Fine-grained Emotion Role Detection Based on Retweet Information

User behaviors in online social networks convey not only literal information but also one’s emotion attitudes towards the information. To compute this attitude, we define the concept of emotion role as the concentrated reflection of a user’s online emotional characteristics. Emotion role detection aims to better understand the structure and sentiments of online social networks and support further analysis, e.g., revealing public opinions, providing personalized recommendations, and detecting influential users. In this paper, we first introduce the definition of a fine-grained emotion role, which consists of two dimensions: emotion orientation (i.e., positive, negative, and neutral) and emotion influence (i.e., leader and follower). We then propose a Multi-dimensional Emotion Role Mining model, named as MERM, to determine a user’s emotion role in online social networks. Specifically, we tend to identify emotion roles by combining a set of features that reflect a user’s online emotional status, including degree of emotional characteristics, accumulated emotion preference, structural factor, temporal factor and emotion change factor. Experiment results on a real-life micro-blog reposting dataset show that the classification accuracy of the proposed model can achieve up to 90.1%

The Effects of Social Capital on Chinese Consumers’ Online Impulse Buying in Social Commerce Environment

The purpose of this study is to examine the impact of social capital including social interaction shared language and trust on Chinese consumer online impulse buying in social commerce platforms To verify the hypothesis we surveyed the Chinese customers who have buying experiences of using social commerce by snowball sampling technique After analyzing 548 valid data we confirmed that three dimensions of social capital social interaction shared language and trust positively affects both peer intrinsic motivation and peer extrinsic motivation In addition peer motivation exerts a positively impact on consumer impulse purchases on social commerce The findings not only provide a new theoretical perspective for studying consumers impulse buying behavior in theory but also have important implications for the online sellers in their follower s groups to improve online marketin

Control and Data Analysis of Complex Networks

abstract: This dissertation treats a number of related problems in control and data analysis of complex networks. First, in existing linear controllability frameworks, the ability to steer a network from any initiate state toward any desired state is measured by the minimum number of driver nodes. However, the associated optimal control energy can become unbearably large, preventing actual control from being realized. Here I develop a physical controllability framework and propose strategies to turn physically uncontrollable networks into physically controllable ones. I also discover that although full control can be guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control energy to achieve actual control, and my work provides a framework to address this issue. Second, in spite of recent progresses in linear controllability, controlling nonlinear dynamical networks remains an outstanding problem. Here I develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another. I introduce the concept of attractor network and formulate a quantifiable framework: a network is more controllable if the attractor network is more strongly connected. I test the control framework using examples from various models and demonstrate the beneficial role of noise in facilitating control. Third, I analyze large data sets from a diverse online social networking (OSN) systems and find that the growth dynamics of meme popularity exhibit characteristically different behaviors: linear, “S”-shape and exponential growths. Inspired by cell population growth model in microbial ecology, I construct a base growth model for meme popularity in OSNs. Then I incorporate human interest dynamics into the base model and propose a hybrid model which contains a small number of free parameters. The model successfully predicts the various distinct meme growth dynamics. At last, I propose a nonlinear dynamics model to characterize the controlling of WNT signaling pathway in the differentiation of neural progenitor cells. The model is able to predict experiment results and shed light on the understanding of WNT regulation mechanisms.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Social Media Use by College Students and Teachers: An Application of UTAUT2

Social media has been increasingly used in education to facilitate innovative instruction. Mainland Chinese people could not use popular social media platforms such as FacebookTM because the government blocked them. Little research studied social media use by Mainland Chinese students and teachers in the isolated network environment. This quantitative study utilized social constructivism, connectivism, and the unified theory of acceptance and use of technology (UTAUT2) as the theoretical base. Research questions explored the influence of 6 UTAUT2 predictors on social media use intention and the influence of social media use intention on social media use behavior. The study used a convenience sample of 197 undergraduate students and 54 full-time faculty from 2 public science and technology universities in Guangzhou, Guangdong province, Mainland China. Survey data were analyzed using descriptive statistics, simple regression, multiple regression, and moderation analysis. The findings showed that performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit significantly influenced social media use intention, and social media use intention significantly influenced social media use behavior. Age moderated the relationship between facilitating conditions and social media use intention, and gender moderated the relationship between habit and social media use intention. The findings might be used to promote positive social change by providing insights of social media use by Chinese students and teachers for university administrators, government, and social media platform designers. Better understanding might facilitate adoption of social media in education and therefore improve teaching and learning for Mainland Chinese students