What people study when they study Tumblr:Classifying Tumblr-related academic research

Purpose: Since its launch in 2007, research has been carried out on the popular social networking website Tumblr. This paper identifies published Tumblr based research, classifies it to understand approaches and methods, and provides methodological recommendations for others. / Design/methodology/approach: Research regarding Tumblr was identified. Following a review of the literature, a classification scheme was adapted and applied, to understand research focus. Papers were quantitatively classified using open coded content analysis of method, subject, approach, and topic. / Findings: The majority of published work relating to Tumblr concentrates on conceptual issues, followed by aspects of the messages sent. This has evolved over time. Perceived benefits are the platform’s long-form text posts, ability to track tags, and the multimodal nature of the platform. Severe research limitations are caused by the lack of demographic, geo-spatial, and temporal metadata attached to individual posts, the limited API, restricted access to data, and the large amounts of ephemeral posts on the site. / Research limitations/implications: This study focuses on Tumblr: the applicability of the approach to other media is not considered. We focus on published research and conference papers: there will be book content which was not found using our method. Tumblr as a platform has falling user numbers which may be of concern to researchers. / Practical implications: We identify practical barriers to research on the Tumblr platform including lack of metadata and access to big data, explaining why Tumblr is not as popular as Twitter in academic studies. - Social implications This paper highlights the breadth of topics covered by social media researchers, which allows us to understand popular online platforms. / Originality/value: There has not yet been an overarching study to look at the methods and purpose of those who study Tumblr. We identify Tumblr related research papers from the first appearing in July 2011 until July 2015. Our classification derived here provides a framework that can be used to analyse social media research, and in which to position Tumblr related work, with recommendations on benefits and limitations of the platform for researchers

Language-based personality:a new approach to personality in a digital world

Personality is typically defined as the consistent set of traits, attitudes, emotions, and behaviors that people have. For several decades, a majority of researchers have tacitly agreed that the gold standard for measuring personality was with self-report questionnaires. Surveys are fast, inexpensive, and display beautiful psychometric properties. A considerable problem with this method, however, is that self-reports reflect only one aspect of personality — people's explicit theories of what they think they are like. We propose a complementary model that draws on a big data solution: the analysis of the words people use. Language use is relatively reliable over time, internally consistent, and differs considerably between people. Language-based measures of personality can be useful for capturing/modeling lower-level personality processes that are more closely associated with important objective behavioral outcomes than traditional personality measures. Additionally, the increasing availability of language data and advances in both statistical methods and technological power are rapidly creating new opportunities for the study of personality at ‘big data’ scale. Such opportunities allow researchers to not only better understand the fundamental nature of personality, but at a scale never before imagined in psychological research

Computers (Basel)

Suicide is a leading cause of death and a global public health problem, representing more than one in every 100 deaths in 2019. Modeling and Simulation (M&S) is widely used to address public health problems, and numerous simulation models have investigated the complex, dependent, and dynamic risk factors contributing to suicide. However, no review has been dedicated to these models, which prevents modelers from effectively learning from each other and raises the risk of redundant efforts. To guide the development of future models, in this paper we perform the first scoping review of simulation models for suicide prevention. Examining ten articles, we focus on three practical questions. First, which interventions are supported by previous models? We found that four groups of models collectively support 53 interventions. We examined these interventions through the lens of global recommendations for suicide prevention, highlighting future areas for model development. Second, what are the obstacles preventing model application? We noted the absence of cost effectiveness in all models reviewed, meaning that certain simulated interventions may be infeasible. Moreover, we found that most models do not account for different effects of suicide prevention interventions across demographic groups. Third, how much confidence can we place in the models? We evaluated models according to four best practices for simulation, leading to nuanced findings that, despite their current limitations, the current simulation models are powerful tools for understanding the complexity of suicide and evaluating suicide prevention interventions.CC999999/ImCDC/Intramural CDC HHSUnited States

Mediating role of attitude, behavioral control, and stakeholders' support on the relationship between entrepreneurial skills and entrepreneurial intentions of it employees in Pakistan

Pakistan being a developing country has a high rate of unemployment among young population. This present study examines the effects of entrepreneurial skills on developing entrepreneurial intentions of IT employee of Punjab, Pakistan. The study also examines the mediating role of attitude towards behavior, perceived behavioral control, and stakeholders’ support system in the relationship between entrepreneurial skills and entrepreneurial intentions. The probing into the literature of concepts and conceptualizations of the theories permitted a theoretical framework that identified the research issues and the research gap. The data were collected from IT employees working with SECP registered companies in Punjab, Pakistan using a cross-sectional study design. The study used simple random sampling technique to the selected 398 employees working with Information Technology (IT) companies in Punjab, Pakistan. For the initial data screening SPSS 20 was used, and then the Partial Least Squares Structural Equation Modelling (PLS-SEM) was employed to test the present study hypotheses. This study found the significant mediating effects of attitude towards behavior, perceived behavioral control, and stakeholders’ support system on the relationship between EPS, LS, MS, PMS, and TS and entrepreneurial intentions of IT employees in Punjab, Pakistan. The findings of this study further reveal that entrepreneurial intentions depend on the degree of EPS, LS, MS, PMS, and TS. The results of this study provide important insights to the policy making institutions, government, and the researchers to further understand the effects of entrepreneurial skills on developing entrepreneurial intentions and mediating role of attitude towards behavior, perceived behavioral control, and stakeholders’ support system. The findings of this research extended to the body of knowledge on entrepreneurial skills and entrepreneurial intentions in Pakistani context

The relationships between brand attributes and word of mouth on brand identity and brand image

Companies all around the world have to deal with issues relating to brand image development and maintenance because brand image can affect their brand performance. Based on the attribution theory, this research examined the impact of the components of brand attributes, namely brand relevance, brand consistency, brand sustainability, brand credibility, brand uniqueness and word of mouth (WOM) of brand image. This study also evaluated the mediating influence of brand WOM identity on the relationship between the brand attribute components, WOM and brand image. Insufficient empirical attention, particularly in relation to the attribution theory, was the driving force for the current study to be undertaken. Two hundred and fifty-four travellers via two airports located in the northern region of Malaysia participated in this study. A cross-sectional survey approach and the quota sampling technique were adopted to select the participants, and PLS algorithm and bootstrapping techniques were deployed to test the hypothesized relationships. The PLS path modelling reported significant results of the major hypotheses; brand sustainability was the only variable not significantly related to brand image. It was found that brand identity mediated significantly the relationship between brand attributes, WOM and brand image. Overall, the results provide support for the attribution theory in that brand attributes, namely brand relevance, brand consistency, brand sustainability, brand credibility, brand uniqueness and word of mouth can help shape consumers' perceptions which ultimately result in harnessing brand image. Finally, the study's implications for theory and practice, limitations, conclusions as well as directions for future research are provided and discussed

Social Media Analytics of Smoking Cessation Intervention: User Behavior Analysis, Classification, and Prediction

Tobacco use causes a large number of diseases and deaths in the United States. Traditional intervention programs are based on face-to-face consulting, and social support is offered to help smoking quitters control stress and achieve better intervention outcomes. However, the scalability of these traditional intervention programs is limited by time and location. With the development of Web 2.0, many intervention programs of smoking cessation are developed online to reach a wider population. QuitNet is a popular website for smoking cessation that provides different services to help users quit smoking. It builds communities on different social media for people to discuss issues of smoking cessation and provide social support for each other. In this dissertation, we develop a comprehensive study to understand user behavior and their discussion interactions in online communities of smoking cessation. We compare user features and behaviors on different social media channels, analyze user interactions from the perspective of social support exchange, and apply data mining techniques to analyze discussion content and recommend threads for users. Health communities are developed on different types of social media. For example, QuitNet has Web forums on its own Web site while it also has its appearance on Facebook. The user participation may vary on different social media platforms. Users may also behave differently depending on the functions and design of the social media platforms. So, as the first step in this dissertation, we carry out a preliminary study to compare smoking cessation communities on different social media channels. We analyze user characteristics and behaviors in QuitNet Forum and QuitNet Facebook with statistical analysis and social network analysis. It is found that most users of QuitNet Forum are early smoking quitters, and they participate in discussions more actively than users of QuitNet Facebook. However, users of QuitNet Facebook have a wider spectrum of quitting statuses and interaction behaviors. Second, we are interested in user behaviors and how they exchange social support in online communities. Social support is "an exchange of resources between two individuals perceived by the provider or the recipient to be intended to enhance the well-being of the recipient". As QuitNet Forum attracts much more active users than QuitNet Facebook, it provides a better platform for our research purpose. So, we focus on QuitNet Forum, developing a classification scheme through qualitative analysis to categorize discussion topics and types of social support on the forum. Patterns of user behaviors are defined and identified. Social networks are built to analyze user interactions of social support exchange. It is found that users at different quit stages have different behaviors to exchange social support, and different types of social support flow between users at different quit stages. Discussion topics, user behaviors and patterns of social support exchanges are thoroughly analyzed. However, due to a huge amount of information on QuitNet Forum, it is difficult for users to find proper topics or peers to discuss or interact with. It would be helpful if we could apply machine learning techniques to understand user generated information in online health communities, and recommend discussion topics to users to participate in. We develop classifiers to categorize posts and comments on QuitNet Forum in terms of user intentions and social support types. User behaviors and patterns are used to help developing various feature sets. Then, we develop recommendation techniques to recommend threads for users to participate in. Based on traditional Collaborative Filtering and content-based approaches, we integrate classification results and user quit stages to develop recommendation systems. The experiments show that integrating classification results or user health statuses can achieve the best recommendation results with different percentages of unknown data. In this dissertation, we implement all-sided studies for online smoking cessation communities, including comprehensive analytics and applications. The proposed frameworks and approaches could be applied to other health communities. In the future, we will apply more analytics and techniques to a larger data set, and develop user-end applications to serve and improve online health intervention programs and communities.Ph.D., Computer Science -- Drexel University, 201

