, as a consequence of the increase of the fre-
quency threshold, the new models had relevant vocabulary
differences when compared with the original f10 presented
in Section 5.1: while the original model has a total of 3,329
unique adjectives, f100 has 1,540 adjectives (roughly 54%
less), and f1000 has 548 adjectives (roughly 84% less). How-
ever, a quantitative analysis of the conceptual biases of the
models show that the conceptual biases are almost the same
for f10 and f100, and very similar for f1000 : almost all top 10
labels most biased towards women and men in f10, are also
biased towards the same target set in f100 and f1000. The
only exception (1 out of the 20 labels for women and men)
when comparing f10 and f100 models is label ’Evaluation:-
Good/bad’, a label slightly biased towards men in f10 but
ranked in the same position for women and men in f100. In
f1000, the figures are very similar too, but as there are many
less words in the vocabulary (84% less), the resulting clus-
ters do not have all labels present in f10. However, and very
importantly, all labels that do appear in f1000 (13 of 20) have
the same relative difference as in f10, with only two excep-
tions (’Quantities’ and ’Knowledge’).
Part-of-Speech (POS) analysis in Reddit We performed
an experiment comparing the tags provided by the nltk
POS tagging method with manual annotations performed
by us over 100 randomly selected posts from the subreddit
/r/TheRedPill, following the same preprocessing presented
in the paper, and focusing on adjectives and nouns. The
results show that the manual POS tagger agrees with the
nltk tagger 81.3% of the times considering nouns over 744
unique nouns gathered from 100 randomly selected com-
ments. For adjectives, the manual tagger agrees with the nltk
tagger 71.1% of the times, over 315 unique words tagged as
adjectives by any of the two methods (manual, nltk) over the
same set of comments. In addition, we also compared nltk
with the spacy POS tagger using the same approach. The re-
sults show an agreement of 68.8% for nouns and 63.7% for
adjectives, obtaining worse results than with the nltk library.
Although the experiments are not conclusive, (a larger–scale
experiment would be needed), the nltk library seems to in-
deed be helpful and be better suited than spacy to POS tag
on Reddit.
B Most Frequent Biased Concepts
In this section we present the set of top 10 most frequent
labels of the communities explored in this work, including
all subreddits and Google News.
Top 10 most frequent labels in Google News (Section
4): Female:Personal names,Other proper names,Clothes
and personal belongings,People:- Female,Anatomy and
physiology,Kin,Cleaning and personal care,Power, or-
ganizing,Judgement of appearance (pretty etc),medicines
and medical treatment.Male:Personal names,Other proper
names,Warfare,Power, organizing,Religion and the super-
natural,Kin,Sports,Crime,Groups and affiliation,Games.
Top 10 most frequent labels in /r/TheRedPill (Sec-
tion 5.1): Female:Anatomy and physiology,Relation-
ship: Intimate/sexual,Judgement of appearance (pretty etc.),
Evaluation:- Good/bad,Kin,Religion and the supernatural,
Comparing:- Similar/different,Definite (+ modals),Reci-
procity,General appearance and physical properties.Male:
Power, organizing,Evaluation:- Good/bad,Geographical
names,Education in general,Egoism,General appearance
and physical properties,Toughness; strong/weak,Quanti-
ties,Importance: Important,Knowledge.
Top 10 most frequent labels in /r/Dating Advice
(Section 5.1): Female:Quantities,Geographical names,
Religion and the supernatural,Language, speech and
grammar,Importance: Important,Judgement of appear-
ance (pretty etc.),Money: Price,Time: Period,Sci-
ence and technology in general,Other proper names.
Male:Evaluation:- Good/bad,Judgement of appear-
ance (pretty etc.),Power, organizing,General ethics,
Interest/boredom/excited/energetic,Quantities,Happy/sad:
Happy,General appearance and physical properties,
Calm/Violent/Angry,Helping/hindering.
Top 10 most frequent labels in /r/atheism (Section
5.2): Islam:Geographical names,Crime, law and or-
der: Law and order,Groups and affiliation,Politeness,
Calm/Violent/Angry,Judgement of appearance (pretty etc.),
General ethics,Relationship: Intimate/sexual,Constraint,
Warfare, defence and the army; weapons.Christian:Re-
ligion and the supernatural,Time: Beginning and ending,
Time: Old, new and young; age,Anatomy and physiol-
ogy,Comparing:- Usual/unusual,Kin,Education in gen-
eral,Getting and giving; possession,Time: General: Past,
Thought, belief.
Top 5 most frequent labels in /r/The Donald (Sec-
tion 5.3): Hispanic:Geographical names,General ethics,
Wanting; planning; ,Crime, law and order,Comparing:-
Usual/unusual.Asian:Geographical names,Government
etc.,Places,Warfare, defence and the army; ,Groups
and affiliation.Russian:Power, organising,Quantities,
Evaluation:- Good/bad,Importance: Important,Sensory:-
Sound.
C Target and Evaluative Sets
The sets of words used in this work were taken from (Garg et
al. 2018), and (Nosek, Banaji, and Greenwald 2002). For the
WEAT test sets performed in Section 4, we used the same
target and attribute word sets used in (Caliskan, Bryson, and
Narayanan 2017). Below, we list all target words sets used.
Google News target and attribute sets From (Garg et al.
2018). Female: sister, female, woman, girl, daughter, she,
hers, her. Male: brother, male, man, boy, son, he, his, him.
Career words: executive, management, professional, corpo-
ration, salary, office, business, career. Family: home, par-
ents, children, family, cousins, marriage, wedding, relatives.
Math: math, algebra, geometry, calculus, equations, compu-
tation, numbers, addition. Arts: poetry, art, sculpture, dance,
literature, novel, symphony, drama. Science: science, tech-
nology, physics , chemistry, Einstein, NASA, experiment,
astronomy.
Google News set of USAS labels related with WEAT
experiments: Career: Money & commerce in industry,
Power, organizing. Family: Kin, People. Arts: Arts and
crafts. Science: Science and technology in general. Mathe-
matics: Mathematics.
/r/TheRedPill target sets From (Nosek, Banaji, and
Greenwald 2002). Female: sister, female, woman, girl,
daughter, she, hers, her. Male: brother, male, man, boy, son,
he, his, him.
/r/atheism target sets From (Garg et al. 2018). Islam
words: allah, ramadan, turban, emir, salaam, sunni, koran,
imam, sultan, prophet, veil, ayatollah, shiite, mosque, is-
lam, sheik, muslim, muhammad. Christianity words: bap-
tism, messiah, catholicism, resurrection, christianity, salva-
tion, protestant, gospel, trinity, jesus, christ, christian, cross,
catholic, church
r/The Donald target sets From (Garg et al. 2018). White
last names: harris, nelson, robinson, thompson, moore,
wright, anderson, clark, jackson, taylor, scott, davis, allen,
adams, lewis, williams, jones, wilson, martin, johnson. His-
panic last names: ruiz, alvarez, vargas, castillo, gomez, soto,
gonzalez, sanchez, rivera, mendoza, martinez, torres, ro-
driguez, perez, lopez, medina, diaz, garcia, castro, cruz.
Asian last names: cho, wong, tang, huang, chu, chung, ng,
wu, liu, chen, lin, yang, kim, chang, shah, wang, li, khan,
singh, hong. Russian last names: gurin, minsky, sokolov,
markov, maslow, novikoff, mishkin, smirnov, orloff, ivanov,
sokoloff, davidoff, savin, romanoff, babinski, sorokin, levin,
pavlov, rodin, agin
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