Early Risk Detection of Anorexia on Social Media

Ramírez‐Cifuentes, Diana; Mayans, Marc; Freire, Ana

doi:10.1007/978-3-030-01437-7_1

Cited by 20 publications

(16 citation statements)

References 27 publications

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Section: Introductionmentioning

confidence: 99%

“…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][9][10][11][12][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][11][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].…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Ramírez‐Cifuentes¹,

Freire²,

Baeza-Yates³

et al. 2021

J Med Internet Res

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Background Eating disorders are psychological conditions characterized by unhealthy eating habits. Anorexia nervosa (AN) is defined as the belief of being overweight despite being dangerously underweight. The psychological signs involve emotional and behavioral issues. There is evidence that signs and symptoms can manifest on social media, wherein both harmful and beneficial content is shared daily. Objective This study aims to characterize Spanish-speaking users showing anorexia signs on Twitter through the extraction and inference of behavioral, demographical, relational, and multimodal data. By using the transtheoretical model of health behavior change, we focus on characterizing and comparing users at the different stages of the model for overcoming AN, including treatment and full recovery periods. Methods We analyzed the writings, posting patterns, social relationships, and images shared by Twitter users who underwent different stages of anorexia nervosa and compared the differences among users going through each stage of the illness and users in the control group (ie, users without AN). We also analyzed the topics of interest of their followees (ie, users followed by study participants). We used a clustering approach to distinguish users at an early phase of the illness (precontemplation) from those that recognize that their behavior is problematic (contemplation) and generated models for the detection of tweets and images related to AN. We considered two types of control users—focused control users, which are those that use terms related to anorexia, and random control users. Results We found significant differences between users at each stage of the recovery process (P<.001) and control groups. Users with AN tweeted more frequently at night, with a median sleep time tweets ratio (STTR) of 0.05, than random control users (STTR=0.04) and focused control users (STTR=0.03). Pictures were relevant for the characterization of users. Focused and random control users were characterized by the use of text in their profile pictures. We also found a strong polarization between focused control users and users in the first stages of the disorder. There was a strong correlation among the shared interests between users with AN and their followees (ρ=0.96). In addition, the interests of recovered users and users in treatment were more highly correlated to those corresponding to the focused control group (ρ=0.87 for both) than those of AN users (ρ=0.67), suggesting a shift in users’ interest during the recovery process. Conclusions We mapped the signs of AN to social media context. These results support the findings of previous studies that focused on other languages and involved a deep analysis of the topics of interest of users at each phase of the disorder. The features and patterns identified provide a basis for the development of detection tools and recommender systems.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Ramírez‐Cifuentes¹,

Freire²,

Baeza-Yates³

et al. 2021

J Med Internet Res

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“…These are features that have been used to address similar tasks, such as depression detection and eating disorders screening [ 7 , 31 ]. These models represent terms or sequences of terms (n-grams) based on their frequencies on the documents analyzed.…”

Section: Methodsmentioning

confidence: 99%

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis

Ramírez‐Cifuentes¹,

Freire²,

Baeza-Yates³

et al. 2020

J Med Internet Res

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Background Suicide risk assessment usually involves an interaction between doctors and patients. However, a significant number of people with mental disorders receive no treatment for their condition due to the limited access to mental health care facilities; the reduced availability of clinicians; the lack of awareness; and stigma, neglect, and discrimination surrounding mental disorders. In contrast, internet access and social media usage have increased significantly, providing experts and patients with a means of communication that may contribute to the development of methods to detect mental health issues among social media users. Objective This paper aimed to describe an approach for the suicide risk assessment of Spanish-speaking users on social media. We aimed to explore behavioral, relational, and multimodal data extracted from multiple social platforms and develop machine learning models to detect users at risk. Methods We characterized users based on their writings, posting patterns, relations with other users, and images posted. We also evaluated statistical and deep learning approaches to handle multimodal data for the detection of users with signs of suicidal ideation (suicidal ideation risk group). Our methods were evaluated over a dataset of 252 users annotated by clinicians. To evaluate the performance of our models, we distinguished 2 control groups: users who make use of suicide-related vocabulary (focused control group) and generic random users (generic control group). Results We identified significant statistical differences between the textual and behavioral attributes of each of the control groups compared with the suicidal ideation risk group. At a 95% CI, when comparing the suicidal ideation risk group and the focused control group, the number of friends (P=.04) and median tweet length (P=.04) were significantly different. The median number of friends for a focused control user (median 578.5) was higher than that for a user at risk (median 372.0). Similarly, the median tweet length was higher for focused control users, with 16 words against 13 words of suicidal ideation risk users. Our findings also show that the combination of textual, visual, relational, and behavioral data outperforms the accuracy of using each modality separately. We defined text-based baseline models based on bag of words and word embeddings, which were outperformed by our models, obtaining an increase in accuracy of up to 8% when distinguishing users at risk from both types of control users. Conclusions The types of attributes analyzed are significant for detecting users at risk, and their combination outperforms the results provided by generic, exclusively text-based baseline models. After evaluating the contribution of image-based predictive models, we believe that our results can be improved by enhancing the models based on textual and relational features. These methods can be extended and applied to different use cases related to other mental disorders.

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“…Paul, Kalyani and Basu [34] determined that the ada boost classifier was the best model to predict anorexia and depression, particularly when combined with the bag of words model. Additionally, Ramirez-Cifuentes and colleagues [35] compared different machine learning models and found that a logistic regression model detected anorexia behavior with the highest confidence level.…”

Section: Previous Researchmentioning

confidence: 99%

Exploring the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, and Autism Quotient to Identify Eating Disorder Vulnerability: A Cluster Analysis

Rosenfield

Linstead

2020

MAKE

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Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, and Autism Quotient scores. The goal is to identify prevalent cluster topologies in the data, using the truth data as a means to validate identified groupings. Our results show that a model with k = 2 performs the best and clustered the dataset in the most appropriate way. This matches our truth data group labels, and we calculated our model’s accuracy at 78.125%, so we know that our model is working well. We see that the Eating Disorder Examination Questionnaire (EDE-Q) and Clinical Impairment Assessment (CIA) scores are, in fact, important discriminators of eating disorder behavior.

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Early Risk Detection of Anorexia on Social Media

Cited by 20 publications

References 27 publications

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Characterization of Anorexia Nervosa on Social Media: Textual, Visual, Relational, Behavioral, and Demographical Analysis

Detection of Suicidal Ideation on Social Media: Multimodal, Relational, and Behavioral Analysis

Exploring the Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, and Autism Quotient to Identify Eating Disorder Vulnerability: A Cluster Analysis

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