Mental disorders are psychological syndromes or patterns associated with distress or disability. Anorexia nervosa (AN) is an eating disorder (ED) characterized by the maintenance of a very low body weight, with a constant desire for thinness and a misconception about body shape [1]. EDs, such as AN, are related to risk factors including perfectionistic traits [2], and the existence of comorbid mood disorders such as depression, of which 33%-50% of anorexia patients experience. Anxiety disorders are common in 50% of these patients [3].

The course of AN is highly variable; however, early intervention is strongly associated with full recovery. The phases that patients go through as they overcome AN can be mapped to the transtheoretical model of health behavior change [4], which is described as an integrative method for understanding how people progress toward adopting and maintaining healthy behaviors. This model identifies the following six stages of change: (1) precontemplation, where the individual does not know that there is a problem or that a change is required in their life and, thus, does not seek help; (2) contemplation, where the person simultaneously considers and rejects the change, while being conscious of the existence of a problem; (3) preparation, where the individual starts to take small steps toward behavioral change, believing that it can lead to a healthier life; (4) action, in which the person has changed their behavior and intends to sustain it; (5) maintenance, a stage wherein the person has maintained the behavioral change for a considerable period (>6 months); and (6) termination, a stage wherein the individual has no desire to return to their unhealthy behaviors. It is important to state that relapse, which implies returning from the action or maintenance stages to an earlier stage, is likely. This approach has been previously evaluated in patients, confirming that the notion of the stages of change is an independent dimension that is very relevant for the treatment of EDs [5].

Automated methods have been designed to detect signs of AN, some of which address the development of early detection approaches [6,7], as it has been proven that the signs and symptoms of mental disorders, including AN, can be traced using social media [6,8-13]. The findings of such research have revealed patterns that can be relevant for the development of tools to detect harmful content [6] and to assist clinicians and psychologists in screening [10-12] and treatment proceedings [6]. Research findings on these topics can also contribute to the improvement of the structure and services provided by online social platforms [6], which are a means through which people with mental disorders can find support for their recovery, as well as they can be used as tools to promote harmful content, which is the case for suicide promoters and pro–eating disorder (pro-ED) communities [6,13].

Recent approaches for characterizing AN on social media account for individual posts from users, meaning that the signs and symptoms are traced by analyzing the content of a single post per user (post level) [6,8,9]. In contrast, there are only a few approaches that address multiple posts of a single user (user level) [10,11]. There are no approaches that address the simultaneous analysis of relational, behavioral, demographical, and multimodal information at the user level, although few studies have combined some of these aspects on AN-related disorders such as anxiety, depression, and suicidal ideation [12,13].

With regard to the type of information extracted and analyzed about EDs on social media, several state-of-the-art approaches are dedicated to the analysis of textual information contained in posts [6,9,14]. They analyze the topics of interest using topic modeling techniques [11], and they also consider the frequency of the terms used through bag-of-words (BoW) models and n-grams [7]. Other textual elements are also considered, such as the frequency of terms closely related to the illness and other lexical and syntactic elements [11,14]. In addition, sentiment analysis tools [15] and methods that use word embeddings, which are vector representations of terms, have been applied [7,16]. Most of these features are later used as representations suitable for the development of predictive models that are often based on machine learning methods [6,7,16]. Through the analysis of text cues, it has been found that users with depression and those with suicidal ideation make more self-references [13], whereas proanorexia and prorecovery communities exhibit distinctive affective, social, cognitive, and linguistic style markers, as proanorexics express greater negative affect, feelings of social isolation, and self-harm [6].

With regard to the analysis of relational information, Wang et al [17,18] analyzed the interactions between users with EDs to identify the topics shared in web-based conversations. In particular, they explored the interactions among communities with different stances on EDs, considering both pro-ED and prorecovery content, and reported that intercommunity interactions are very limited. In the context of similar disorders, De Choudhury et al [19] constructed egocentric social graphs for postpartum depression detection and found that increased social isolation and reduced availability of social capital on Facebook are the best predictors of postpartum depression detection among mothers. In addition, Colombo et al [20] studied the connectivity and communication of suicidal users on Twitter based on the evaluation of retweets.

