A Complex Social Network Analyses of Online Finanical Communties in Times of Geopolitcal Military and Terrorist Events

Given the advances in technology the field of social network analysis has very much hit the forefront in recent years. The information age harnesses the use of social network analysis for multiple industries and for solving complex problems. Social network analysis is an important tool in the world of the military and counter intelligence, whether it’s the capture of Osama Bin Laden or uncovering hidden Al Qaeda terrorist networks, the world around us is built on networks, be that hidden or otherwise. Online social networks give new information in the world of intelligence agencies similarly online financial communities such as Yahoo Finance gives intelligent information to knowledge hungry investors. This thesis is concerned with the exploration and exploitation of online financial community dynamics and networks using social network analysis (SNA) as a mechanism. Social network analysis measurement techniques will be applied to understand the reaction of online investors to military and terrorist geopolitical events, the stock market’s reaction to these events and if it is possible to predict military stock prices after military and terrorist geopolitical events

Social Networks And Online Gamer Loyalty

Online social networks are popular issues in electronic commerce and information systems areas. However, the social network issues have received relatively little attention from scholars in online gaming contexts. Online games experience a strong growth in revenue and popularity. Therefore, this study chose to focus on social networks in online games. In online gaming studies, online gamer loyalty has been one of the recent issues. Therefore, this study consulted classic psychological theories to construct a theoretical model which contains specific hypotheses to explain how social networks impact the formulation of online gamer loyalty. This study collected the responses from more than one thousand online gamers. The demographic and gaming behavior distributions resemble those of the online gamer populations, indicating the representativeness of the study sample. This study used measurement items from the literature and slightly modified them according to the research contexts. This study used confirmatory factor analysis and various indices to verify the measurement psychometric properties, including reliability, validity, and model fit. The analytical results supported adequate psychometric properties of the measurement used in this study. Moreover, this study used the structural equation modeling technique to examine the study hypotheses. The analytical results indicated that the hypothesized aspects of social networks impact online gamer loyalty, as predicted. Furthermore, this study examined the mechanism underlying such impact. This study is the first one examining how the hypothesized aspects of social networks contribute to the development of online gamer loyalty. Findings of this study provide insights for managers of electronic business (i.e., e-business) managers to retain loyal gamers, sustain stable revenues, and build competitive advantages, demonstrating the relevance of this study to ebusiness managers

An Examination Of Online Social Networks Properties With Tie-Strength

In the past, most researchers focused on the efficacy of tie-strength in various applications for both online and offline social networks. However, how tie-strength can help in the analysis of online social networks was a commonly neglected issue. The massive size and recording properties of online social networks offer the possibility to measure tie-strength objectively. In this study, we examine a social network extracted from a blog network. We then propose a tie-strength measurement and investigate several properties of the network using the tie-strength we defined. We also study how tie-strength plays a role in these properties

Online social networks: Measurement, analysis, and applications to distributed information systems

Recently, online social networking sites have exploded in popularity. Numerous sites are dedicated to finding and maintaining contacts and to locating and sharing different types of content. Online social networks represent a new kind of information network that differs significantly from existing networks like the Web. For example, in the Web, hyperlinks between content form a graph that is used to organize, navigate, and rank information. The properties of the Web graph have been studied extensively, and have lead to useful algorithms such as PageRank. In contrast, few links exist between content in online social networks and instead, the links exist between content and users, and between users themselves. However, little is known in the research community about the properties of online social network graphs at scale, the factors that shape their structure, or the ways they can be leveraged in information systems. In this thesis, we use novel measurement techniques to study online social networks at scale, and use the resulting insights to design innovative new information systems. First, we examine the structure and growth patterns of online social networks, focusing on how users are connecting to one another. We conduct the first large-scale measurement study of multiple online social networks at scale, capturing information about over 50 million users and 400 million links. Our analysis identifies a common structure across multiple networks, characterizes the underlying processes that are shaping the network structure, and exposes the rich community structure. Second, we leverage our understanding of the properties of online social networks to design new information systems. Specifically, we build two distinct applications that leverage different properties of online social networks. We present and evaluate Ostra, a novel system for preventing unwanted communication that leverages the difficulty in establishing and maintaining relationships in social networks. We also present, deploy, and evaluate PeerSpective, a system for enhancing Web search using the natural community, structure in social networks. Each of these systems has been evaluated on data from real online social networks or in a deployment with real users

