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Browsing by Subject "social networking"
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Item The Labor of Fun: Understanding the Social Relationships between Gamers and Paid Gaming Teammates in China(ACM, 2021-05) Shen, Chenxinran; Lu, Zhicong; Faas, Travis; Wigdor, Daniel; Human-Centered Computing, School of Informatics and ComputingOnline video games support the development of social relationships through gameplay, however, gamers often cannot cultivate and maintain relationships based on social factors such as personality when using in-game matchmaking services. To address this, teammate matching sites external to games have emerged and enable gamers to offer to play games with others in exchange for payment. The affordances of these services are different from other existing gamer social sites, e.g., live streaming. Interviews were conducted with 16 dedicated users on Bixin, one of China’s largest paid teammate matching sites, to examine user motivations, practices, and perceptions. The interviews found that gamers selected paid teammates on Bixin using different criteria compared to in-game matchmaking services and emphasized the importance of real-life characteristics such as voice. To maintain connections, paid teammates often also extended communication to external communication services such as WeChat. Although most gamers expected to communicate with paid teammates as if they were friends, very few reported building real friendships with their matched counterparts.Item Preserving Graph Utility in Anonymized Social Networks? A Study on the Persistent Homology(IEEE, 2017-10) Gao, Tianchong; Li, Feng; Engineering Technology, School of Engineering and TechnologyFollowing the trend of privacy preserving online social network publishing, various anonymization mechanisms have been designed and employed. Many differential privacy-based mechanisms claim that they can preserve the utility as well as guarantee the privacy. Their utility analysis are always based on some specifically chosen metrics.This paper aims to find a novel angle that describing the network in multiple scales. Persistent homology is such a high level metric that it reveals the parameterized topological features with various scales and it is applicable for read-world applications. In this paper, four differential privacy mechanisms employing different models are analyzed under the traditional graph metrics and the persistent homology. The evaluation results demonstrate that all algorithms can partially or conditionally preserve certain traditional graph utilities, but none of them are suitable for all metrics. Furthermore, none of the existing mechanisms can fully preserve the persistent homology, especially in high dimensions, which implies that the true graph utility is lost.Item Social Media Sensing Framework for Population Health(IEEE, 2019) Esperanca, Alvaro; Miled, Zina Ben; Mahoui, Malika; Electrical and Computer Engineering, School of Engineering and TechnologyConducting large health population studies is expensive. For instance, collecting field information about the efficacy of health campaigns or the impact of a disease may require the involvement of many health providers over an extended period of time and sometimes may not reach the target population. In fact, due to the aforementioned difficulties, health-related population statistics may be unavailable or lag by several years. Recently, social media networks have emerged as a source of sensory data for various aspects of social behavior. This source of information is used to drive marketing campaigns, conduct threat analysis and profile groups of individuals among numerous other applications. However, these applications are usually limited to specific case studies and do not provide a systematic approach to translating social media data into knowledge. In this paper, we propose a framework that can extract knowledge from social media networks in support of large scale health studies. The framework consists of an automated workflow designed to collect data from social media platforms, filter the data based on geographical criteria, and extract information relevant to a target hypothesis. The framework is demonstrated in the case of mortality and incidence of three chronic diseases, namely asthma, cancer, and diabetes. Twitter data is extracted over the period 2010 to 2015 for each target geographical region and classified based on its relevance to each of the aforementioned diseases. Due to the large number of extracted records, a simple random sampling approach is used to support the supervised training and testing of the classifier in the framework. Despite the limited number of records used for the training of the classifiers as a result of this approach, high classification accuracies are achieved for all three diseases. While the focus of the case studies in this paper is on the three chronic diseases asthma, diabetes and cancer, the utility of the proposed framework extends to other areas in the health sector. The proposed framework can help automate data-driven hypothesis validation for social media health-related studies. This paper describes the underlying methodology as well as the limitations associated with using social media data as a sensor for trends in population health.