Roger J. Booth

Bio: Roger J. Booth is an academic researcher from University of Auckland. The author has contributed to research in topics: Antigen & Randomized controlled trial. The author has an hindex of 32, co-authored 79 publications receiving 6380 citations.

Disclosure of trauma and immune response to a hepatitis B vaccination program.

Abstract: This study investigated whether emotional expression of traumatic experiences influenced the immune response to a hepatitis B vaccination program. Forty medical students who tested negative for hepatitis B antibodies were randomly assigned to write about personal traumatic events or control topics during 4 consecutive daily sessions. The day after completion of the writing, participants were given their first hepatitis B vaccination, with booster injections at 1 and 4 months after the writing. Blood was collected before each vaccination and at a 6-month follow-up. Compared with the control group, participants in the emotional expression group showed significantly higher antibody levels against hepatitis B at the 4 and 6-month follow-up periods. Other immune changes evident immediately after writing were significantly lower numbers of circulating T helper lymphocytes and basophils in the treatment group. The finding that a writing intervention influences immune response provides further support for a link between emotional disclosure and health.

The immunological effects of thought suppression.

Abstract: Individuals often suppress emotional thoughts, particularly thoughts that arouse negative emotions, as a way of regulating mood and reducing distress. However, recent work has highlighted the complexities and unexpected cognitive and physiological effects of thought suppression. In a study designed to examine the short-term immunological effects of thought suppression, participants wrote about either emotional or nonemotional topics with or without thought suppression. Blood was drawn before and after each experimental session on 3 consecutive days. Results showed a significant increase in circulating total lymphocytes and CD4 (helper) T lymphocyte levels in the emotional writing groups. Thought suppression resulted in a significant decrease in CD3 T lymphocyte levels. The implications of the results for the role of the expression and suppression of emotion in health are discussed.

Effect of written emotional expression on immune function in patients with human immunodeficiency virus infection: a randomized trial.

Abstract: Objectives: To determine whether writing about emotional topics compared with writing about neutral topics could affect CD4 lymphocyte count and human immunodeficiency virus (HIV) viral load among HIV-infected patients Methods: Thirty-seven HIV-infected patients were randomly allocated to 2 writing conditions focusing on emotional or control topics Participants wrote for 4 days, 30 minutes per day The CD4 lymphocyte count and HIV viral load were measured at baseline and at 2 weeks, 3 months, and 6 months after writing Results: The emotional writing participants rated their essays as more personal, valuable, and emotional than those in the control condition Relative to the drop in HIV viral load, CD4 lymphocyte counts increased after the intervention for participants in the emotional writing condition compared with control writing participants Conclusions: The results are consistent with those of previous studies using emotional writing in other patient groups Based on the self-reports of the value of writing and the preliminary laboratory findings, the results suggest that emotional writing may provide benefit for patients with HIV infection Key words: HIV infection, disclosure, emotional writing, HIV viral load, CD4 lymphocyte count HIV human immunodeficiency virus; AIDS acquired immune deficiency syndrome; ANOVA analysis of variance

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Prejudice, social stress, and mental health in lesbian, gay, and bisexual populations: conceptual issues and research evidence

Abstract: In this article the author reviews research evidence on the prevalence of mental disorders in lesbians, gay men, and bisexuals (LGBs) and shows, using meta-analyses, that LGBs have a higher prevalence of mental disorders than heterosexuals. The author offers a conceptual framework for understanding this excess in prevalence of disorder in terms of minority stress— explaining that stigma, prejudice, and discrimination create a hostile and stressful social environment that causes mental health problems. The model describes stress processes, including the experience of prejudice events, expectations of rejection, hiding and concealing, internalized homophobia, and ameliorative coping processes. This conceptual framework is the basis for the review of research evidence, suggestions for future research directions, and exploration of public policy implications. The study of mental health of lesbian, gay, and bisexual (LGB) populations has been complicated by the debate on the classification of homosexuality as a mental disorder during the 1960s and early 1970s. That debate posited a gay-affirmative perspective, which sought to declassify homosexuality, against a conservative perspective, which sought to retain the classification of homosexuality as a mental disorder (Bayer, 1981). Although the debate on classification ended in 1973 with the removal of homosexuality from the second edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association, 1973), its heritage has lasted. This heritage has tainted discussion on mental health of lesbians and gay men by associating— even equating— claims that LGB people have higher prevalences of mental disorders than heterosexual people with the historical antigay stance and the stigmatization of LGB persons (Bailey, 1999). However, a fresh look at the issues should make it clear that whether LGB populations have higher prevalences of mental disorders is unrelated to the classification of homosexuality as a mental disorder. A retrospective analysis would suggest that the attempt to find a scientific answer in that debate rested on flawed logic. The debated scientific question was, Is homosexuality a mental disorder? The operationalized research question that pervaded the debate was, Do homosexuals have high prevalences of mental disorders? But the research did not accurately operationalize the scientific question. The question of whether homosexuality should be considered a mental disorder is a question about classification. It can be answered by debating which behaviors, cognitions, or emotions should be considered indicators of a mental

The Benefits of Frequent Positive Affect: Does Happiness Lead to Success?

Abstract: Numerous studies show that happy individuals are successful across multiple life domains, including marriage, friendship, income, work performance, and health. The authors suggest a conceptual model to account for these findings, arguing that the happiness-success link exists not only because success makes people happy, but also because positive affect engenders success. Three classes of evidence--crosssectional, longitudinal, and experimental--are documented to test their model. Relevant studies are described and their effect sizes combined meta-analytically. The results reveal that happiness is associated with and precedes numerous successful outcomes, as well as behaviors paralleling success. Furthermore, the evidence suggests that positive affect--the hallmark of well-being--may be the cause of many of the desirable characteristics, resources, and successes correlated with happiness. Limitations, empirical issues, and important future research questions are discussed.

Sentiment Analysis and Opinion Mining

Abstract: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

The psychological meaning of words: LIWC and computerized text analysis methods

Abstract: We are in the midst of a technological revolution whereby, for the first time, researchers can link daily word use to a broad array of real-world behaviors. This article reviews several computerized text analysis methods and describes how Linguistic Inquiry and Word Count (LIWC) was created and validated. LIWC is a transparent text analysis program that counts words in psychologically meaningful categories. Empirical results using LIWC demonstrate its ability to detect meaning in a wide variety of experimental settings, including to show attentional focus, emotionality, social relationships, thinking styles, and individual differences.