# Social Dynamics of Online Pro-Eating Disorder Communities

Kristina Lerman∗,1,2, Aryan Karnati2, Shuchan Zhou2, Siyi Chen2, Sudesh
Kumar2, Zihao He2, Joanna Yau2, Abigail Horn1,2<EMAIL_ADDRESS>1\. USC
Information Sciences Institute
2\. University of Southern CaliforniaUSA

(2018)

###### Abstract.

The rise in eating disorders, a mental health condition with serious medical
complications, has been linked to the proliferation of idealized body images
on social media. However, the link between social media and eating disorders
is more complex, with online platforms potentially enabling harmful behaviors
by linking people to “pro-ana” communities that promote eating disorders. We
conceptualize the growth of harmful pro-ana communities as a process of online
radicalization. We show that a model of radicalization explains how
individuals are driven to conversations about extreme behaviors, like fasting,
to achieve the “thin body” goal, and how these conversations are validated by
pro-ana communities. By facilitating social connections to like-minded others,
a shared group identity and emotional support, social media platforms can trap
individuals within toxic echo chambers that normalize disordered eating
behaviors and other forms of self-harm. We also show that while harmful
messages are easily accessible online, they are also easy to distinguish from
the non-harmful messages, thereby enabling content moderation. Characterizing
and quantifying the role of online communities in amplifying harmful
conversations will support the development of strategies to mitigate their
impact and promote better mental health.

twitter, eating disorders, mental health, radicalization

††copyright: acmcopyright††journalyear: 2018††doi:
XXXXXXX.XXXXXXX††conference: Make sure to enter the correct conference title
from your rights confirmation emai; June 03–05, 2018; Woodstock, NY††price:
15.00††isbn: 978-1-4503-XXXX-X/18/06††ccs: Human-centered computing††ccs:
Information systems††ccs: Applied computing Psychology††ccs: Human-centered
computing Empirical studies in collaborative and social computing

##  1\. Introduction

[Warning: This paper discusses eating disorders and self-harm, which some may
find distressing.]

Eating disorders represent a serious mental health condition characterized by
obsessive thoughts and unhealthy behaviors around food, eating, body weight
and shape. The condition, which includes anorexia, bulimia, and binge eating
disorder, affects 24 million people in the US. Many sufferers have their
health, daily routines and quality of life severely compromised due to medical
complications, which also impose a high toll on family members and other
caregivers (van Hoeken and Hoek, 2020). Eating disorders are also among the
deadliest of all mental health conditions, with over 10,000 deaths each year.
To the alarm of public health experts and mental health professionals, the
prevalence of eating disorders has more than doubled in recent years, growing
from 3.5% in the 2000-2006 period to 7.8% in the 2013-2018 period (Galmiche et
al., 2019). The Covid-19 pandemic has further exacerbated the condition
(Nutley et al., 2021), particularly among girls and young women (Hartman-
Munick et al., 2022). According to the CDC, the number of weekly visits to
emergency rooms for adolescent girls struggling with eating disorders has
doubled in 2021 compared to 2019 (Radhakrishnan, 2022). These trends, along
with the growing gender disparity, call for urgent solutions.

Researchers have linked the rise in eating disorders to the proliferation of
idealized body images on visual social media platforms like Instagram and
TikTok that promote the “thin ideal” and the “muscular ideal” (Fardouly and
Vartanian, 2016; Marks et al., 2020). Exposure to idealized images can be
distressing, inviting negative comparisons and pressure to conform to
unrealistic beauty standards. This, in turn, makes individuals feel worse
about themselves and contributes to negative body image, a known risk factor
for developing eating disorders (Choukas-Bradley et al., 2022). However, the
link between social media and eating disorders goes beyond negative body
image. The risks are two-fold. First, social media search and recommendation
algorithms may provide a gateway for youth searching for health, nutrition and
fitness information to discover harmful content that promotes extreme dieting
and over-excercizing. Second, individuals may stumble upon “pro-ana”
communities that glorify thinness and view anorexia as an aesthetic rather
than a serious medical condition (Ging and Garvey, 2018). While such
communities may provide validation, a safe space to vent, and emotional
support to individuals who often feel stigmatized (Oksanen et al., 2016;
Yeshua-Katz and Martins, 2013), they may ultimately harm them by trapping them
within worldviews that normalize disordered behaviors and delay recovery
(Pater et al., 2016; Pater and Mynatt, 2017). Although such pro-ana
communities have existed since the early days of the web in the form of blogs
and message forums (Ging and Garvey, 2018), social media has vastly expanded
their reach and accessibility (Choukas-Bradley et al., 2022). According to one
recent study, discussions about eating disorders and other forms of self-harm
on Twitter have quintupled in less than a year (Goldenberg et al., 2022). By
helping users find one another within pro-ana spaces, social media has
supercharged their growth.