Regarding the analysis of images shared by users, only a few studies analyzing the content of images to determine patterns that can be linked to AN signs are available in the literature. This aspect of analysis is important for studies on EDs, as the symptoms of AN, such as extreme weight loss, an obsessive interest in excessive exercise, and restricted food intake through dieting, can be identified from images [1]. Chancellor et al [21] focused on Instagram, which mainly includes image posts, but they exclusively studied textual (captions) and relational elements. Recently, only a few approaches have addressed the actual analysis of image properties to detect mental disorders, such as self-harm, depression, and anxiety [12,22,23]. To the extent of our knowledge, the work of Chancellor et al [16] is the only available study on EDs that analyzed images. Chancellor et al [16] classified moderated pro-ED content and found that the identification of deviant pro-ED content through the analysis of images was possible, reaching a recall of 84%. This approach mainly focused on the detection of harmful images but did not include the analysis of interpretable elements and patterns that might characterize AN-related content on images.

There are also a few studies that consider the stages that patients go through during the course of the illness. De Choudhury [6] and Wang et al [17] addressed the detection and characterization of prorecovery communities, which can be distinguished from pro-ED communities. However, both of these communities did not exclusively include people with the illness. Chancellor et al [24] determined the content and participation measures that could predict the likelihood of recovery of these types of users on Tumblr. They found that only half of the analyzed users were likely to exhibit signs of recovery after a 4-year period, demonstrating the importance of further consideration of the stages that lead to recovery, thus supporting the transtheoretical model.

Regarding the analysis of content generated by Spanish speakers with EDs, we identified two approaches that address writings in this language [9,25]. Both approaches are focused on the analysis and detection of users with AN at the post level, with a main focus on textual-based features. Furthermore, these approaches do not address any further recovery stages. Recent works have also addressed other mental conditions such as depression [26] and suicidal ideation [13].

This paper addresses the characterization of EDs, in particular AN, in social media. We aim to answer the following research questions (RQs): (1) Which are the visual, textual, behavioral, and demographical elements that characterize and distinguish users at the early, treatment, and full recovery stages of AN on social platforms?; (2) Which elements characterize the social network of users that use anorexia-related terms?; (3) Which are the topics of interest among AN users and their followees?; (4) Which linguistic attributes characterize Spanish-speaking users with AN?; (5) Does the proportion of tweets related to AN significantly change according to the recovery stage?; (6) In a social platform context, can the elements that distinguish users at the precontemplation stage from users at the contemplation stage be identified?; and (7) Are there significant differences between the focused and random control groups?

The main contributions of this work are as follows:

We chose Twitter as our main data source, as it has been previously used for studying mental disorders, including EDs, on social media [14,27]. Twitter is suitable as it allows the collection of a set of chronologically organized posts in Spanish and provides metadata relevant for analyzing the relational and behavioral aspects of users.

We selected keywords and popular hashtags commonly used by ED communities, phrases likely to be used by people undergoing treatment, and terms used by recovered users. These keywords and phrases were manually collected and classified from multiple sources in Spanish and English, including proana blogs, academic publications, and documents made available by the Spanish Association Against Anorexia and Bulimia [14,28,29].

In addition, we conducted a survey among volunteers who have recovered from AN. The phrases and keywords collected were evaluated and filtered by clinicians that were asked to agree on choosing up to 30 keywords or phrases in Spanish that would lead to reach posts from users with AN, including proana, peso objetivo (objective weight), perder peso (lose weight), IMC (body mass index BMI), sibutramina (sibutramine), mi anorexia (my anorexia), ana y mia (ana and mia). We collected 114,627 public tweets containing the search phrases, considering a period in the past, from December 21, 2017, to December 21, 2018. At the same time, a sample of up to 10,000 tweets from the same search period was collected for each user.

To protect the privacy and identity of these users, generic identifiers were assigned to each of them and their posts. In addition, we removed any personal information from the users’ descriptions and tweet texts such as usernames, names (proper nouns), email addresses, URLs, password combinations, location names, and all numbers. The extracted metadata elements and texts passed through a strict transformation process to build and exclusively store vector representations of the features of interest, guaranteeing the analysis of fully anonymized information.

For labeling purposes, we filtered and only considered users with at least three different tweets containing the selected keywords for each category. Among all categories, 645 users met this criterion. Before the submission of the text samples to the annotators, the sample of tweets' texts selected for annotation were anonymized and translated to English to avoid the reidentification of users based on their writings.

