Statistical analysis of emotions and opinions at Digg website

We performed statistical analysis on data from the Digg.com website, which enables its users to express their opinion on news stories by taking part in forum-like discussions as well as directly evaluate previous posts and stories by assigning so called "diggs". Owing to fact that the content of each post has been annotated with its emotional value, apart from the strictly structural properties, the study also includes an analysis of the average emotional response of the posts commenting the main story. While analysing correlations at the story level, an interesting relationship between the number of diggs and the number of comments received by a story was found. The correlation between the two quantities is high for data where small threads dominate and consistently decreases for longer threads. However, while the correlation of the number of diggs and the average emotional response tends to grow for longer threads, correlations between numbers of comments and the average emotional response are almost zero. We also show that the initial set of comments given to a story has a substantial impact on the further "life" of the discussion: high negative average emotions in the first 10 comments lead to longer threads while the opposite situation results in shorter discussions. We also suggest presence of two different mechanisms governing the evolution of the discussion and, consequently, its length.Comment: 26 pages, 16 figures, 6 table

Computation in Complex Networks

Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

DYNAMICS OF IDENTITY THREATS IN ONLINE SOCIAL NETWORKS: MODELLING INDIVIDUAL AND ORGANIZATIONAL PERSPECTIVES

This dissertation examines the identity threats perceived by individuals and organizations in Online Social Networks (OSNs). The research constitutes two major studies. Using the concepts of Value Focused Thinking and the related methodology of Multiple Objectives Decision Analysis, the first research study develops the qualitative and quantitative value models to explain the social identity threats perceived by individuals in Online Social Networks. The qualitative value model defines value hierarchy i.e. the fundamental objectives to prevent social identity threats and taxonomy of user responses, referred to as Social Identity Protection Responses (SIPR), to avert the social identity threats. The quantitative value model describes the utility of the current social networking sites and SIPR to achieve the fundamental objectives for averting social identity threats in OSNs. The second research study examines the threats to the external identity of organizations i.e. Information Security Reputation (ISR) in the aftermath of a data breach. The threat analysis is undertaken by examining the discourses related to the data breach at Home Depot and JPMorgan Chase in the popular microblogging website, Twitter, to identify: 1) the dimensions of information security discussed in the Twitter postings; 2) the attribution of data breach responsibility and the related sentiments expressed in the Twitter postings; and 3) the subsequent diffusion of the tweets that threaten organizational reputation

Knowledge Modelling and Learning through Cognitive Networks

One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot