A Multimodal Deep Learning Approach for Identification of ‎Severity of Reflective Depression ‎

Social media consumes a greate time of our dialy times that generate a significant amount of information through expressing feeling and activities, sharing admiral contents, viewing, and more. This information mostly contains valuable discoveries. Despite many attempts to mining such produced data, it is still unexploited in certain issues and attracts many research areas. In this paper, we use the data extracted from social media from female’s pages to detect possibility of depression. A new deep learning model based on the psycholinguistic vocabulary to create the embedding words is developed. First, we extract the features from the data before and after the preprocessing phase. Second, the Convolutional Neural Network (CNN) is used to label the data for extracting the remaining features. Based on the previouse two phases; the developed model succeeded to predict the depression possibilty. Adetailed comparative analysis is also presented for the evaluation of the proposed system. The proposed indicator model proved promising results in predicting depression

Detecting Suicidality in Arabic Tweets Using Machine Learning and Deep Learning Techniques

Social media platforms have revolutionized traditional communication techniques by enabling people globally to connect instantaneously, openly, and frequently. People use social media to share personal stories and express their opinion. Negative emotions such as thoughts of death, self-harm, and hardship are commonly expressed on social media, particularly among younger generations. As a result, using social media to detect suicidal thoughts will help provide proper intervention that will ultimately deter others from self-harm and committing suicide and stop the spread of suicidal ideation on social media. To investigate the ability to detect suicidal thoughts in Arabic tweets automatically, we developed a novel Arabic suicidal tweets dataset, examined several machine learning models, including Na\"ive Bayes, Support Vector Machine, K-Nearest Neighbor, Random Forest, and XGBoost, trained on word frequency and word embedding features, and investigated the ability of pre-trained deep learning models, AraBert, AraELECTRA, and AraGPT2, to identify suicidal thoughts in Arabic tweets. The results indicate that SVM and RF models trained on character n-gram features provided the best performance in the machine learning models, with 86% accuracy and an F1 score of 79%. The results of the deep learning models show that AraBert model outperforms other machine and deep learning models, achieving an accuracy of 91\% and an F1-score of 88%, which significantly improves the detection of suicidal ideation in the Arabic tweets dataset. To the best of our knowledge, this is the first study to develop an Arabic suicidality detection dataset from Twitter and to use deep-learning approaches in detecting suicidality in Arabic posts

Reconocimiento de depresión en redes sociales basado en la detección de síntomas

Depression is a common mental disorder that affects millions of people around the world. Recently, several methods have been proposed that detect people suffering from depression by analyzing their language patterns in social media. These methods show competitive results, but most of them are opaque and lack of explainability. Motivated by these problems, and inspired by the questionnaires used by health professionals for its diagnosis, in this paper we propose an approach for the detection of depression based on the identification and accumulation of evidence of symptoms through the users’ posts. Results in a benchmark collection are encouraging, as they show a competitive performance with respect to state-of-the-art methods. Furthermore, taking advantage of the approach’s properties, we outline what could be a support tool for healthcare professionals for analyzing and monitoring depression behaviors in social networks.La depresión es un trastorno mental que afecta a millones de personas en todo el mundo. Recientemente, se han propuesto varios métodos que detectan personas que sufren depresión analizando sus patrones de lenguaje en las redes sociales. Estos métodos han mostrado resultados competitivos, sin embargo la mayoría son opacos y carecen de explicabilidad. Motivados por estos problemas, e inspirados en los cuestionarios utilizados por los profesionales de la salud para su diagnóstico, en este trabajo proponemos un método para la detección de depresión basado en la identificación y acumulación de evidencia de síntomas a través de las publicaciones de los usuarios. Los resultados obtenidos en una colección de referencia son prometedores, ya que muestran un desempeño competitivo con respecto a los mejores métodos actuales. Además, aprovechando las propiedades del método, describimos lo que podría ser una herramienta de apoyo para que los profesionales de la salud analicen y monitoreen las conductas depresivas en las redes sociales

DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media

Twitter is currently a popular online social media platform which allows users to share their user-generated content. This publicly-generated user data is also crucial to healthcare technologies because the discovered patterns would hugely benefit them in several ways. One of the applications is in automatically discovering mental health problems, e.g., depression. Previous studies to automatically detect a depressed user on online social media have largely relied upon the user behaviour and their linguistic patterns including user's social interactions. The downside is that these models are trained on several irrelevant content which might not be crucial towards detecting a depressed user. Besides, these content have a negative impact on the overall efficiency and effectiveness of the model. To overcome the shortcomings in the existing automatic depression detection methods, we propose a novel computational framework for automatic depression detection that initially selects relevant content through a hybrid extractive and abstractive summarization strategy on the sequence of all user tweets leading to a more fine-grained and relevant content. The content then goes to our novel deep learning framework comprising of a unified learning machinery comprising of Convolutional Neural Network (CNN) coupled with attention-enhanced Gated Recurrent Units (GRU) models leading to better empirical performance than existing strong baselines

Social media mental health analysis framework through applied computational approaches

