Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction

[EN] This paper summarizes the contributions of the PRHLT- UPV team as a participant in the eRisk 2020 tasks on self-harm detection and prediction of depression levels from social media. Computational methods based on machine learning and natural language processing have a great potential to assist with early detection of mental disorders of social media users, based on their online activity.We use multi-dimensional representations of language, and compare various deep learning models' performance, exploring rarely approached avenues in previous research, including hierarchical deep learning architectures and pre-trained transformers and language models.The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana.Uban, A.; Rosso, P. (2020). Deep Learning Architectures and Strategies for Early Detection of Self-harm and Depression Level Prediction. CEUR Workshop Proceedings. 2696:1-12. http://hdl.handle.net/10251/166536S112269

UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet

[EN] This paper presents the contributions of the UPV-Symanto team, a collaboration between Symanto Research and the PRHLT Center, in the eRisk 2021 shared tasks on gambling addiction, self-harm detection and prediction of depression levels. We have used a variety of models and techniques, including Transformers, hierarchical attention networks with multiple linguistic features, a dedicated early alert decision mechanism, and temporal modelling of emotions. We trained the models using additional training data that we collected and annotated thanks to expert psychologists. Our emotions-over-time model obtained the best results for the depression severity task in terms of ACR (and second best according to ADODL). For the self-harm detection task, our Transformer-based model obtained the best absolute result in terms of ERDE5 and we ranked equal first in terms of speed and latency.The authors from Universitat Politècnica de València thank the EU-FEDER Comunitat Valenciana 2014-2020 grant IDIFEDER/2018/025. The work of Paolo Rosso was in the framework of the research project PROMETEO/2019/121 (DeepPattern) by the Generalitat Valenciana. We would like to thank the two anonymous reviewers who helped us improve this paper.Basile, A.; Chinea-Ríos, M.; Uban, A.; Müller, T.; Rössler, L.; Yenikent, S.; Chulvi-Ferriols, MA.... (2021). UPV-Symanto at eRisk 2021: Mental Health Author Profiling for Early Risk Prediction on the Internet. CEUR. 908-927. http://hdl.handle.net/10251/19067090892

From the edge of a rib: pro-ana edgeworking narratives

People who deliberately practice eating disordered behaviors as a lifestyle form digital communities known as pro-ana. Pro-ana identity is constructed through the medicalized frameworks of anorexia nervosa that attempt to maximize weight loss and also manage other eating disorder behaviors such as binging and purging. This thesis examines pro-ana materials requested and produced on websites, Whisper, and Reddit, assembling these discursive and image exchanges into discourses that identify and describe eating disorder pathology and recovery. My ethnographic collection consists of screenshots of written materials and images from websites, Whisper posts, Reddit threads, and a Discord. This thesis examines discursive strategies used by pro-ana writers to avoid censorship, maintain pro-ana identity, move toward lower weight goals, and manage physiological risks and social risks of stigmatization or interference. Personification and other figurative externalizations of eating disorders separate and concretize the chaotic and compulsive aspects of eating disorders. These writers construct iconic figures, skill-sets and practices that both embrace and reject ideologies around anorexia nervosa and other eating disorders

A grounded theory analysis of the forms of support on two online anorexia forums

Using Grounded theory this thesis analyses the forms of support that are present on two online anorexia forums. Data was collected through non-participant observation and online interviews with members of two online anorexia forums, one pro-anorexic in orientation, one pro-recovery. Despite the clear differences that exist between the two communities, continuities are strongly apparent, especially when looking at these forums as support environments. This thesis illustrates that support is conditional, that is takes on a variety of forms in any one environment and highlights the role of offline discourses in shaping online support. It also provides an in-depth comparison of two online anorexia forums

Understanding, measuring and treating eating disorders in those with type 1 diabetes

