Online social capital : mood, topical and psycholinguistic analysis

Social media provides rich sources of personal information and community interaction which can be linked to aspect of mental health. In this paper we investigate manifest properties of textual messages, including latent topics, psycholinguistic features, and authors\u27 mood, of a large corpus of blog posts, to analyze the aspect of social capital in social media communities. Using data collected from Live Journal, we find that bloggers with lower social capital have fewer positive moods and more negative moods than those with higher social capital. It is also found that people with low social capital have more random mood swings over time than the people with high social capital. Significant differences are found between low and high social capital groups when characterized by a set of latent topics and psycholinguistic features derived from blogposts, suggesting discriminative features, proved to be useful for classification tasks. Good prediction is achieved when classifying among social capital groups using topic and linguistic features, with linguistic features are found to have greater predictive power than latent topics. The significance of our work lies in the importance of online social capital to potential construction of automatic healthcare monitoring systems. We further establish the link between mood and social capital in online communities, suggesting the foundation of new systems to monitor online mental well-being

Leveraging Twitter data to analyze the virality of Covid-19 tweets: a text mining approach

As the novel coronavirus spreads across the world, work, pleasure, entertainment, social interactions, and meetings have shifted online. The conversations on social media have spiked, and given the uncertainties and new policies, COVID-19 remains the trending topic on all such platforms, including Twitter. This research explores the factors that affect COVID-19 content-sharing by Twitter users. The analysis was conducted using 57,000 plus tweets that mentioned COVID-19 and related keywords. The tweets were subjected to the Natural Language Processing (NLP) techniques like Topic modelling, Named Entity-Relationship, Emotion & Sentiment analysis, and Linguistic feature extraction. These methods generated features that could help explain the retweet count of the tweets. The results indicate that tweets with named entities (person, organisation, and location), expression of negative emotions (anger, disgust, fear, and sadness), reference to mental health, optimistic content, and greater length have higher chances of being shared (retweeted). On the other hand, tweets with more hashtags and user mentions are less likely to be shared

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

Global Minds and Hearts Pathways Towards a Sustainable Future

This volume contains the program, abstracts, and links to the recordings of the keynotes of the 27th Regional Conference of IACCP, 2023.https://scholarworks.gvsu.edu/iaccp_regional/1000/thumbnail.jp

Predicting And Characterizing The Health Of Individuals And Communities Through Language Analysis Of Social Media

A large and growing fraction of the global population uses social media, through which users share their thoughts, feelings, and behaviors, predominantly through text. To quantify the expression of psychological constructs in language, psychology has evolved a set of “closed-vocabulary” methods using pre-determined dictionaries. Advances in natural language processing have made possible the development of “open-vocabulary” methods to analyze text in data-driven ways, and machine learning algorithms have substantially improved prediction performances. The first chapter introduces these methods, comparing traditional methods of text analysis with newer methods from natural language processing in terms of their relative ability to predict and elucidate the language correlates of age, gender and the personality of Facebook users (N = 65,896). The second and third chapters discuss the use of social media to predict depression in individuals (the most prevalent mental illness). The second chapter reviews the literature on detection of depression through social media and concludes that no study to date has yet demonstrated the efficacy of this approach to screen for clinician-reported depression. In the third chapter, Facebook data was collected and connected to patients’ medical records (N = 683), and prediction models based on Facebook data were able to forecast the occurrence of depression with fair accuracy–about as well as self-report screening surveys. The fourth chapter applies both sets of methods to geotagged Tweets to predict county-level mortality rates of atherosclerotic heart disease mortality (the leading cause of death in the U.S.) across 1,347 counties, capturing 88% of the U.S. population. In this study, a Twitter model outperformed a model combining ten other leading demographic, socioeconomic and health risk factors. Across both depression and heart disease, associated language profiles identified fine-grained psychological determinants (e.g., loneliness emerged as a risk factor for depression, and optimism showed a protective association with heart disease). In sum, these studies demonstrate that large-scale text analysis is a valuable tool for psychology with implications for public health, as it allows for the unobtrusive and cost-effective monitoring of disease risk and psychological states of individuals and large populations

Year of the Golden Jubilee: Culture Change in the Past, Present and Future

Part 1 of the IACCP Proceedings contains the abstracts and links to the recordings of the XXVI Congress of the International Association for Cross-Cultural Psychology, 2022. (c) 2023, International Association for Cross-Cultural Psychologyhttps://scholarworks.gvsu.edu/iaccp_proceedings/1011/thumbnail.jp

The Role of Attachment in Young Adults\u27 Use of Facebook for Coping

The Internet has become integrated into the daily lives of adolescents and young adults, and researchers have begun to investigate the predictors, correlates, and consequences of Internet use. Research has suggested that individuals with social strengths and individuals with social weaknesses both may benefit from using the Internet to cope. The purpose of this study was to explore the relations among attachment, offline coping, online coping, and adjustment, as well as to evaluate whether the rich-get-richer or social compensation hypotheses of Internet use explained these relations. Undergraduate students aged 17 to 25 years ( N = 296) completed online measures of their Internet and Facebook use, attachment anxiety and avoidance, offline coping, online coping through Facebook, well-being, and distress. Results showed that the relation between higher levels of attachment anxiety and greater distress was partially mediated by online coping. Attachment avoidance was not related to online coping, but the relation between higher levels of attachment avoidance and decreased well-being was partially mediated by less frequent use of adaptive offline coping strategies. An alternative model suggested a possible reciprocal path indicating that individuals higher in both distress and well-being reported greater frequency of online coping. Further analyses of online coping indicated that most subtypes were related to more intense usage of Facebook, greater attachment anxiety and avoidance, greater use of avoidant coping strategies offline, greater distress, and reduced well-being. These results suggested that the relations among attachment, offline coping, online coping, and psychosocial adjustment are more complex than can be explained by either the rich-get-richer or social compensation hypotheses. Implications of these findings for the development of pathological Internet use also are outlined

Themes and Participants’ Role in Online Health Discussion: Evidence From Reddit

Health-related topics are discussed widely on different social networking sites. These discussions and their related aspects can reveal significant insights and patterns that are worth studying and understanding. In this dissertation, we explore the patterns of mandatory and voluntary vaccine online discussions including the topics discussed, the words correlated with each of them, and the sentiment expressed. Moreover, we explore the role opinion leaders play in the health discussion and their impact on participation in a particular discussion. Opinion leaders are determined, and their impact on discussion participation is differentiated based on their different characteristics such as their connections and locations in the social network, their content, and their sentiment. We apply social network analysis, topic modeling, sentiment analysis, machine learning, econometric analysis, and other techniques to analyze the collected data from Reddit. The results of our analyses show that sentiment is an important factor in health discussion, and it varies between different types of discussions. In addition, we identified the main topics discussed for each vaccine. Furthermore, the results of our study found that global opinion leaders have more influence compared to local opinion leaders in elevating the health discussion. Our study has important theoretical and practical implications

Abstracts and Recorded Presentations

The abstracts are organized in the following way: All special events (keynotes, award presentations, meet the editor, pre-conference workshops, provocation sessions, etc.) are presented first. All other presentations are organized along the Thematic Streams in alphabetical order. Within each Thematic Stream, the order follows the structure: symposia, individual papers, and posters