Towards Explainability in Using Deep Learning for the Detection of Anorexia in Social Media

Amini, Hessam; Kosseim, Leila

doi:10.1007/978-3-030-51310-8_21

Cited by 13 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

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%

“…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].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

View full text Add to dashboard Cite

show abstract

“…Overall, much work is focused on automatically identifying whether online social media users are deemed as positive mental health concerns cases. On very few occasions, the authors of such works attempt to obtain some explanations from the outcomes of the classification models to better understand why certain decisions were taken, as happened with some eRisk participants (Amini & Kosseim, 2020;Uban et al, 2021;Aragon et al, 2021). However, research devoted to understanding, measuring, visualising and providing insight on the attributes that characterise such users and differentiate them both from healthy individuals and between diverse disorders has been noticeably scarce.…”

Section: Related Workmentioning

confidence: 99%

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

Ríssola¹,

Aliannejadi²,

Crestani³

2022

Preprint

View full text Add to dashboard Cite

Due to the worldwide accessibility to the Internet along with the continuous advances in mobile technologies, physical and digital worlds have become completely blended, and the proliferation of social media platforms has taken a leading role over this evolution. In this paper, we undertake a thorough analysis towards better visualising and understanding the factors that characterise and differentiate social media users affected by mental disorders. We perform different experiments studying multiple dimensions of language, including vocabulary uniqueness, word usage, linguistic style, psychometric attributes, emotions' co-occurrence patterns, and online behavioural traits, including social engagement and posting trends.Our findings reveal significant differences on the use of function words, such as adverbs and verb tense, and topic-specific vocabulary, such as biological processes. As for emotional expression, we observe that affected users tend to share emotions more regularly than control individuals on average. Overall, the monthly posting variance of the affected groups is higher than the control groups. Moreover, we found evidence suggesting that language use on micro-blogging platforms is less distinguishable for users who have a mental disorder than other less restrictive platforms. In particular, we observe on Twitter less quantifiable differences between affected and control groups compared to Reddit.

show abstract

“…In precision-oriented applications, users require to understand why and how a prediction is made. Inspection of a system helps developers to detect fallacies in a system and provide insight for the improvement of the system (Verma et al, 2020;Amini and Kosseim, 2019) and users to trust the autonomous system.…”

Section: Introductionmentioning

confidence: 99%

Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing

Bagherzadeh¹,

Bergler²

2021

Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP

View full text Add to dashboard Cite

show abstract

Towards Explainability in Using Deep Learning for the Detection of Anorexia in Social Media

Cited by 13 publications

References 18 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

Mental Disorders on Online Social Media Through the Lens of Language and Behaviour: Analysis and Visualisation

Interacting Knowledge Sources, Inspection and Analysis: Case-studies on Biomedical text processing

Contact Info

Product

Resources

About