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1. A Hybrid Learning-Architecture for Mental Disorder Detection Using Emotion Recognition

   https://doi.org/10.1109/ACCESS.2024.3421376  

   Aina, Joseph; Akinniyi, Oluwatunmise; Rahman, Md Mahmudur; Odero-Marah, Valerie; Khalifa, Fahmi (January 2024, IEEE Access)

   Mental illness has grown to become a prevalent and global health concern that affects individuals across various demographics. Timely detection and accurate diagnosis of mental disorders are crucial for effective treatment and support as late diagnosis could result in suicidal, harmful behaviors and ultimately death. To this end, the present study introduces a novel pipeline for the analysis of facial expressions, leveraging both the AffectNet and 2013 Facial Emotion Recognition (FER) datasets. Consequently, this research goes beyond traditional diagnostic methods by contributing a system capable of generating a comprehensive mental disorder dataset and concurrently predicting mental disorders based on facial emotional cues. Particularly, we introduce a hybrid architecture for mental disorder detection leveraging the state-of-the-art object detection algorithm, YOLOv8 to detect and classify visual cues associated with specific mental disorders. To achieve accurate predictions, an integrated learning architecture based on the fusion of Convolution Neural Networks (CNNs) and Visual Transformer (ViT) models is developed to form an ensemble classifier that predicts the presence of mental illness (e.g., depression, anxiety, and other mental disorder). The overall accuracy is improved to about 81% using the proposed ensemble technique. To ensure transparency and interpretability, we integrate techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) and saliency maps to highlight the regions in the input image that significantly contribute to the model’s predictions thus providing healthcare professionals with a clear understanding of the features influencing the system’s decisions thereby enhancing trust and more informed diagnostic process. 

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2. Engineering Students Coping With COVID-19: Yoga, Meditation, and Mental Health

   Beddoes, Kacey; Danowitz, Andrew (June 2021, 2021 ASEE Virtual Annual Conference Content Access)

   In 2020, we conducted a nationwide online survey of undergraduate engineering students in the United States to examine how the novel coronavirus pandemic was affecting engineering students’ mental health and what strategies they were using to cope with mental health challenges. The survey was a compilation of validated mental health instruments that screen for depression, anxiety, somatoform disorders, eating disorders, non-specific psychological distress, and post-traumatic stress disorder. Given that prior research has shown that yoga and meditation can help people suffering from anxiety, depression, and post-traumatic stress disorder, we were interested in exploring the subset of respondents who said that they were using yoga and/or meditation to cope with mental health challenges during the pandemic. The research questions addressed in this paper are: 1) What are the demographic characteristics of students who used yoga and/or meditation to cope with mental health challenges of the 2020 novel coronavirus pandemic? and 2) Does the mental health of the students who used these strategies differ in any from the mental health of students who did not use yoga and meditation coping strategies? Based on 669 responses from students at 140 different universities, we found that there were 20 survey items for which the yoga/meditation group fared statistically significantly differently than the non-yoga/meditation group. These 20 items appeared in the screens for depression, anxiety, somatoform disorders, eating disorders, non-specific psychological distress, and post-traumatic stress disorder. For example, yoga and meditation practitioners were significantly less likely to have experienced feelings of hopelessness during the prior 30 days, as well as to have experienced feelings of being so depressed that nothing could cheer them up. A causative relationship cannot be claimed, but the correlations we found align with prior research showing that yoga and meditation can support many aspects of mental health. 

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3. Emoji use in tweets: Relationships with personality traits and word usage

   Kennison, S; Hurtado_Morales, A; Fritz, K; Chan-Tin, E (February 2024, n/a)

   na (Ed.)

   Emojis are digital pictographs used to express ideas and emotions. There are thousands of emojis, some depicting faces with expressions and others depicting objects, animals, and activities. We examined how emoji use on Twitter was related to users’ language use and Big Five personality traits. In the present research, we examined emoji use in tweets for seventy-six twitter accounts belonging to undergraduate students (34 women, 22 men) who also completed an online survey in which they reported personal characteristics including personality traits. With their consent, we retrieved tweets from accounts using the Twitter API and python script. We also analyzed word usage in tweets using the Linguistic Inquiry Word Count (LIWC). The results showed that frequent use of emojis was related to lower levels of openness, but not other Big Five traits. More frequent use of emojis was related to more frequent use of word related to: tone, positive emotion, sad, affect, feel, you pronouns, family, and the body. More frequent use of emojis was related to less frequent use of articles and words related to insight, money, risk, anger, sexual, ingest, informal, and swear words. 