Our paper attempts to explain these trends by casting the growth of online
communities that promote eating disorders— _pro-ED communities_ —as a
radicalization process. This framing allows us to draw on rich literature and
theory that explain how online group dynamic processes can radicalize
individuals by linking them to like-minded others who push them towards more
extreme beliefs and behaviors. Prior works have shown similar group dynamics
amplify political polarization (Barberá, 2020), hate speech (Schmitz et al.,
2022), misinformation (Chou et al., 2018) and conspiracy movements (Wang et
al., 2023). However, the link between online radicalization and mental health
disorders has not be widely recognized.

Our approach is inspired by the 3N model of radicalization (Kruglanski et al.,
2022). Originally developed to explain how terrorists and violent extremists
are formed (Bélanger et al., 2019; Kruglanski et al., 2014), this theoretical
framework has been applied to non-violent behaviors such as joining the QAnon
conspiracy (Wang et al., 2023). The model explains how loss of personal
significance (Need for significance) moves individuals to justify extreme
behaviors (create Narratives) that enable them to achieve their goals and
thereby regain significance. These narratives are validated by communities
(Networks) that connect individuals to like-minded others, while isolating
them from opposing worldviews. We operationalize key concepts of the 3N model
and link them to data using a large corpus of online eating disorders-related
conversations. Although alternative theoretical lenses have been applied to
pro-ED online spaces (Pater and Mynatt, 2017; Choukas-Bradley et al., 2022),
they fail to account for group dynamics.

Our study focuses on Twitter, which has an active—and quickly growing
(Goldenberg et al., 2022)—eating disorders community. Twitter does not
forcefully moderate harmful pro-ED content (Sukunesan et al., 2021). Unlike
TikTok and Instagram, which returned links to National Eating Disorder
Association resources when searching for ED-related topics, Twitter returned
harmful posts at the time that data was collected. We show that content
related to eating disorders is deeply integrated within mainstream topics such
as “diet” and “weightloss.” Moreover, due to the intersections between various
mental health disorders, individuals viewing pro-ED messages are often exposed
to self-harm behaviors like cutting. Thus, eating disorders may be a gateway
to other behaviors and beliefs that erode mental health.

We empirically ground the 3N model by operationalizing its constructs and
quantifying them using Twitter data. For example, the model predicts that
individuals may regain personal significance by adopting a group identity. We
show that a large share of accounts posting about eating disorders adopt a
collective identity through the use of self-defining hashtags (Barron and
Bollen, 2022), such as “edtwt” (eating disorders Twitter) or “shtwt” (self-
harm Twitter). While self-defining hashtags were studied in the context of
social justice movements (Chang et al., 2022), their importance in mental
health conditions has not been as widely acknowledged. Next, we analyze pro-ED
conversations and find that, consistent with the 3N model, many promote and
justify disordered eating behaviors. Specifically, they motivate extreme
behaviors through “inspirational” images and accountability posts, provide
tips to manage ED symptoms or hide them from caregivers, as also found by
other researchers (Chancellor et al., 2016a; Pater et al., 2016; Chancellor et
al., 2016b). Finally, we characterize the quality of interactions between
members of pro-ED spaces to demonstrate that pro-ED communities provide social
and emotional support. Using state-of-the-art language models, we measure
emotions expressed in pro-ED tweets to show that positive emotions like joy
dominate. To best of our knowledge this is the first large scale analysis of
emotions of ED-related conversations.

The online radicalization framework not only allows us to better understand
the social dynamics through which pro-ED spaces grow, but also suggests
strategies to limit their growth. These range from content moderation to limit
exposure to harmful content to banning problematic communities and users.
Focusing on content moderation, we show that it is feasible to automatically
distinguish harmful tweets from the more mainstream content.

Contributions of this paper:  Our contributions are three-fold. First, we
audit a sample of Twitter conversations about nutrition and fitness and show
that content promoting eating disorders is widely accessible via popular
topics like diet and weightloss. Second, we use the framework of online
radicalization to describe the social dynamics of pro-ED communities. This
conceptualization allows us to cast phenomena that were studied separately in
the past — eating disorders as an identity, the sharing of pro-ED tips and
motivating “thinspo” imagery, providing emotional support — as part of _the
same underlying process_. Also, by uniting such seemingly disparate processes
as political polarization, online extremism, conspiracy theories and mental
health under the umbrella of radicalization hints at universal human needs
that drive them. As our third contribution we demonstrate that it is possible
to automatically distinguish harmful tweets from other conversations about
diet and fitness.