We defined five independent groups of users: (1) AN users that manifest the first stages of the disorder and describe signs and symptoms of AN in their texts, which includes users at both the precontemplation and contemplation stages according to the transtheoretical model; (2) a focused control group in which, similar to our prior work [13], we included users that did not manifest signs of anorexia but use terms related to the disorder in their writings; (3) treatment users that explicitly stated that they have been diagnosed with AN and are in treatment; (4) recovered users who claim they have recovered from AN; and (5) doubtful cases in which clinicians were not sure about any of the prior categories. A total of 5 annotators participated in the labeling process: 3 psychologists and 2 psychiatrists. These annotators collaborated closely with organizations specializing in the treatment of EDs. The final label for a user’s set of tweets was assigned if at least three annotators agreed on the assigned label. For cases where an agreement was not met, the users’ tweets were categorized as doubtful cases.

From this first classification approach, a total of 195 users were classified as users with AN, 283 as focused control users, 29 as under treatment users, 18 as recovered users, and 119 as doubtful cases. We performed an interannotator agreement analysis and obtained a Light κ coefficient of 0.4751 (P<.001), which is the result of the averaged Cohen κ values calculated between each pair of annotators. This approach was chosen over Fleiss κ, as all annotators evaluated every sample. The values obtained suggest a moderate agreement among annotators.

In addition to the focused control group, we included another control group consisting of 223 randomly selected users called random control users. These users did not necessarily use terms related to AN and were selected using Twitter’s Sample Tweets application programming interface (API) [30], which provides a set of random tweets, from which we could obtain the users for our sample. By analyzing the description and a sample of their tweets, we ensured that no users with AN were part of this group. The anonymization and translation process applied for the other groups was followed for this case. Table 1 summarizes the user groups defined initially for the study.

This work is dedicated to the characterization of AN considering the multiple stages through which people on the recovery path go through (RQ1), including the analysis of the differences between the two control groups analyzed: focused and random control (RQ7). Our goal is to identify the elements and patterns that distinguish people at each phase based on the analysis of multiple perspectives that are usually considered by clinicians for screening and treatment purposes. The perspectives analyzed include (1) the way Spanish-speaking users express themselves through written posts (content shared) that can lead to the exploration of linguistic dimensions, affective processes and emotions, personal concerns, topics of interest, vocabulary that implies the existence of risk factors associated with the illness, and vocabulary related to signs and symptoms of AN (RQ4); (2) the social network of users, with a focus on the interests shared with their followees and the detection of communities (RQ2 and RQ3); (3) the users’ behavior, with the analysis of posting patterns in different periods; (4) demographic aspects such as gender and age ranges; and (5) visual elements that include the analysis of the characteristics of the profile picture of users, along with the images shared on posts.

In addition to the analysis of prior perspectives, for a further exploration of textual cues, we built and evaluated classifiers for the detection of individual tweets related to AN. These tools can be useful for the detection of users with AN, as we assume that a larger number of tweets related to anorexia will be found in the profiles of users living with the disorder. Through this analysis, we also wanted to analyze whether the proportion of tweets related to AN changes significantly according to the recovery stage (RQ5). In addition, these tools can later be useful for automatically filtering writings that are exclusively related to the condition. They can also be used for screening and annotation purposes [13].

We also tested a clustering approach to distinguish cases at the precontemplation stage from cases at the contemplation stage (RQ6). People in the precontemplation phase (also known as the honeymoon phase) are characterized by being enthusiastic about their weight loss and the social support they receive. This is a phase in which people believe they are in control of their behavior, and therefore, they are in denial of their unhealthy condition. This behavior differs from the contemplation stage, in which bad habits and symptoms remain, but as the illness progresses, the person notices that there is something wrong and feels that they are losing control of the situation. People at this stage are constantly depressed and may even experience a decrease in their cognitive capabilities [31]. Taking into account these aspects, we consider the detection task relevant, as people at the contemplation stage are more willing to seek help, and therefore, a potential nonintrusive intervention through social media, such as social recommendation, could be successful having users at this stage as a target. However, such potential applications must be further analyzed by considering all the privacy and ethical aspects involved.