The purpose of this thesis was to explore the nature of Eating Disorders in Type 1 Diabetes. Whether or not Eating Disorders are more prevalent in this demographic is a topic of contention but regardless there is a consensus that those with comorbid Type 1 have considerably worse outcomes and are significantly more difficult to treat. It has been argued that this may be due to a feature unique to this population; insulin omission for weight control. The first aim of this thesis was to systematically review how Eating Disorders have been measured in Type 1 Diabetes, paying particular attention to whether researchers have taken the role of Diabetes regimen and insulin omission into account. Following this a comparison between two Eating Disorder scales, one Diabetes specific the other not, was made in order to compare prevalence rates, to explore which items may be potentially biased and to investigate what the effect of modification may be. The structure of the Diabetes specific scale (the Diabetes Eating Problem Scale Revised) was then explored. The second aim of this thesis was to replicate a pilot study that aimed to explore demographic, psychological and health seeking features of those with Type 1 Diabetes related Eating Disorders. This formed the basis of a structural model whereby psychological and Diabetes specific traits were hypothesised to predict Eating Disorder behaviour and elevated blood glucose levels. A questionnaire built for that study regarding patient attributions was also reanalysed using new data. The final aim was to investigate how Eating Disorders in Type 1 Diabetes have been treated by reviewing literature from the last 2 decades, paying particular attention as to how treatment providers have accommodated the unique needs of those with T1D and whether or not programmes have been successful in relation to both psychological and biological outcomes

Alone together: Exploring community on an incel forum

Incels, or involuntary celibates, are men who are angry and frustrated at their inability to find sexual or intimate partners. This anger has repeatedly resulted in violence against women. Because incels are a relatively new phenomenon, there are many gaps in our knowledge, including how, and to what extent, incel forums function as online communities. The current study begins to fill this lacuna by qualitatively analyzing the incels.co forum to understand how community is created through online discourse. Both inductive and deductive thematic analyses were conducted on 17 threads (3400 posts). The results confirm that the incels.co forum functions as a community. Four themes in relation to community were found: The incel brotherhood; We can disagree, but you’re wrong; We are all coping here; and Will the real incel come forward. The four themes elucidate that incels most often exchange informational and emotional support

Automatic Detection of Emotions and Distress in Textual Data

Online data can be analyzed for many purposes, including the prediction of stock market, business, and political planning. Online data can also be used to develop systems for the automatic emotion detection and mental health assessment of users. These systems can be used as complementary measures in monitoring online forums by detecting users who are in need of attention. In this thesis, we first present a new approach for contextual emotion detection, i.e. emotion detection in short conversations. The approach is based on a neural feature extractor, composed of a recurrent neural network with an attention mechanism, followed by a final classifier, that can be neural or SVM-based. The results from our experiments showed that, by providing a higher and more robust performance, SVM can act as a better final classifier in comparison to a feed-forward neural network. We then extended our model for emotion detection, and created an ensemble approach for the task of distress detection from online data. This extended approach utilizes several attention-based neural sub-models to extract features and predict class probabilities, which are later used as input features to a Support Vector Machine (SVM) making the final classification. Our experiments show that using an ensemble approach which makes use different sub-models accessing diverse sources of information can improve classification in the absence of a large annotated dataset. The extended model was evaluated on two shared tasks, CLPsych and eRisk 2019, which aim at suicide risk assessment, and early risk detection of anorexia, respectively. The model ranked first in tasks A and C of CLPsych 2019 (with macro-average F1 scores of 0.481 and 0.268, respectively), and ranked first in the first task of eRisk 2019 in terms of F1 and latency-weighted F1 scores (0.71 and 0.69, respectively)