Our findings highlight the dangerous “harm spiral” created by the
technological affordances of social media: _harmful content is easy to
discover and group dynamics encourage people to stay engaged, which exposes
them to more harmful content_. Note that processes similar to radicalization
can create “virtuous spirals” that result in social justice movements (Chang
et al., 2022) or online support groups for marginalized identities. However,
the fundamental asymmetry of human cognition that makes us pay more attention
to negative information (Baumeister et al., 2001) make the harm spirals more
pervasive and dangerous. However, since radicalization is a feedback loop,
even small changes can have large downstream effects. Specifically, content
moderation, which would automatically limit exposure to harmful tweets by
making them harder to find, could have a big impact on slowing the growth of
pro-ED communities.

Ethics: This research touches on highly sensitive topics related to mental
health, which calls for extra precautions to minimize risks to study subjects
as well as researchers. All data used for this study is public and collected
following Twitter’s terms of service. To minimize privacy risks, identifiable
information was removed and analysis was carried out on aggregated data. As a
result, we believe that the risks of negative outcomes due to the use of these
data are trivial. To minimize risks to researchers, we did not collect images
and regularly met with the research team to identify potential sources of
distress.

One potential objection is that using the term “radicalization” to describe
people with eating disorders is wrong, since they are not violent, and
moreover, risks stigmatizing an already vulnerable population. As a
counterargument, we offer that the radicalization paradigm has been used to
describe both violent (terrorism) and non-violent (conspiracies) phenomena.
Moreover, uniting these disparate phenomena under the umbrella of
radicalization is evidence for the universality of online social dynamics.
Another potential objection is whether pro-ED speech should be moderated at
all. We argue that pro-ED speech normalizes harmful behaviors that at minimum
delay recovery but potentially cause more distress to people struggling with
ED, their families and caregivers. Disrupting the group dynamics of
polarization by moderating pro-ED speech would create ripple effects that
reduce pro-ana groups.

It is important to note that our study cannot discriminate between individuals
with eating disorders and those who are merely discussing disordered behaviors
and beliefs. Nor do we have evidence that joining pro-ana conversations
results in people developing eating disorders or exacerbating an existing
illness. We argue, however, that such communities are harmful in that they
normalize and promote ED behaviors, potentially delaying recovery for
individuals struggling with ED. With these risks in mind, our study identifies
a new concern in the mental health crisis, specifically, online communities
which trap individuals within toxic echo chambers that entrench and amplify
extreme views. Better understanding the role of social media platforms in
promoting eating disorders content and communities will inform the development
of better strategies to enhance mental health.

##  2\. Related Works

Eating disorders have complex biopsychosocial etiology, with contributing
biological factors, such as genetics and infections (Aman et al., 2022), as
well as psychological comorbidities, such as anxiety and perfectionism. In
addition, social mechanisms such as peer effects (Allison et al., 2014) and
exposure to idealized body images in the media, contribute to eating
disorders. For example, before the introduction of Western TV programming on
Fiji in 1995, purging as a means of controlling weight was virtually unknown,
but quickly grew afterwards (Becker, 2004).

###  2.1. Social Media and Body Image Concerns

One of the best studied area is the relationship between social media and body
image concerns, a key risk factor for developing depressive symptoms and
eating disorders (Choukas-Bradley et al., 2022). Body image concerns arise
when a person feels anxious, distressed, or self-conscious about their body
weight, shape or appearance. Studies have shown that social media use
contributes to body image concerns in several ways. First, content on social
media sites, especially image-based platforms like Instagram, Snapchat and
Tiktok, features images of bodies, often heavily edited, that promote the
‘thin ideal’. Studies have shown that when users are exposed to this type of
content, they compare themselves to the idealized body images and as a result,
feel worse about their own body and appearance (Saiphoo and Vahedi, 2019;
Fardouly and Vartanian, 2016; Choukas-Bradley et al., 2022). Similar to
traditional media, exposure to the “thin ideal” by influencers on social media
can provoke upward social comparisons, where individuals compare themselves to
people whom they perceive to be better than themselves (Harriger et al.,
2022). In contrast to television and magazines, social media also allows
adolescents to compare themselves to peers, which provides more salient and
potent point of negative social comparison (Festinger, 1957). What’s more,
adolescents are often not aware that images are frequently heavily edited
(Marks et al., 2020), and as a result they normalize distorted, unrealistic
beauty ideals. Social media can further fuel body image concerns through
negative feedback to user’s own content and the images posted by others. In
extreme cases, the negative feedback can manifest as body shaming and
cyberbullying. Our study differs from existing literature in that it looks at
social dynamics of online interactions that trap vulnerable individuals within
toxic echo chambers.

