Identification of Online Users' Social Status via Mining User-Generated Data

With the burst of available online user-generated data, identifying online users’ social status via mining user-generated data can play a significant role in many commercial applications, research and policy-making in many domains. Social status refers to the position of a person in relation to others within a society, which is an abstract concept. The actual definition of social status is specific in terms of specific measure indicator. For example, opinion leadership measures individual social status in terms of influence and expertise in an online society, while socioeconomic status characterizes personal real-life social status based on social and economic factors. Compared with traditional survey method which is time-consuming, expensive and sometimes difficult, some efforts have been made to identify specific social status of users based on specific user-generated data using classic machine learning methods. However, in fact, regarding specific social status identification based on specific user-generated data, the specific case has several specific challenges. However, classic machine learning methods in existing works fail to address these challenges, which lead to low identification accuracy. Given the importance of improving identification accuracy, this thesis studies three specific cases on identification of online and offline social status. For each work, this thesis proposes novel effective identification method to address the specific challenges for improving accuracy. The first work aims at identifying users’ online social status in terms of topic-sensitive influence and knowledge authority in social community question answering sites, namely identifying topical opinion leaders who are both influential and expert. Social community question answering (SCQA) site, an innovative community question answering platform, not only offers traditional question answering (QA) services but also integrates an online social network where users can follow each other. Identifying topical opinion leaders in SCQA has become an important research area due to the significant role of topical opinion leaders. However, most previous related work either focus on using knowledge expertise to find experts for improving the quality of answers, or aim at measuring user influence to identify influential ones. In order to identify the true topical opinion leaders, we propose a topical opinion leader identification framework called QALeaderRank which takes account of both topic-sensitive influence and topical knowledge expertise. In the proposed framework, to measure the topic-sensitive influence of each user, we design a novel influence measure algorithm that exploits both the social and QA features of SCQA, taking into account social network structure, topical similarity and knowledge authority. In addition, we propose three topic-relevant metrics to infer the topical expertise of each user. The extensive experiments along with an online user study show that the proposed QALeaderRank achieves significant improvement compared with the state-of-the-art methods. Furthermore, we analyze the topic interest change behaviors of users over time and examine the predictability of user topic interest through experiments. The second work focuses on predicting individual socioeconomic status from mobile phone data. Socioeconomic Status (SES) is an important social and economic aspect widely concerned. Assessing individual SES can assist related organizations in making a variety of policy decisions. Traditional approach suffers from the extremely high cost in collecting large-scale SES-related survey data. With the ubiquity of smart phones, mobile phone data has become a novel data source for predicting individual SES with low cost. However, the task of predicting individual SES on mobile phone data also proposes some new challenges, including sparse individual records, scarce explicit relationships and limited labeled samples, unconcerned in prior work restricted to regional or household-oriented SES prediction. To address these issues, we propose a semi-supervised Hypergraph based Factor Graph Model (HyperFGM) for individual SES prediction. HyperFGM is able to efficiently capture the associations between SES and individual mobile phone records to handle the individual record sparsity. For the scarce explicit relationships, HyperFGM models implicit high-order relationships among users on the hypergraph structure. Besides, HyperFGM explores the limited labeled data and unlabeled data in a semi-supervised way. Experimental results show that HyperFGM greatly outperforms the baseline methods on individual SES prediction with using a set of anonymized real mobile phone data. The third work is to predict social media users’ socioeconomic status based on their social media content, which is useful for related organizations and companies in a range of applications, such as economic and social policy-making. Previous work leverage manually defined textual features and platform-based user level attributes from social media content and feed them into a machine learning based classifier for SES prediction. However, they ignore some important information of social media content, containing the order and the hierarchical structure of social media text as well as the relationships among user level attributes. To this end, we propose a novel coupled social media content representation model for individual SES prediction, which not only utilizes a hierarchical neural network to incorporate the order and the hierarchical structure of social media text but also employs a coupled attribute representation method to take into account intra-coupled and inter-coupled interaction relationships among user level attributes. The experimental results show that the proposed model significantly outperforms other stat-of-the-art models on a real dataset, which validate the efficiency and robustness of the proposed model