Health Misinformation in Search and Social Media

People increasingly rely on the Internet in order to search for and share health-related information. Indeed, searching for and sharing information about medical treatments are among the most frequent uses of online data. While this is a convenient and fast method to collect information, online sources may contain incorrect information that has the potential to cause harm, especially if people believe what they read without further research or professional medical advice. The goal of this thesis is to address the misinformation problem in two of the most commonly used online services: search engines and social media platforms. We examined how people use these platforms to search for and share health information. To achieve this, we designed controlled laboratory user studies and employed large-scale social media data analysis tools. The solutions proposed in this thesis can be used to build systems that better support people's health-related decisions. The techniques described in this thesis addressed online searching and social media sharing in the following manner. First, with respect to search engines, we aimed to determine the extent to which people can be influenced by search engine results when trying to learn about the efficacy of various medical treatments. We conducted a controlled laboratory study wherein we biased the search results towards either correct or incorrect information. We then asked participants to determine the efficacy of different medical treatments. Results showed that people were significantly influenced both positively and negatively by search results bias. More importantly, when the subjects were exposed to incorrect information, they made more incorrect decisions than when they had no interaction with the search results. Following from this work, we extended the study to gain insights into strategies people use during this decision-making process, via the think-aloud method. We found that, even with verbalization, people were strongly influenced by the search results bias. We also noted that people paid attention to what the majority states, authoritativeness, and content quality when evaluating online content. Understanding the effects of cognitive biases that can arise during online search is a complex undertaking because of the presence of unconscious biases (such as the search results ranking) that the think-aloud method fails to show. Moving to social media, we first proposed a solution to detect and track misinformation in social media. Using Zika as a case study, we developed a tool for tracking misinformation on Twitter. We collected 13 million tweets regarding the Zika outbreak and tracked rumors outlined by the World Health Organization and the Snopes fact-checking website. We incorporated health professionals, crowdsourcing, and machine learning to capture health-related rumors as well as clarification communications. In this way, we illustrated insights that the proposed tools provide into potentially harmful information on social media, allowing public health researchers and practitioners to respond with targeted and timely action. From identifying rumor-bearing tweets, we examined individuals on social media who are posting questionable health-related information, in particular those promoting cancer treatments that have been shown to be ineffective. Specifically, we studied 4,212 Twitter users who have posted about one of 139 ineffective ``treatments'' and compared them to a baseline of users generally interested in cancer. Considering features that capture user attributes, writing style, and sentiment, we built a classifier that is able to identify users prone to propagating such misinformation. This classifier achieved an accuracy of over 90%, providing a potential tool for public health officials to identify such individuals for preventive intervention

Anorexia nervosa, depression and medicalisation: a corpus-based study of patients and professionals

This study reports on the analysis of the Mental Health Discourse Corpus. This dataset is comprised of four sub-corpora that contain patients' online discussions of anorexia nervosa, patients' online discussions of depression, general practitioners' discussions of anorexia, and general practitioners' discussions of depression, respectively. The methodology integrates quantitative corpus linguistic approaches with qualitative analysis drawing on Hallidayan functional grammar, discourse analysis and discursive psychology. By interrogating corpora of health communication across communicative modes and participants, the study offers novel insights into the verbal presentation of anorexia and depression by patients and professionals, and examines their respective uptake of medical explanations of mental illness. Common patterns in the online patient interactions are linguistic choices which realise the externalisation and personification of anorexia and depression, the discursive construction of individual helplessness, and the representation of psychological distress in terms of medical pathology. The uptake and proliferation of biomedical explanatory models of anorexia and depression serves to reduce illness stigma for individuals and, notably, is also used to perform local interactional tasks. In the practitioners' talk, participants draw on medical and social explanations of depression and anorexia. Doctors construct depression as a categorical medical diagnosis while also expressing doubt towards its medical treatment and advocating non-medical interventions. When discussing anorexia, clinicians emphasise the bureaucratic role which body mass index scores occupy in managing anorexia and repeatedly highlight the difficulty of overcoming patients' resistance. In both cases, participants highlight the bureaucratic and communicative challenges of working with anorexic and depressed patients and construct a range of unfavourable moral identities for the chronically ill. The practical implications of the research for users of online support groups and general practitioners working with depressed and anorexic patients are identified. In particular, I emphasise the centrality of communication to primary mental health care and the utility of studying online support groups to illuminate the experiences and beliefs of patients. A critical evaluation of the study's methodology is offered, along with recommendations for future research