###  2.2. Online Pro-ana Communities

Pro-ana communities are online spaces, originally on blogs, online forums, but
also increasingly on social media platforms, that promote anorexia as a
lifestyle not an illness. Pro-ana communities provide a venue for individuals
with eating disorders to share tips on losing weight and concealing weight
loss from others, as well as “thinspriration” images of very thin bodies to
motivate weight loss (Ging and Garvey, 2018). Researchers have argued that
pro-ana communities have both positive and negative effects on individuals
with eating disorders. On the positive side, content analysis and qualitative
studies revealed that these communities provide social support (Juarascio et
al., 2010) and a sense of belonging to individuals who often feel stigmatized
and misunderstood (Oksanen et al., 2016; Yeshua-Katz and Martins, 2013).
Members can find empathy, encouragement, a safe space to vent, and information
to help them better understand and manage their illness (McCormack, 2010). On
the negative side, pro-ana communities often promote unhealthy and harmful
behaviors that can exacerbate eating disorders, such as extreme calorie
restriction, purging, and over-exercising. Members may compete with each other
in weight loss, ask the group to hold them accountable to their weight loss
goals or find ana buddies to go through the difficult periods of food
restriction. The normalization of ED behaviors along with content that
celebrates the “thin ideal” can increase psychological distress around body
image and encourage disordered behaviors to persist (Mento et al., 2021). Our
paper extends these findings to social media platforms and links ED to other
forms of deviant behaviors using online radicalization framework.

Computer scientists have studied online pro-anorexia communities. (Pater et
al., 2016) characterized hashtags and media content associated with eating
disorders-related posts on Tumblr, Instagram, and Twitter. Distinct from this
work, we also focus on how well integrated the eating disorders-related
content is within the more mainstream diet and fitness content. (Chancellor et
al., 2016a) build a classifier to predict which posts will be taken down for
violating Instagram’s prohibitions against promoting self-harm. The authors
then combined statistical text analysis of Instagram posts with clinician
annotations to predict the severity of an individual user’s eating disorder
(Chancellor et al., 2016b). In contrast to these works, we explore the social
dynamics of pro-ana communities, including the mechanisms by which individuals
discover and become trapped within these communities. Rather than focus on
individuals, our aim is to identify pathways for altering social dynamics so
as to disrupt the growth of pro-ana communities.

###  2.3. Online Radicalization

Radicalization is a process by which individuals adopt extreme beliefs that
enable them to engage in deviant behaviors, such as violence and terrorism.
The internet has facilitated this process in a number of ways, creating an
explosion of research into online radicalization. Social media platforms and
discussion forums provide individuals access to online communities where they
can receive social support and validation, engage with others who hold similar
beliefs, and access extreme content that reinforces their beliefs and
justifies extreme behaviors. In addition, algorithms on social media platforms
may promote radicalization through the reinforcement of extremist beliefs
through echo chambers and algorithmic amplification.

A number of theories have been proposed to explain the process of
radicalization, focusing on political radicalization. These theories typically
consider motivational, ideological, and social factors. Kruglanski et al.
(2014) found that individuals who are more susceptible to online
radicalization tend to be socially isolated and lack a sense of purpose or
belonging (Bélanger et al., 2019).

The 3N model of radicalization explains how violent extremists and terrorists
are created (Kruglanski et al., 2022). The model has three key elements: (i)
Need for significance, i.e., to feel that one matters or merits respect.
Extremist ideology fills the void created by the loss of significance due to
humiliation, stigma or social identify that is disrespected by others; (ii)
Narratives are justifications for using extreme behaviors (e.g., violence,
terrorism) to pursue the goal for personal significance; (iii) Networks arise
from social processes that connect individuals to like-minded others who
validate their extremist ideology and narratives while isolating them from
opposing ideas. The 3N model has been empirically validated in different
cultural settings in individuals expressing high levels of social isolation
(need) and support for political violence (narrative) (Bélanger et al., 2019).
In addition, the 3N model was recently applied to explain emergence of online
conspiracies like QAnon (Wang et al., 2023).

The 3N model of radicalization provides a framework for understanding the
growth of online eating disorders and self-harm communities. Individuals
experiencing body image concerns may feel devalued and rejected, and are
therefore vulnerable to being radicalized. Loss of personal significance
motivates them to restore it (Dugas et al., 2016) by seeking out meaning and
identity from extremist communities. These communities also provide narratives
that promote and justify extreme behaviors like food restriction, purging, or
self-harm that restore personal significance, i.e., the thin ideal. Our work
identifies elements of online activity linked to the 3N model and develops
quantitative indicators to measure their effects.