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Analyzing the Relationship between Pastoral Leadership and Church Attendance in Baptist Congregations in Eastman, Georgia

The reality of the decline of the church is evident in many churches today. What is causing the decline in worship attendance? This research study examined 14 of the 42 Baptist churches in Eastman, Georgia, from the Dodge County Baptist Association, where the average weekly reporting worship attendance during 2018 to 2021 was 50 to 150 in attendance. This research used the survey method, where participants anonymously answered questions regarding their church and pastor. The research study\u27s results objectively prove that pastoral leadership can affect church attendance. Further research can be completed to investigate this connection between pastoral leadership and church attendance

Strategies for a Successful PhD Program: Words of Wisdom From the \u3cem\u3eWJNR\u3c/em\u3e Editorial Board

Nursing doctoral programs prepare students for research-focused careers within academic settings. The purpose of this Editorial Board Special Article is to provide PhD students and advisors with suggestions for making the most of their doctoral experience. Editorial Board members provide their individual insights on the skills and attributes students must acquire during the course of their doctoral education in order to succeed. The authors provide practical tips and advice on how to excel in a PhD program, including how to select an advisor and a dissertation committee, the importance of attending conferences to increase visibility and develop a network of colleagues, presenting and publishing research while still a student, and balancing work and personal life. Students who take full advantage of the opportunities available to them during the course of their doctoral programs will graduate well prepared to take on the multiple responsibilities of research, teaching, and leadership

Teens Acting Against Violence (TAAV) Program Evaluation

Teens Acting Against Violence (TAAV) is a violence prevention and youth empowerment program at the Tundra Women’s Coalition (TWC) for teenagers living in Bethel, Alaska. Participation is voluntary and open for any interested teens aged 12-18. TWC and TAAV partnered with the University of Alaska Anchorage (UAA) Justice Center to conduct an evaluation of the TAAV program through a one-time survey of former and current adult members (over 18 years of age) of TAAV. Pursuant to TAAV objectives, the focus of the evaluation was placed on examining efforts in the areas of domestic violence and sexual assault prevention, building healthy relationships, encouraging sobriety, and suicide prevention.Tundra Women’s CoalitionTable of Contents / Acknowledgments / Executive Summary / Section I. Introduction and Background / Section II. Methodology / Section III. Program Satisfaction / Section IV. TAAV Staff / Section V. Cultural Considerations / Section VI. TAAV Activities / Section VII. TAAV Impacts / Section VIII. Life Skills / Section IX. Self-perceptions / Section X. Interpersonal Relationships / Section XI. Bystander Intervention / Section XII. High-risk Behaviors / Section XIII. Member Feedback / Section XIV. Conclusion and Recommendations / Appendix A: TAAV Survey / Appendix B: List of Survey Resources / Appendix C: Data Table

Leveraging Mixed Expertise in Crowdsourcing.

Crowdsourcing systems promise to leverage the "wisdom of crowds" to help solve many kinds of problems that are difficult to solve using only computers. Although a crowd of people inherently represents a diversity of skill levels, knowledge, and opinions, crowdsourcing system designers typically view this diversity as noise and effectively cancel it out by aggregating responses. However, we believe that by embracing crowd workers' diverse expertise levels, system designers can better leverage that knowledge to increase the wisdom of crowds. In this thesis, we propose solutions to a limitation of current crowdsourcing approaches: not accounting for a range of expertise levels in the crowd. The current body of work in crowdsourcing does not systematically examine this, suggesting that researchers may not believe the benefits of using mixed expertise warrants the complexities of supporting it. This thesis presents two systems, Escalier and Kurator, to show that leveraging mixed expertise is a worthwhile endeavor because it materially benefits system performance, at scale, for various types of problems. We also demonstrate an effective technique, called expertise layering, to incorporate mixed expertise into crowdsourcing systems. Finally, we show that leveraging mixed expertise enables researchers to use crowdsourcing to address new types of problems.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/133307/1/afdavid_1.pd

