Decision Making in Online Social Networks

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    Early Detection of Rumor Veracity in Social Media
    ( 2019-01-08) Dang, Anh ; Moh’d, Abidalrahman ; Islam, Aminul ; Milios, Evangelos
    Rumor spread has become a significant issue in online social networks (OSNs). To mitigate and limit the spread of rumors and its detrimental effects, analyzing, detecting and better understanding rumor dynamics is required. One of the critical steps of studying rumor spread is to identify the level of the rumor truthfulness in its early stage. Understanding and identifying the level of rumor truthfulness helps prevent its viral spread and minimizes the damage a rumor may cause. In this research, we aim to debunk rumors by analyzing, visualizing, and classifying the level of rumor truthfulness from a large number of users that actively engage in rumor spread. First, we create a dataset of rumors that belong to one of five categories: "False", "Mostly False", "True", "Mostly True", and "Half True". This dataset provides intrinsic characteristics of a rumor: topics, user's sentiment, network structural and content features. Second, we analyze and visualize the characteristics of each rumor category to better understand its features. Third, using theories from social science and psychology, we build a feature set to classify those rumors and identify their truthfulness. The evaluation results on our new dataset show that the approach could effectively detect the truth of rumors as early as seven days. The proposed approach could be used as a valuable tool for existing fact-checking websites, such as or, to detect the veracity of rumors in its early stage automatically and educate OSN users to have a well-informed decision-making process.
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    Where Does My Product Stand? A Social Network Perspective on Online Product Reviews
    ( 2019-01-08) Vemprala, Naga ; Xiong, Rachael Ruizhu ; Liu, Charles Zhechao ; Choo, Kim-Kwang Raymond
    Customer reviews often include comparative comments on competing products. Adopting the "The Strength of Weak Ties" theory, we build a product social network around “strong tie” and “weak tie” entities. By performing text mining on comparative customer reviews collected from Amazon, we successfully identify strong and weak ties in a product network and compute the strength of these ties. Utilizing these network properties, we generate network graphs based on different product features and discover the underlying competitive relationships among them. In particular, our regression analysis shows that the strength of ties positively contributes to the review rating of a product and the strength of weak ties plays a more significant role than the strength of strong ties. These results will benefit vendors in online market to discover potential competitors, effectively tailor their marketing and product development efforts, and better position their products to increase profit and explore new market opportunities.
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    Users’ Participation Motivation and Behavior Patterns in Online Health Community: A Game Theory Viewpoint
    ( 2019-01-08) Shang, Yanyan
    Online health communities (OHC) are one of the most promising health-related social media services that have been developed, increasing in numbers and users in the past decade. Studies show that patients can benefit from participating in OHC, including obtaining information and knowledge, receiving support, and releasing mental stress. The purpose of this study is to identify the motivation behind users’ participation and to understand their behavior patterns across time in the online health community. A game theoretic model is used.
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    Towards Intelligent Decision Making in Emotion-aware Applications
    ( 2019-01-08) Wang, Jiaqi ; Wang, Siqi ; Guo, Yanxiang ; Hu, Xiping ; Li, Xitong ; Cheng, Jun
    In this paper, we propose an intelligent emotion-aware system (IES), which aims to provide a systematic approach that can make use of the online technology to improve the intelligence of different emotion-aware mobile applications. IES is constructed to provide multi-dimensional online social community data collection and processing approaches for decision making, so as to recommend intelligent services for emotion-aware mobile applications. Furthermore, we present a flow of intelligent decision making process designed on IES, and highlight the implementation and orchestration of several key technologies and schemes applied in this system for different emotion-aware mobile applications in run-time. We demonstrate the feasibility of the proposed IES by presenting a novel emotion-aware mobile application - iSmile, and evaluate the system performance based on this application.
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    Introduction to the Minitrack on Decision Making in Online Social Networks
    ( 2019-01-08) Sadovykh, Valeria ; Peko, Gabrielle ; Sundaram, David