Communicating Climate Change In Internet Discussion Fora: Processes and Implications

Communicating climate change issues in the Internet era requires new strategies that incorporate online communication. The rapid growth of new media and widespread use of the internet has marked everyday lifestyles in modern society. Information on a wide range of social issues, including climate change, is disseminated and debated through online discussions in internet fora. In this research, communication on internet fora and other potential forms of online social interaction are explored, to identify ways to enhance climate change communication on the Internet. The thesis raises three research questions to explore the communication context of internet fora discussion, namely: what are characteristics of the communication process on internet fora? Who is involved in the communication process? What influences do these online communication activities have on users’ everyday activities? The research applies a mixed-methods approach of analysing the usage of Internet fora and the contents of fora communication activities to explore these questions. This includes qualitative reviews of topic-thread discussions to reveal users’ roles in discussions, as well as surveys of fora users. It is argued that with increasing levels of interaction among communicators (people who post or reply to articles in order to express or respond ideas) on internet fora, these communicators are mobilised to join the online discussion process, competing for opinion leadership. The online discussions further contribute to the formation of opinions on climate change, as climate change and related issues are discussed The thesis thereby aims to contribute to the development of effective approaches for opinion formation and climate change communication online, and to encourage individuals to discuss changing behaviour patterns and public engagement of greenhouse gas reduction actions

Agent-Based Overlapping Generations Modeling for Educational Policy Analysis

Educational systems are complex adaptive systems (CAS). The macroeffects of an educational policy emerge from and depend on individual students\u27 reactions to the policy. However, educational policymakers traditionally rely on equation-based models, which are deficient in reflecting the work of microbehaviors. Using inappropriate tools to make policies may be a reason why there were many unintended educational consequences in history. A proper methodology to design and analyze policies for complex educational systems is agent-based modeling (ABM). Grounded in the theories of CAS and computational irreducibility, ABM is capable of connecting microbehaviors with macropatterns. The purpose of this study was to contribute to the application of ABM in educational policy analysis by constructing an agent-based overlapping generations model with hypothesized inputs to qualitatively represent the environment of the Taipei School District. Four research questions explored the effects of Taipei\u27s 2016 student-assignment mechanism and its free tuition policy on educational opportunity and school quality under different assumptions of students\u27 school-choice strategies. The simulated outputs were analyzed using descriptive statistics and paired samples t tests. The findings, which could hardly be revealed by traditional models, showed that the effects were complex and depended on students\u27 strategies along with the number of choices students were allowed to make; the assignment outcomes for elite students were robust to the mechanism, and the free tuition policy worsened school quality. Although exploratory, these findings can serve as hypotheses and a guide for Taipei\u27s policymakers to collect empirical data in evaluating their 2016 mechanism and tuition policy

A Multidisciplinary Study Of Antecedents To Voluntary Knowledge Contribution Within Online Forums

One challenge faced by online forums is the provision of a sustainable supply of contributions of knowledge (Wasco et al., 2009). Previous studies have identified online trust and perceived critical mass as antecedents of online knowledge contributions. However, the dynamic aspects of antecedents are little investigated. Moreover, how the dynamics together impact on members’ willingness to contribute knowledge is an open question to be further investigated. To examine the dynamic antecedents of online knowledge continuance, this thesis seeks to develop a holistic approach through three studies. Drawing on a decomposed theory of planned behaviour (Taylor and Todd, 1995), study one identifies dynamic antecedents of intentional online contribution behaviours. Covariance-based structural equation modelling analysis of 910 responses obtained shows that perceived critical mass and trust in online forums that mediates trust in members are the highlighted antecedents in the context of online forums. The development of trust in online forums is investigated through a time series approach in study two. Findings using webnographic and machine learning analysis show that the cognitive dimension of institutional trust is essential in initial trust building. Study three uses network analysis techniques to explore the role of critical mass members. Results indicate that only 5% of critical mass members can sustain online forums. However, critical mass members compete for their connections, inferring the importance of brand building in the beginning of online forums development. A summary of findings from the three studies suggests that the structure assurance of online forums can mediate the effects of interactions between members to a coalition of membership over time. The study provides further knowledge on the voluntary contribution within online forums by taking a dynamic approach, while previous studies in this field are predominantly cross-sectional and un-prophetic

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

How does rumination impact cognition? A first mechanistic model.

Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression

A computational model of focused attention meditation and its transfer to a sustained attention task

Although meditation and mindfulness practices are widely discussed and studied more and more in the scientific literature, there is little theory about the cognitive mechanisms that comprise it. Here we begin to develop such a theory by creating a computational cognitive model of a particular type of meditation: focused attention mediation. This model was created within Prims, a cognitive architecture similar to and based on ACT-R, which enables us to make predictions about the cognitive tasks that meditation experience may affect. We implemented a model based on an extensive literature review of how the meditation experience unfolds over time. We then subjected the Prims model to a session of the Sustained Reaction to Response Task, a task typically used to study sustained attention, a faculty that may be trained with meditation practice. Analyses revealed that the model was significantly more sensitive to detecting targets and non-targets after the meditation practice than before. These results agree with empirical findings of a longitudinal study conducted in 2010. These results suggest that our approach to modeling meditation and its effects of cognition is feasible

Detecting Abnormal Behavior in Web Applications

The rapid advance of web technologies has made the Web an essential part of our daily lives. However, network attacks have exploited vulnerabilities of web applications, and caused substantial damages to Internet users. Detecting network attacks is the first and important step in network security. A major branch in this area is anomaly detection. This dissertation concentrates on detecting abnormal behaviors in web applications by employing the following methodology. For a web application, we conduct a set of measurements to reveal the existence of abnormal behaviors in it. We observe the differences between normal and abnormal behaviors. By applying a variety of methods in information extraction, such as heuristics algorithms, machine learning, and information theory, we extract features useful for building a classification system to detect abnormal behaviors.;In particular, we have studied four detection problems in web security. The first is detecting unauthorized hotlinking behavior that plagues hosting servers on the Internet. We analyze a group of common hotlinking attacks and web resources targeted by them. Then we present an anti-hotlinking framework for protecting materials on hosting servers. The second problem is detecting aggressive behavior of automation on Twitter. Our work determines whether a Twitter user is human, bot or cyborg based on the degree of automation. We observe the differences among the three categories in terms of tweeting behavior, tweet content, and account properties. We propose a classification system that uses the combination of features extracted from an unknown user to determine the likelihood of being a human, bot or cyborg. Furthermore, we shift the detection perspective from automation to spam, and introduce the third problem, namely detecting social spam campaigns on Twitter. Evolved from individual spammers, spam campaigns manipulate and coordinate multiple accounts to spread spam on Twitter, and display some collective characteristics. We design an automatic classification system based on machine learning, and apply multiple features to classifying spam campaigns. Complementary to conventional spam detection methods, our work brings efficiency and robustness. Finally, we extend our detection research into the blogosphere to capture blog bots. In this problem, detecting the human presence is an effective defense against the automatic posting ability of blog bots. We introduce behavioral biometrics, mainly mouse and keyboard dynamics, to distinguish between human and bot. By passively monitoring user browsing activities, this detection method does not require any direct user participation, and improves the user experience

Total Quality Management and Six Sigma

In order to survive in a modern and competitive environment, organizations need to carefully organize their activities regarding quality management. TQM and six sigma are the approaches that have been successful in solving intricate quality problems in products and services. This volume can help those who are interested in the quality management field to understand core ideas along with contemporary efforts done in the field and authored as case studies in this volume. This volume may be useful to students, academics and practitioners across diversified disciplines