Our Celebrities Our Selves: Reconstructing Ourselves as Online Personalities

Celebrity influence on consumer behavior at the online macro level is the motivation for this study that addresses the nature of celebrity consumption and how consumers apply that consumption to develop their online self-presentation. The sample for this study is limited to consumers with active accounts at online social networks such as Facebook or Twitter. Methodology is a three-part design. A multi-factor qualitative exploratory study (n=73) reveals four celebrity-consumer relationships whose proposed measurement scales are tested in a quantitative pilot study (n=85). Finally, a large sample study (n=593) is used to test the measurement model and to test the proposed relationships among the four constructs. Model fit was tested using a confirmatory factor analysis that returned significant fit indices. Convergent, discriminant and nomological validity tests supported the four-construct model. Finally, structural equation model analysis was performed to test the overall model fit and test the proposed relationships among constructs. The online celebrity relationship scale overall fit was positive and particularly convincing is that online Self Celebritization (consumers mimicking celebrities in their social media pages) is dependent on Celebrity Connectedness. The study contributes by confirming the link between extensive consumption of celebrities and people\u27s behavior online. The propensity of consumers to celebritize themselves online is predicated with the need to first consume the celebrities

Our Celebrities Our Selves: Reconstructing Ourselves as Online Personalities

Celebrity influence on consumer behavior at the online macro level is the motivation for this study that addresses the nature of celebrity consumption and how consumers apply that consumption to develop their online self-presentation. The sample for this study is limited to consumers with active accounts at online social networks such as Facebook or Twitter. Methodology is a three-part design. A multi-factor qualitative exploratory study (n=73) reveals four celebrity-consumer relationships whose proposed measurement scales are tested in a quantitative pilot study (n=85). Finally, a large sample study (n=593) is used to test the measurement model and to test the proposed relationships among the four constructs. Model fit was tested using a confirmatory factor analysis that returned significant fit indices. Convergent, discriminant and nomological validity tests supported the four-construct model. Finally, structural equation model analysis was performed to test the overall model fit and test the proposed relationships among constructs. The online celebrity relationship scale overall fit was positive and particularly convincing is that online Self Celebritization (consumers mimicking celebrities in their social media pages) is dependent on Celebrity Connectedness. The study contributes by confirming the link between extensive consumption of celebrities and people\u27s behavior online. The propensity of consumers to celebritize themselves online is predicated with the need to first consume the celebrities

Analyzing Users' Activity in On-line Social Networks over Time through a Multi-Agent Framework

[EN] The number of people and organizations using online social networks as a new way of communication is continually increasing. Messages that users write in networks and their interactions with other users leave a digital trace that is recorded. In order to understand what is going on in these virtual environments, it is necessary systems that collect, process, and analyze the information generated. The majority of existing tools analyze information related to an online event once it has finished or in a specific point of time (i.e., without considering an in-depth analysis of the evolution of users activity during the event). They focus on an analysis based on statistics about the quantity of information generated in an event. 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Measuring Time-Sensitive and Topic-Specific Influence in Social Networks with LSTM and Self-Attention.

Influence measurement in social networks is vital to various real-world applications, such as online marketing and political campaigns. In this paper, we investigate the problem of measuring time-sensitive and topic-specific influence based on streaming texts and dynamic social networks. A user's influence can change rapidly in response to a new event and vary on different topics. For example, the political influence of Douglas Jones increased dramatically after winning the Alabama special election, and then rapidly decreased after the election week. During the same period, however, Douglas Jones' influence on sports remained low. Most existing approaches can only model the influence based on static social network structures and topic distributions. Furthermore, as popular social networking services embody many features to connect their users, multi-typed interactions make it hard to learn the roles that different interactions play when propagating information. To address these challenges, we propose a Time-sensitive and Topic-specific Influence Measurement (TTIM) method, to jointly model the streaming texts and dynamic social networks. We simulate the influence propagation process with a self-attention mechanism to learn the contributions of different interactions and track the influence dynamics with a matrix-adaptive long short-term memory. To the best of our knowledge, this is the first attempt to measure time-sensitive and topic-specific influence. Furthermore, the TTIM model can be easily adapted to supporting online learning which consumes constant training time on newly arrived data for each timestamp. We comprehensively evaluate the proposed TTIM model on five datasets from Twitter and Reddit. The experimental results demonstrate promising performance compared to the state-of-the-art social influence analysis models and the potential of TTIM in visualizing influence dynamics and topic distribution