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4. Sexist Slurs: Reinforcing Feminine Stereotypes Online

   https://doi.org/10.1007/s11199-019-01095-z  

   Felmlee, Diane; Inara Rodis, Paulina; Zhang, Amy (November 2019, Sex Roles)

   Social media platforms are accused repeatedly of creating environments in which women are bullied and harassed. We argue that online aggression toward women aims to reinforce traditional feminine norms and stereotypes. In a mixed methods study, we find that this type of aggression on Twitter is common and extensive and that it can spread far beyond the original target. We locate over 2.9 million tweets in one week that contain instances of gendered insults (e.g., “bitch,” “cunt,” “slut,” or “whore”)—averaging 419,000 sexist slurs per day. The vast majority of these tweets are negative in sentiment. We analyze the social networks of the conversations that ensue in several cases and demonstrate how the use of “replies,” “retweets,” and “likes” can further victimize a target. Additionally, we develop a sentiment classifier that we use in a regression analysis to compare the negativity of sexist messages. We find that words in a message that reinforce feminine stereotypes inflate the negative sentiment of tweets to a significant and sizeable degree. These terms include those insulting someone’s appearance (e.g., “ugly”), intellect (e.g., “stupid”), sexual experience (e.g., “promiscuous”), mental stability (e.g., “crazy”), and age (“old”). Messages enforcing beauty norms tend to be particularly negative. In sum, hostile, sexist tweets are strategic in nature. They aim to promote traditional, cultural beliefs about femininity, such as beauty ideals, and they shame victims by accusing them of falling short of these standards. Harassment on social media constitutes an everyday, routine occurrence, with researchers finding 9,764,583 messages referencing bullying on Twitter over the span of two years (Bellmore et al. 2015). In other words, Twitter users post over 13,000 bullying-related messages on a daily basis. Forms of online aggression also carry with them serious, negative consequences. Repeated research documents that bullying victims suffer from a host of deleterious outcomes, such as low self-esteem (Hinduja and Patchin 2010), emotional and psychological distress (Ybarra et al. 2006), and negative emotions (Faris and Felmlee 2014; Juvonen and Gross 2008). Compared to those who have not been attacked, victims also tend to report more incidents of suicide ideation and attempted suicide (Hinduja and Patchin 2010). Several studies document that the targets of cyberbullying are disproportionately women (Backe et al. 2018; Felmlee and Faris 2016; Hinduja and Patchin 2010; Pew Research Center 2017), although there are exceptions depending on definitions and venues. Yet, we know little about the content or pattern of cyber aggression directed toward women in online forums. The purpose of the present research, therefore, is to examine in detail the practice of aggressive messaging that targets women and femininity within the social media venue of Twitter. Using both qualitative and quantitative analyses, we investigate the role of gender norm regulation in these patterns of cyber aggression. 

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5. Analyzing Machine Learning Models that Predict Mental Illnesses from Social Media Text

   Weerasinghe, Janith; Morales, Kediel; Greenstadt, Rachel (July 2018, Privacy Enhancing Technologies Symposium)

   Previous studies, both in psychology and linguistics, have shown that individuals with mental illnesses show deviations from normal language use, that these differences can be used to make predictions, and used as a diagnostic tool. Recent studies have shown that machine learning can be used to predict people with mental illnesses based on their writing. However, little attention is paid to the interpretability of the machine learning models. In this talk we will describe our analysis of the machine learning models, the different language patterns that distinguish individuals having mental illnesses from a control group, and the associated privacy concerns. We use a dataset of Tweets that are collected from users who reported a diagnosis of a mental illnesses on Twitter. Given the self-reported nature of the dataset, it is possible that some of these individuals are actively talking about their mental illness on social media. We investigated if the machine learning models are detecting the active mentions of the mental illness or if they are detecting more complex language patterns. We then conducted a feature analysis by creating feature vectors using word unigrams, part of speech tags and word clusters and used feature importance measures and statistical methods to identify important features. This analysis serves two purposes: to understand the machine learning model, and to discover language patterns that would help in identifying people with mental illnesses. Finally, we conducted a qualitative analysis of the misclassifications to understand the potential causes for the misclassifications. 

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