##  3\. Methods

###  3.1. Data

Despite Twitter’s policy prohibiting content that glorifies suicide and self-
harm, tweets related to these topics have proliferated on the platform. One
study found that the number of tweets mentioning self-harm increased 500%
between 2021-10 and 2022-08 (Goldenberg et al., 2022), with 20,000 monthly
tweets on average. Twitter does not appear to enforce their content policy and
rarely moderates harmful content. Although Twitter is adding links to mental
health resources to search results for self-harm keywords, as of March 2023,
searches for keywords related to eating disorders were not flagged.

We collected 2.532M tweets using keywords to query Twitter for messages
covering the period from 2022-10 to 2023-03. Our starting point was a set of
terms identified by prior works (Chancellor et al., 2016a; Pater et al., 2016)
as promoting eating disorders. This include thinspo (“thininspiration”),
proana (pro-anorexia), and pro-mia (pro-bulimia), among others. After
examining results, we removed spammy terms like skinny and added terms related
to diet and weightloss topics such as (ketodiet, weightloss, $\ldots$), and
anti-diet culture (bodypositivity, dietculture, $\ldots$). See Appendix for
the full list of keywords. Information collected on tweets includes user
profile description, timestamp, message content, and hashtags.

###  3.2. Hashtag Co-occurrence Graph

A hashtag co-occurrence graph is a network that represents relationships
between hashtags in a set of social media posts. An edge between two hashtags
exists if they appear together in a post, with the weight of the edge
representing the number of times–i.e., the number of tweets in which—the two
hashtags co-occur.

###  3.3. Measuring Emotions

Language carries cues to emotions, or feelings. These can include positive
emotions, like joy and love, as well as negative emotions like anger and
disgust. We use a state-of-the-art emotion detection model SpanEmo (Alhuzali
and Ananiadou, 2021), which has been trained on SemEval 2018 Task 1 E-c data
(Mohammad et al., 2018). The SemEval dataset contains tweets labeled according
to whether or not they contain emotions such as anger, disgust, fear, sadness,
joy, love, optimism, pessimism and trust. SpanEmo uses a transformer-based
language model to encode input text and returns a continuous value per
emotion, denoting its confidence that the emotion is present in text.
Consequently, the model can predict none, one, or multiple emotions per tweet.

Figure 1. Hashtag co-occurrence network. Nodes represent the most popular
hashtags (seen at least 230 times) in our data, with edges linking hashtags
that frequently appear together in the same tweet (more than 100 times). Node
size represents its centrality in the network. Harmful hashtags are
highlighted light orange.

##  4\. Results

We use the methods described above to analyze social dynamics of online
conversations about eating disorders.

###  4.1. Accessibility of Pro-ana Communities

One hazard of social media is that vulnerable individuals searching for health
information may stumble upon harmful content promoting eating disorders. To
investigate how well pro-ED discussions are integrated within mainstream
health topics, we create a hashtag co-occurrence network of popular hashtags
in our dataset. The hashtags represent important topics that users choose to
highlight with a “#” symbol (for clarity, we omit this symbol). We focus on
the 612 most popular hashtags in our dataset, which appeared more than 230
times. Figure 1 shows the largest connected component of the hashtag co-
occurrence network with 521 hashtags. Edges connect strongly linked hashtags
that co-occur in at least 100 tweets. Here node size represents its PageRank
centrality and we used Louvain modularity to help visualize dense hashtag
clusters. These clusters represent (1) Weightloss and fitness community with
topics such as diabetes, ozempic, workout, gym, fitspo, $\ldots$; (2) Keto
diet community with topics keto, paleo, lowcarb, $\ldots$; (3) Healthy living
community with topics nutrition, wellness, vegan, cleaneating, mentalhealth,
$\ldots$; (4) Body positivity community with topics curvy, thicc, plussize,
$\ldots$; and (5) the pro-ED community highlighted in light orange. This
cluster contains hashtags like thinspo, a term used to describe content that
glorifies extreme thinness, proana, a term used to refer to a subculture that
views anorexia as a lifestyle choice rather than a serious illness, and edtwt
and shtwt, hashtags used to identify the ED and self-harm subcultures on
Twitter, respectively.

ED-related hashtags are densely interconnected but they are also strongly
linked to four “mainstream” topics: diet, weightloss, fasting, and
mentalhealth. When we relax the definition of “strongly linked” to mean
hashtags that occur together in 30 (instead of 100) tweets, the ED hashtags
remain well-segregated from other topics, forming a “bubble.” A concern here
is that people could stumble upon ED content through popular gateway topics
such as weightloss and diet and then become trapped in the bubble, isolated
from the more mainstream views on nutrition, health and fitness.

