Detecting Depression in Social Media : An Emotional Analysis Approach

Depression has been an ongoing mental health issue that has been affecting a wide range of humanity, particularly the young adults. To address and observe the more general public in a natural habitat, social media is examined for constructing a system to accurately detect depression. Despite the assiduous effort to construct a novel mechanism to detect depression from social media, behavioral approaches had underlying problems for users with a short activity span. To address this problem, emotion analysis was used as a tool to extract the emotion(s) of a user’s post to identify those with depression. Via machine learning techniques to construct an emotion classifier which in turn creates emotion embeddings for a binary classifier, this study proposes a pipeline structure to identify reddit posts from the depression subreddit. The model yielded promising results, introducing emotional analysis as a novel methodology in assessing mental health within social media

A Study of User Behaviors and Activities on Online Mental Health Communities

abstract: Social media is a medium that contains rich information which has been shared by many users every second every day. This information can be utilized for various outcomes such as understanding user behaviors, learning the effect of social media on a community, and developing a decision-making system based on the information available. With the growing popularity of social networking sites, people can freely express their opinions and feelings which results in a tremendous amount of user-generated data. The rich amount of social media data has opened the path for researchers to study and understand the users’ behaviors and mental health conditions. Several studies have shown that social media provides a means to capture an individual state of mind. Given the social media data and related work in this field, this work studies the scope of users’ discussion among online mental health communities. In the first part of this dissertation, this work focuses on the role of social media on mental health among sexual abuse community. It employs natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; how online response differs from traditional response found in a physical world. To answer these questions, this work extracts Reddit posts on rape to understand the nature of user responses for this stigmatized topic. In the second part of this dissertation, this work expands to a broader range of online communities. In particular, it investigates the potential roles of social media on mental health among five major communities, i.e., trauma and abuse community, psychosis and anxiety community, compulsive disorders community, coping and therapy community, and mood disorders community. This work studies how people interact with each other in each of these communities and what these online forums provide a resource to users who seek help. To understand users’ behaviors, this work extracts Reddit posts on 52 related subcommunities and analyzes the linguistic behavior of each community. Experiments in this dissertation show that Reddit is a good medium for users with mental health issues to find related helpful resources. Another interesting observation is an interesting topic cluster from users’ posts which shows that discussion and communication among users help individuals to find proper resources for their problem. Moreover, results show that the anonymity of users in Reddit allows them to have discussions about different topics beyond social support such as financial and religious support.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Detecting Addiction, Anxiety, and Depression by Users Psychometric Profiles

Detecting and characterizing people with mental disorders is an important task that could help the work of different healthcare professionals. Sometimes, a diagnosis for specific mental disorders requires a long time, possibly causing problems because being diagnosed can give access to support groups, treatment programs, and medications that might help the patients. In this paper, we study the problem of exploiting supervised learning approaches, based on users' psychometric profiles extracted from Reddit posts, to detect users dealing with Addiction, Anxiety, and Depression disorders. The empirical evaluation shows an excellent predictive power of the psychometric profile and that features capturing the post's content are more effective for the classification task than features describing the user writing style. We achieve an accuracy of 96% using the entire psychometric profile and an accuracy of 95% when we exclude from the user profile linguistic features

Climate Change Frames and Emotional Responses on Reddit

Climate change remains a highly polarized topic in the United States. Research suggests that the divide in climate change beliefs is partly a result of news media’s representation of select aspects of the problem, or framing. Frames influence individuals’ attitudes, emotions, and behaviors towards climate change. Overwhelming representation of certain climate change frames has led to a lack of emotional connection to the issue, resulting in inaction or dismissal. Climate change researchers have investigated the presence and effects of frames on both news media and select social media sites, particularly Twitter. However, little research has investigated the climate change conversation on other social media sites, such as Reddit. Reddit is a community-based social media site whose users represent a unique demographic in the United States. Reddit users rely heavily on Reddit for news and are highly engaged with the site. Unlike Twitter, Reddit does not have a small character limit on posts, allowing for longer conversation and a potential for greater peer influence. Using both human coders and computer-aided textual analysis, this thesis investigated which climate change frames are the most popular on Reddit and which emotions appear most frequently in the discussion sections of those posts. This study sampled posts from six subreddits that represent a range of climate change stances. The data found that political/ideological struggle was the most common frame and that anger was the most expressed emotion. Further results and implications are discussed

I Can’t Get No (Need) Satisfaction: Applying Basic Psychological Needs Theory to Foster Human Connection and Improve Applicant Reactions in Asynchronous Video Interviews.

This research addresses the importance of making job applicants feel valued and respected, and tries to find ways to humanize high-stakes interactions in the digital age. In particular, this research experiments with designing one-way video interviews to increase applicants’ sense of connection to the hiring organization. The results underscore the need for organizations to develop these assessments with humane technology principles in mind, in order to foster positive reactions from applicants and secure top talent

Towards Using Word Embedding Vector Space for Better Cohort Analysis

On websites like Reddit, users join communities where they discuss specific topics which cluster them into possible cohorts. The authors within these cohorts have the opportunity to post more openly under the blanket of anonymity, and such openness provides a more accurate signal on the real issues individuals are facing. Some communities contain discussions about mental health struggles such as depression and suicidal ideation. To better understand and analyse these individuals, we propose to exploit properties of word embeddings that group related concepts close to each other in the embeddings space. For the posts from each topically situated sub-community, we build a word embeddings model and use handcrafted lexicons to identify emotions, values and psycholinguistically relevant concepts. We then extract insights into ways users perceive these concepts by measuring distances between them and references made by users either to themselves, others or other things around them. We show how our proposed approach can extract meaningful signals that go beyond the kinds of analyses performed at the individual word level

Social Media and Electronics’ Impacts on Psychological Well-being

The use of electronics, specifically social media, has grown immensely in recent years along with the increase of mental illness, thus implying a possible correlation between the two. The use of digital media may have serious impacts on the psychological well-being of adolescents, teenagers, and adults. This review will focus on the specific psychological impacts that electronics and social media have on adolescents, along with the contributions social media has to the development of depression in all ages, and how language use can be accurate predictors of common mental conditions. It is unclear why depression, anxiety, bipolar disorder, sleep disorders and Attention-Deficit/Hyperactivity Disorder (ADHD) are becoming so prevalent. The underlying causes of these conditions must be discovered, and prevention techniques must be implemented for future generations