Questioning Prime Ministers: Procedures, Practices and Functions in Parliamentary Democracies

This thesis investigates parliamentary oral questioning mechanisms that involve prime ministers in parliamentary democracies. Considering the fact that prime ministers are powerful and visible actors in parliamentary democracies, and that accountability is a key component of democratic politics, it maps the mechanisms through which parliamentarians may question prime ministers in different countries, and explores the extent to which these mechanisms contribute to accountability, and the extent to which they perform other functions. The first research component is a survey of procedural rules regarding mechanisms through which parliamentarians may question prime ministers in 31 parliamentary democracies. It draws on an indepth examination of parliamentary rules of procedure, followed by a consultation with practitioners and officials in each country to uncover aspects of convention and practice. Subsequently, questioning mechanisms are classified based on dimensions such as their collective or individualised nature, the extent to which procedures allow more open or closed participation, as well as the degree of questioning exposure to which prime ministers are subjected. It then discusses how these dimensions might affect the practice of questioning. Drawing on these classifications, the second research component investigates the practice of questioning prime ministers in four countries: two using collective questioning mechanisms, where prime ministers are questioned together with ministers (Question Period in Canada, Question Time in Australia); and two using individualised mechanisms, where prime ministers are questioned alone (Prime Minister’s Questions in the UK, Oral Questions to the Taoiseach in Ireland). This second component relies on quantitative and qualitative content analysis of transcripts of parliamentary debates for each case study country. Departing from the assumption that parliamentary questioning mechanisms are designed to facilitate accountability, it investigates the degree to which they do so, and the degree to which they perform other functions, such as facilitating the expression of conflict, support, or territorial representation

Rural Teachers’ Perceptions Of School Principals’ Leadership Behaviors Affecting Motivation To Improve Professional Practice.

School principals and teachers being a powerful force of social change is a well-established argument. While literature confirms the substantial impact school leaders and teachers have on improving organizations and student outcomes, there is a dearth of granular knowledge related to how rural school principals in China influence teachers\u27 motivation to improve professional practice. Thus, by engaging in a qualitative study leveraging the Interpretative Phenomenological Approach (IPA) this study aimed to illuminate the principals\u27 behaviors that teachers perceived as having significant impact on their motivation to improve practice. As part of its conceptual framework, the study incorporated a theoretical framework that combined the Behavioral Theory of Leadership with Social Contagion Theory. Seven participants from various rural schools in mainland China participated in the study and in-depth semi-structured interviews were conducted in two rounds over a two-month timescale in the Fall of 2020. The findings revealed that rural school leaders’ behaviors most germane to teachers’ motivation to improve professional practice were genuine care and concern for teachers’ well-being, accessibility and tempered friendliness, consequential dialogical discourse, articulated communication of school-based expectations and initiatives, avoidance of dogmatic micromanagement on classroom-based matters, perceptible consistency, and appreciable predictability. Recommendations for further study center on future longitudinal studies aimed at investigating the observed phenomenon over time and in different settings and a deeper investigation into the nature of principal friendliness to ascertain degrees to which teachers deem it to be appropriate

Behavioral risk factors for suicide among adolescent schoolchildren

The studies devoted to suicide risk factors are of importance because they define the transition from intent and conflict to the realization of the intention in the form of a suicidal act. In this study, three groups of people undergo a survey on the behavioral factors for suicide risk and findings are presented alongside interpretation. The survey shows that the suicidal situation is considered the most serious by the third group (the adolescents), as evidenced by the absence of low scores among the given suicidal factors. At the same time, respondents in all three groups believe that drugs and substance abuse have the greatest influence on the formation of suicidal behavior in adolescents. Thus, the suicidal situation among the adolescent population is unfavorable and requires the adoption of urgent measures to improve it. The study provides recommendations for reducing behavioral risk factors for suicide among adolescent schoolchildren