We also find that eating disorder hashtags are strongly linked to other mental
health conditions, especially self-harm (shtwt), but also suicidal ideation
(988twt) and borderline personality disorder (bpdtwt). Interestingly, the
self-harm topics clustered around the hashtag shtwt (not shown) employ an
array of euphemisms for cutting skin, the scars it leaves, skin layers, and
blood. These euphemisms are likely an effort to evade Twitter safety and
moderation protocols, or perhaps an attempt to create an insider language.
However, most of the connections between the two clusters are mediated by
edtwt and shtwt, hinting at the central role they play in defining Twitter
communities and allowing individuals to easily navigate between content
created by these communities.

###  4.2. Radicalization in Pro-ED Communities

In this section, we operationalize the constructs of the 3N model of
radicalization and link them to the patterns found in Twitter data.

####  4.2.1. Need for Significance

Loss of personal significance is a precipitating factor in radicalization
(Kruglanski et al., 2014). Studies of terrorism and violent extremism show
that this can be triggered by a range of factors, such as marginalization,
humiliation, social dislocation, identity crisis, despair, and political
grievances (Dugas et al., 2016; Kruglanski et al., 2022; Bélanger et al.,
2019). These factors motivate individuals to turn to extremism as a means of
regaining significance. In the context of eating disorders, loss of
significance may be triggered by negative body image and despair that drive
vulnerable youth to seek dieting and weightloss advice online (Ging and
Garvey, 2018; Choukas-Bradley et al., 2022; Pater et al., 2016).

Online eating disorder spaces fulfill the need for significance in several
ways. They allow individuals to set body goals (e.g., “lose 20 lbs before
prom”) and post progress pictures for accountability. They are also rife with
“inspirational” images of grotesquely thin bodies that community members
believe will motivate them and others to lose weight or achieve a certain body
shape. In fact, many hashtags in Fig. 1 are related to “thinspiration”,
including thinspo, thinsp0, sweetspo, bonespo, deathspo.

Another way that pro-ED communities help individuals regain significance is by
provisioning them with a collective identity (Polletta and Jasper, 2001).
Studies have shown that users self-label themselves in their online profiles,
using popular hashtags to express solidarity with a chosen group (Barron and
Bollen, 2022) or cause, such as “#blacklivesmatter” or “#metoo” (Chang et al.,
2022). To check whether individuals adopt a collective identity of a pro-ED
subculture, we check for the presence of strings “edtwt” and “shtwt” in their
profile descriptions. Of the 83,883 unique accounts in our dataset that posted
1,851,419 messages with harmful terms (edtwt, shtwt, thinspo, etc.), 11,126
have “edtwt” in their profile description, and 3,217 have “shtwt” in the
description. Even without accounting for variations of these terms (e.g., “ed
twt”), 17% of accounts tweeting about harmful topics were self-labeling
themselves as belonging to the pro-ED and self-harm communities. This suggests
that group identify is an important factor in joining harmful communities.

####  4.2.2. Pro-ED Communities Promote Harmful Narratives

Pro-ED tweets encourage and support harmful behaviors, such as restricting
food intake, purging, and over-exercising, as a means for individuals achieve
the “thin ideal” goal. Among the set of 230 most popular hashtags, we
identified those related to harmful topics that promote or enable harmful
behaviors (excluding terms used for data collection). Table 1 categorizes
these harmful hashtags.

Table 1. Harmful hashtags used in pro-eating disorders tweets categorized by
topic.

Motivation & Inspiration::

Hashtags aiming to inspire others to engage in pro-ED behaviors with images
that promote the “thin ideal,” or motivational images of progress
(“bodycheck”) towards their ultimate goal weight (“ugw”).

  1. (1):

thinsp0 thinspothread bonesp0 b0nesp0 b0nesp0 thinpo ribspo thinspiration
thinspodaily th1nsp0 th1nspo

  2. (2):

ugw thighgap bodycheck bodychecking thinarms thinwaist ribs hipbones legs
goals motivation

Information & Tips::

Hashtags used to label threads that provide detailed information on how to
tolerate hunger, avoid medical complications from starvation, or hide symptoms
from caregivers. A thread may, for example, offer “things that help you
/motivate you to keep you $\star$ving”, details about “How I lost 7kg in a
month,” or recipes for an “anorexia diet.”

  1. (1):

edthread edthreads edtwtthreads edtips edtwttips restricting

  2. (2):

anadiet eddiet edfood edtwtdiet edtwtfood edtwtfoodpoll edtwtrecipe abcdiet
lowcal lowcalorie lowcalrecipe

Social & Buddies::

Hashtags used to request “mutual” followers or recruit “buddies” and “coaches”
to guide them through the anorexia journey.