After performing the clustering approach with k-means, we obtained two clusters of users among those labeled as AN (RQ6). We analyzed both groups and found strong indicators of the groups identified as our contemplation (cluster 1) and precontemplation (cluster 2) groups. This was mainly because the precontemplation cluster was characterized by higher values of positivity compared with its negativity, whereas the opposite occurred for the contemplation cluster (Figure 9). In addition, the median polarity score for the precontemplation cluster (0.1915) was significantly higher (P<.001) than that for the contemplation (0.1694) cluster. In fact, there were significant differences (P<.001) in the values for all features considered in the clustering approach. In addition, the differences between opposite (positive and negative) emotions such as joy and sadness are lower for the values of the precontemplation cluster, as the early stages of the illness are characterized by signs of the person feeling in control of the weight loss process and being enthusiastic about their progress and the social support received. The median values for all features analyzed for each cluster are displayed in Table 20. Among the AN users, a total of 115 users were assigned to the precontemplation cluster, and 56 users were assigned to the contemplation cluster.

We can observe that the results obtained for this task are consistent with the characteristics of people going through each stage; however, there is no annotated information for the validation of the classification performed, and this is an aspect that needs to be addressed in the future. Ours is the first approach to address this classification task, which is relevant given that users at the contemplation stage are more prone to seek help, and therefore, different ways to approach each type of user may be considered.

This study analyzed the elements that characterize Spanish-speaking users with AN during the stages of their recovery process. We have considered multiple points of view, including linguistic, psychological, relational, behavioral, demographical, and visual aspects, for our analysis. We believe that this is the first approach to analyze both users in treatment and those fully recovered in multiple perspectives. Furthermore, we identified the topics of interest of different types of users that used AN-related vocabulary, along with those of their followees. In this section, we report the main findings according to the RQs addressed.

With regard to RQ1, we found multiple elements that characterize and distinguish users with AN at the early, treatment, and full recovery stages. AN users tweeted more frequently at night and on weekends than focused control users. These results are consistent with clinical findings that suggest that patients with AN often report poor sleep quality and reduced sleep time [67]. In addition, the image results indicate that the analysis of visual elements is relevant for the detection of AN cases and focused control cases. In particular, our findings showed that the features extracted from the content generated by users are the most relevant for characterizing AN users, especially those related to linguistic and psychological factors, including terms that describe risk factors and the signs and symptoms of AN (ie, anorexia-related vocabulary).

With regard to RQ4, we determined the linguistic attributes that characterize Spanish-speaking users with AN and found that similar to a related work on English texts [6], the high use of first-person singular pronouns and verbs conjugated with these pronouns distinguishes AN users from control and recovered cases (P<.001). We have also observed that the AN group is characterized by a significantly lower use of articles and higher use of impersonal pronouns than control users (P<.001), which, to the best of our knowledge, has not been reported in any related work in English.

Our findings also reinforced the relevance of textual elements through the development of a deep learning model for the detection of AN-related tweets, which was constructed to address RQ5. We explored the change in the ratio of posts related to AN tweeted by users at each stage and found that users at the early stages of AN posted more AN-related tweets. In addition, highly significant differences among AN cases, recovered cases, and control cases (P<.001) were observed. There were also very significant differences between the AN and treatment groups (P=.004) in terms of this feature, that is, the proportion of tweets related to AN significantly changed depending on the recovery stage, indicating the progress in the recovery process of social media users with AN.

Among the relational factors explored (RQ2), we found that the AN and focused control groups could be identified by analyzing the structure of their social network (clustering approach). Among focused control users, there were several organizations and specialists for the treatment and prevention of EDs. The high polarization noted among the AN- and focused control–related communities reinforces the findings of previous studies conducted on networks of English speakers [17,18], which reported limited interactions between pro-ED and prorecovery communities (RQ4). From a psychological perspective, these findings can be explained by the elements that characterize people at the precontemplation stage according to the transtheoretical model of health behavior change, where people are in denial of their unhealthy conditions and tend to feel supported by their equals (pro-ED community members) [4,5], resulting in a rejection of prorecovery content. Regarding the topics of interest of users and their followees (RQ3), we found that the interests of AN users and their followees were highly correlated (ρ=0.96). We also observed a higher correlation between the treatment followees and focused control followee groups (ρ=0.86) and the recovered followees versus focused control followees groups (ρ=0.86) in comparison with the AN followees versus focused control followees groups (ρ=0.69). These results suggest that more interests are shared with focused control users as the recovery process advances.