Studies have shown that mental illness burdens not only public health and productivity but also established market economies throughout the world. However, mental disorders are difficult to diagnose and monitor through traditional methods, which heavily rely on interviews, questionnaires and surveys, resulting in high under-diagnosis and under-treatment rates. The increasing use of online social media, such as Facebook and Twitter, is now a common part of people’s everyday life. The continuous and real-time user-generated content often reflects feelings, opinions, social status and behaviours of individuals, creating an unprecedented wealth of person-specific information. With advances in data science, social media has already been increasingly employed in population health monitoring and more recently mental health applications to understand mental disorders as well as to develop online screening and intervention tools. However, existing research efforts are still in their infancy, primarily aimed at highlighting the potential of employing social media in mental health research. The majority of work is developed on ad hoc datasets and lacks a systematic research pipeline. [Continues.]</div

Using Social Media for Actionable Disease Surveillance and Outbreak Management: A Systematic Literature Review

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Mental Health Coping Stories on Social Media: A Casual-Inference Study of Papageno Effect

The Papageno effect concerns how media can play a positive role in preventing and mitigating suicidal ideation and behaviors. With the increasing ubiquity and widespread use of social media, individuals often express and share lived experiences and struggles with mental health on these platforms. However, there is a gap in our understanding about the existence and effectiveness of the Papageno effect in social media, which are studied in this thesis. In particular, this work adopts a causal-inference framework to examine the impact of exposure to mental health coping stories on individuals on Twitter. A Twitter dataset with ~2M posts by ~10K individuals is obtained. This work considers engaging with coping stories as the Treatment intervention, and adopts a stratified propensity score approach to find matched cohorts of Treatment and Control individuals. This work measures the psychosocial shifts in affective, behavioral, and cognitive outcomes in longitudinal Twitter data before and after engaging with the coping stories. The findings of this study reveal that, engaging with coping stories leads to decreased stress and depression, and improved expressive writing, diversity, and interactivity. This thesis discusses the practical and platform design implications in supporting mental wellbeing

Three Research Essays on Human Behaviors in Social Media

Social Media (SM) has grown to be one of the most popular Internet technologies for individual users and has fostered a global community. For instance, recent statistics reveal that monthly active users of Facebook are almost 1.5 billion by Mar 2015. At the same time, 20% of internet users in the US are expected to have Twitter accounts. This figure has grown from 15.2% in 2012, and is expected to rise to 24.2% by 2018 (Twitter 2015). People like spending their time on SM to track the latest news, seek knowledge, update personal status, and connect with friends. It is possible that being exposed to others’ positive information on SM could generate darker emotions, such as envy. Extant literature suggests that envy significantly influences human behaviors and life satisfaction (Krasnova et al. 2013). This dissertation, consisting of three essays, studies the effects of SM on human behaviors. Chapter 2 investigates how others’ positive information arouses envy and influences user behaviors from different angles. Chapter 3 focuses on how espoused national cultures reshape online benign envy and impact SM usage. Chapter 4 discusses the relationship between social media and envy with textual analysis techniques. Chapter 5 provides a summary and overall conclusion to this work. Chapter 2- Envy and How it can Influence SM Use Users tend to disclose the positive side of their lives on SM. Such information can be perceived in an extremely positive light in the eyes of their connections, which could leads to envy. In the current study, we develop a theoretical framework that elaborates the mechanism through which online envy is generated and consequently influences SM usage. We specify that online users experience two types of envy: malicious and benign envy, which have distinct impacts on IS use. Specifically, malicious envy plays a mitigating role and benign envy serves as an enhancer of SM use. Our findings provide valuable implications for both academic researchers and IS practitioners. Chapter 3 – Benign Envy, Social Media, and Culture Although envy universally exits in human society, its influence on human behaviors varies by cultural contexts. As shown in chapter 2, benign envy is a more salient factor in the social media context. In the current essay, we focus on investigating how different espoused national cultural values affect this relationship between online benign envy and consequent behaviors. We also developed a benign envy and IT usage model, which integrates four espoused national cultural values. We conceptualized several main constructs and then theoretically justified the relationships between them. As expected, if people experience benign envy when using SM, they are more likely to continue their use. Moreover, different espoused national culture values work as independent and moderating variables along with the envy procedures. People who hold different levels of culture behave distinctly. The study found that people who espouse a greater level of collectivism were be more likely to compare with other peers in order to evaluate their self-social status; people who espouse higher levels of uncertainty avoidance were more likely to experience benign envy; and the relationship between perceived enhancement and use intention was stronger for individuals with higher levels of espoused masculinity. However, espoused power distance values were not significantly moderating the relationship between perceived enjoyment and intended behavior in the current context (general SM). This study provided some theoretical and practical implications. Chapter 4 – Tweet, Favorite, Status, and Envy Many social media studies have demonstrated that aggregating social information could provide valuable insight into sociological, economical, healthcare, and other critical fields. Among these studies, Twitter has been one of the most popular social platforms that researchers value. It has a greater potential for academics to observe and explore critical social behaviors, such as envy, which could lead to avoidance of using certain IT platforms, emotional depression, and even worse, suicide. With text mining techniques, massive numbers of tweets can be collected, classified, and analyzed. The envy literature has largely theorized on the motivations of envy. However, in the IS context, envy related research is very limited, and the empirical tests are confounded by limited data. In order to address these gaps, we collected envy related tweets from Twitter and classified them into the two types (benign and malicious) of envy relying on text mining techniques with sentiment analysis (positive to negative). Based on the data set, we further analyzed the patterns of online envy. Additionally, by using logistic regression, the impacts of certain social media usage behaviors were tested on differentiating online envy. Our work included both qualitative observation and quantitative analysis, along with the evaluation of regression output