  1. (1):

edmoots edmoot mutuals moots

  2. (2):

anabuddies anabuddy anacoach proanacoach

Table 2. The reach of pro-ED content. Engagement with tweets (retweets and replies) on a variety of harmful topics promoting eating disorders and a control set of tweets. Tweet group | Count | Retweets | Replies
---|---|---|---
Motivation 1 | 70,749 | 101.12 | 0.35
Motivation 2 | 157,610 | 132.44 | 0.53
Information 1 | 5,799 | 114.80 | 0.57
Information 2 | 23,573 | 97.88 | 0.32
Social 1 | 128,381 | 43.39 | 0.70
Social 2 | 1,803 | 4.68 | 1.07
Random | 10,000 | 139.91 | 0.52

We grouped tweets according to the specified hashtags. We also created a
random set of tweets from the data for comparison. Table 2 reports the volume
of tweets within each group and engagement levels, as measured by the average
number of retweets and replies the tweets receive. Motivation and information
tweets have wide reach, as demonstrated by large numbers of retweets. Requests
for mutual followers are far more common than requests for pro-ana buddies and
coaches, but the latter tweets receive more replies. The level of engagement
with pro-ED content is similar to that for the control set of tweets chosen at
random from our dataset of tweets related to dieting and fitness. These
results suggest that online communities play a role in providing information
and inspiration to engage in disordered eating behaviors.

####  4.2.3. Networks Offer Social Support

Once individuals join an extremist group, a number of group dynamics processes
keep them there (Kruglanski et al., 2014). Similar processes also play out
within online pro-ED communities. By linking individuals who are struggling
with eating disorders to like-minded others, pro-ED communities provide social
and emotional support, as well as validation to those who may feel stigmatized
and misunderstood. The desire to connect to like-minded others is visible in
community introductions, where new members announce themselves while asking
others to become their moots, or “mutual” followers. The hashtag edtwtmoots
appears among the popular hashtags in Fig. 1.

We explore the means through which pro-ED communities provide support. First,
we provide evidence that individuals identifying with pro-ED and self-harm
subcultures form a tight community. Next, we show that members provide
emotional support in the form of positive emotions.

Table 3. Ratio of followers to followings for accounts that use specific terms
in their profiles. We report the number of accounts using the specified term,
the mean and standard deviation of the ratio of their followers/followings, a
proxy “mutual” follows. Values near one suggest a high share of mutual
followers, i.e., echo chambers.  User Followers/Followings Ratio
---
Term | Count | Mean | Std Dev
edtwt | 51,060 | 1.11 | 13.57
shtwt | 16,242 | 0.98 | 3.36
diet | 3,217 | 51.74 | 1451.17
weight | 4,676 | 20.76 | 642.65

##### Social Embeddedness

The group dynamics of online radicalization result in the formation of echo
chambers that link individuals to like-minded others while isolating them from
alternative points of view (Barberá, 2020). Unfortunately, our dataset does
not include information about the follower graph of users participating in
eating disorders-related conversations, so we cannot formally test for the
existence of echo chambers or the presence of reciprocated follower
relationships, i.e., “mutuals.” However, our data includes user profile
information, with user’s bio, the number of followers as well as the number of
accounts the user follows (followings). We use the ratio of followers to
followings as a proxy of the share of “mutual” followers. Table 3 reports the
mean value of this ratio for accounts that use one of the reported terms
within their user profile. Accounts that use terms “edtwt” and “shtwt” as part
of their profile have the ratio of followers to followings close to one,
implying a large share of mutual connections. In contrast, accounts that use
non-harmful terms “diet” and “weight” as part of their profile have a much
larger ratio on average, albeit with very high variance. These results provide
weak evidence that users identifying with “edtwt” and “shtwt” subcultures form
echo chamber-like communities.

Figure 2. Emotion analysis. The share of tweets using specified terms that
contain specific emotions.

##### Emotion Analysis

We analyzed emotional expressions in tweets in our dataset. Joy was the most
common emotion, occurring in almost 1.4M tweets, followed by optimism ($>$100K
tweets), disgust ( 100K), sadness, anger, fear and love. There was no emotion
detected in a little more than 600K tweets.

Next, we focus on tweets with harmful terms. Figure 2 shows the share of
tweets mentioning select harmful terms that express each emotion. Joy is the
most common emotion in harmful tweets. Tweets mentioning ‘fitspo’ (fit
inspiration) and ‘edtwtthread’, which includes tips that promote disordered
eating behaviors, are predominantly joyful, as are community introductions
where members request “mutual followers.” In contrast, tweets mentioning
‘eating disorder’, ‘edtwt’ and ‘shtwt’ express less joy and more negative
emotions such as anger, disgust and sadness. The high prevalence of positive
emotions within pro-ED messages suggests that communities provide emotional
support. This may incentivize members to keep participating within ED
communities.