Applied Cognitive Sciences

Cognitive science is an interdisciplinary field in the study of the mind and intelligence. The term cognition refers to a variety of mental processes, including perception, problem solving, learning, decision making, language use, and emotional experience. The basis of the cognitive sciences is the contribution of philosophy and computing to the study of cognition. Computing is very important in the study of cognition because computer-aided research helps to develop mental processes, and computers are used to test scientific hypotheses about mental organization and functioning. This book provides a platform for reviewing these disciplines and presenting cognitive research as a separate discipline

Towards a Sustainable Life: Smart and Green Design in Buildings and Community

This Special Issue includes contributions about occupants’ sustainable living in buildings and communities, highlighting issues surrounding the sustainable development of our environments and lives by emphasizing smart and green design perspectives. This Special Issue specifically focuses on research and case studies that develop promising methods for the sustainable development of our environment and identify factors critical to the application of a sustainable paradigm for quality of life from a user-oriented perspective. After a rigorous review of the submissions by experts, fourteen articles concerning sustainable living and development are published in this Special Issue, written by authors sharing their expertise and approaches to the concept and application of sustainability in their fields. The fourteen contributions to this special issue can be categorized into four groups, depending on the issues that they address. All the proposed methods, models, and applications in these studies contribute to the current understanding of the adoption of the sustainability paradigm and are likely to inspire further research addressing the challenges of constructing sustainable buildings and communities resulting in a sustainable life for all of society

An interoperable electronic medical record-based platform for personalized predictive analytics

Indiana University-Purdue University Indianapolis (IUPUI)Precision medicine refers to the delivering of customized treatment to patients based on their individual characteristics, and aims to reduce adverse events, improve diagnostic methods, and enhance the efficacy of therapies. Among efforts to achieve the goals of precision medicine, researchers have used observational data for developing predictive modeling to best predict health outcomes according to patients’ variables. Although numerous predictive models have been reported in the literature, not all models present high prediction power, and as the result, not all models may reach clinical settings to help healthcare professionals make clinical decisions at the point-of-care. The lack of generalizability stems from the fact that no comprehensive medical data repository exists that has the information of all patients in the target population. Even if the patients’ records were available from other sources, the datasets may need further processing prior to data analysis due to differences in the structure of databases and the coding systems used to record concepts. This project intends to fill the gap by introducing an interoperable solution that receives patient electronic health records via Health Level Seven (HL7) messaging standard from other data sources, transforms the records to observational medical outcomes partnership (OMOP) common data model (CDM) for population health research, and applies predictive models on patient data to make predictions about health outcomes. This project comprises of three studies. The first study introduces CCD-TOOMOP parser, and evaluates OMOP CDM to accommodate patient data transferred by HL7 consolidated continuity of care documents (CCDs). The second study explores how to adopt predictive model markup language (PMML) for standardizing dissemination of OMOP-based predictive models. Finally, the third study introduces Personalized Health Risk Scoring Tool (PHRST), a pilot, interoperable OMOP-based model scoring tool that processes the embedded models and generates risk scores in a real-time manner. The final product addresses objectives of precision medicine, and has the potentials to not only be employed at the point-of-care to deliver individualized treatment to patients, but also can contribute to health outcome research by easing collecting clinical outcomes across diverse medical centers independent of system specifications