With regard to RQ6, we posed a question regarding the detection of users at the precontemplation stage and users at the contemplation stage. After applying a clustering process and using emotion indicators, we identified two clusters, where one was characterized by a predominance of positive emotions over the negative ones, suggesting that users at the precontemplation stage could be the members of this cluster. On the other hand, the second cluster showed values that characterize people at the contemplation stage, where negative emotions predominate.

Finally, to address RQ7, where we explore the existence of differences among our control groups (random and focused control users), we observed that the focused control group had three times more organizations’ accounts than the random control group. We also noticed that the main differences among these groups were found in linguistic attributes, especially for focused control users that were characterized by the use of a reduced number of swearing terms and more anorexia-related vocabulary terms. These findings complement our prior assumptions that focused control users were mostly organizations, specialists, and clinicians, corresponding to a prorecovery community [6,17,18].

Given our findings, another relevant aspect to discuss is the indirect and unintended role of social platforms in the promotion of harmful content among users with and without EDs. A previous study [68] reported significant decreases in caloric intake among people exposed to pro-ED websites between preexposure and postexposure. As recommender systems are designed, users with AN are likely to be recommended to follow users similar to them (other AN users), and we believe that this indirectly contributes to the reinforcement of unhealthy habits. Tools that are aware of these risks must thus be developed.

The analyses performed in this study present certain limitations that are mainly given by the structure of the social platform analyzed, which does not provide explicit information regarding elements relevant to our analysis, such as the location, age, gender, or medical records of users. This is the main reason that has led us to infer information based on the analysis of the users’ posts; therefore, the accuracy of our results is limited to the performance of the tools we have applied. This applies to the demographical features inferred (age groups and gender); the analysis of elements that are related to the signs and symptoms of anorexia (terms related to risk factors and AN-related vocabulary); image analysis tools; and the inference of aspects that involve the location of the user, such as the weekend tweet count ratio and the sleep period tweeting ratio. This last feature is calculated in a way that overcomes the issue of not knowing the difference in the posting time according to the user’s time zone. This aspect is also an issue regarding the tweeting frequency in different periods of the year, as the seasons change according to the location of users. In this sense, a Spanish variant classifier could be useful to distinguish users from different locations; however, this would still be insufficient for the cases of migrants.

We did not measure the biases introduced by the platform in our data set; however, our samples seemed to be representative of the reality in terms of age and gender despite most Twitter users being male and middle-aged [69]. The AN group users were mostly female teenagers, consistent with the age group with the highest incidence rate (15- to 19-year-old females) for AN [54]. We also considered the limitations owing to the accuracy of the translation of terms to English for the annotation and use of Empath as a topic detection tool.

We also considered the limitations that pose the characteristics of users who have a preference for Twitter as a platform and who choose to make their tweets publicly available, which might differ from those that keep their profiles private. It is important to recall that our study is limited to users who make use of Twitter; therefore, the analysis of the behavior of users from other social platforms and even of people with AN that do not have accounts on any social platforms is out of our reach.

This work was approved by the ethical review board of Pompeu Fabra University. To avoid processing and storing personal or sensitive data, a proper process of data transformation and anonymization was followed. We only stored the extracted transformed features. Our approach corresponded to an observational study, with findings that implied that the detection of AN on social media is viable; however, the further development and deployment of detection tools are required. Thus, it is necessary to perform a prior and proper risk-benefit assessment accompanied by the analysis of the legal frameworks that regulate such activities. It is also important to address the potential misusage capabilities of such tools [16,70].

Regarding the reproducibility of this work, Twitter’s policies on the distribution of the data collected through its API should be respected. No information that could lead to the identification of the users included in our study will be shared, as we did not store any personal information [71]. However, the values of the features calculated are available upon reasonable request and after a proper evaluation of the use purpose.

We believe that our findings are relevant to the development of predictive models that can assist specialists in the detection of users with AN and that can display indicators for risk factors as well as signs and symptoms that characterize AN. A proper risk assessment process should be performed before the deployment of such models.

Another aspect that we intend to analyze is the area of recommender systems. According to our findings, AN users mainly followed AN-related content, which reinforces their harmful behaviors. Thus, a recommender system that breaks this bubble and reduces the polarization between AN and focused control users can be an interesting path to follow. This is relevant considering that most recommender systems of social platforms are focused on matching users with shared interests, which in this case, might not be appropriate.