###  4.3. Harmful Tweet Classification

Is it possible to automatically detect harmful tweets promoting eating
disorders? To answer this question, we trained text classification models to
recognize harmful tweets. We define three classification tasks: recognize
harmful tweets that contain hashtags from 1) the Motivation & Inspiration
category of Table 1, 2) the Information & Tips category, or 3) any harmful
hashtag in Table 1. These tasks allow us to test how well we can recognize
harmful tweets promoting thininspiration and over-excercising, as well sd
harmful tweets that promote extreme dieting.

To represent the text of the tweets (excluding hashtags) we used bag-of-words
(TF-IDF) representation and word embeddings (Word2Vec). For consistency, we
kept the number of features in the TF-IDF representation at 50K, and set the
vector size in word2vec at 100 across all models. We used three classification
models—logistic regression, SVM, and XGBoost—and used default parameters in
training all models. For each classification task, we created a balanced
dataset of 50K randomly chosen harmful tweets and 50K random non-harm tweets.
We trained all models on the randomly selected 80% of tweets and tested
classification performance on the held out 20% of tweets.

Table 4. Accuracy of harmful tweet classification. | motivation | information | all
---|---|---|---
tfidf logreg | 80.99 | 90.57 | 86.39
tfidf SVM | 80.27 | 88.39 | 86.53
tfidf XGBoost | 79.46 | 89.03 | 86.94
word2vec logreg | 76.61 | 86.65 | 85.35
word2vec SVM | 77.57 | 89.58 | 85.16
word2vec XGBoost | 80.37 | 91.88 | 86.51

Table 4 reports the accuracy of harmful tweet classification on the three
tasks. Across both harmful motivation and information tweets, logistic
regression using TF-IDF representation and XGBoost using word embeddings
achieved best performance, distinguishing harmful tweets from non-harmful ones
with 90% accuracty. We then combined the two datasets to create a balanced
dataset of 100K harmful tweets and 100K non-harmful tweet and report
classification performance. In this case, XGBoost gives best performance of
87% with both representations. These results suggest that it is relatively
easy for social media platforms to distinguish harmful tweets promoting eating
disorders from non-harmful tweets about fitness and diet.

##  5\. Conclusion

Our paper examines the role of social media platforms like Twitter in eating
disorders. We argue that the growth of online communities that promote eating
disorders and other forms of self-harm can be described as online
radicalization process (Kruglanski et al., 2014, 2022; Bélanger et al., 2019).
This helps explains how vulnerable individuals seek meaning and social
connection in communities that adoption of extreme worldviews. Although the
model describes offline behaviors that create terrorists and violent
extremists, it was also applied to online conspiracies, hate groups, and
extremist ideologies. To the best of our knowledge, our work is the first to
apply this model to mental health conditions like eating disorders. By
facilitating social connections to like-minded others, a shared group identity
and a sense of belonging, pro-anorexia communities create toxic echo chambers
that normalize extreme behaviors that lead to eating disorders and self-harm.
These communities can make recovery difficult.

Our work highlights the importance of social dynamics in explaining mental
health trends and contributes to the body of research implicating social media
in the transmission of behaviors that lead to negative mental health outcomes.
The ongoing mental health crisis among the youth creates an urgent need for
solutions. The radicalization model explored in this paper provides a
framework for understanding the phenomenon, links it to other harmful
behaviors, and provides insights into potential mitigation strategies. At the
very least, our work calls for more stringent moderation of online content
that is harmful to mental health.

###### Acknowledgements.

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##  Appendix A Search Terms

The keywords that we use to retrieve ED tweets are: _thinspo_ , _edtwt_ ,
_proana_ , _proanatips_ , _anatips_ , _meanspo_ , _fearfood_ , _sweetspo_ ,
_eatingdisorder_ , _bonespo_ , _promia_ , _redbracetpro_ , _bonespo_ ,
_m34nspo_ , _fatspo_ , _lowcalrestriction_ , _edvent_ , _WhatIEatInADay_ ,
_Iwillbeskinny_ , _thinspoa_ , _skinnycheck_ , _thighgapworkout_ , _bodyimage_
, _bodygoals_ , _weightloss_ , _skinnydiet_ , _chloetingchallange_ ,
_fatacceptance_ , _midriff_ , _foodistheenemy_ , _cleanvegan_ , _keto_ ,
_ketodiet_ , _cleaneating_ , _intermittentfasting_ , _juicecleanse_ ,
_watercleanse_ , _EDrecovery_ , _bodypositivity_ , _dietculture_.