The Experts below are selected from a list of 2913 Experts worldwide ranked by ideXlab platform
Vineet M. Arora - One of the best experts on this subject based on the ideXlab platform.
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Correlations Between Hospitals' Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis.
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P
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correlations between hospitals social media presence and Reputation Score and ranking cross sectional analysis
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital's Reputation Score and total points (ie, overall rank). This association may indicate that a hospital's Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
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Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis (Preprint)
2017Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children’s hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and Reputation Score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children’s hospitals’ Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital’s Reputation Score and total points (ie, overall rank). This association may indicate that a hospital’s Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
Justin D Triemstra - One of the best experts on this subject based on the ideXlab platform.
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Correlations Between Hospitals' Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis.
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P
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correlations between hospitals social media presence and Reputation Score and ranking cross sectional analysis
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital's Reputation Score and total points (ie, overall rank). This association may indicate that a hospital's Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
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Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis (Preprint)
2017Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children’s hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and Reputation Score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children’s hospitals’ Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital’s Reputation Score and total points (ie, overall rank). This association may indicate that a hospital’s Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
Salil S Kanhere - One of the best experts on this subject based on the ideXlab platform.
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A Reputation Framework for Social Participatory Sensing Systems
Mobile Networks and Applications, 2014Co-Authors: Haleh Amintoosi, Salil S KanhereAbstract:Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic Reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A Reputation Score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our Reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate Reputation Scores.
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Providing Trustworthy Contributions via a Reputation Framework in Social Participatory Sensing Systems.
arXiv: Social and Information Networks, 2013Co-Authors: Haleh Amintoosi, Salil S KanhereAbstract:Social participatory sensing is a newly proposed paradigm that tries to address the limitations of participatory sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic Reputation framework for social participatory sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A Reputation Score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our Reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate Reputation Scores.
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PerCom - IncogniSense: An anonymity-preserving Reputation framework for participatory sensing applications
2012 IEEE International Conference on Pervasive Computing and Communications, 2012Co-Authors: Delphine Christin, Christian Rosskopf, Matthias Hollick, Leonardo A. Martucci, Salil S KanhereAbstract:Reputation systems rate the contributions to participatory sensing campaigns from each user by associating a Reputation Score. The Reputation Scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the Reputation Scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on Reputation transfer between these pseudonyms. The Reputation transfer process has an inherent trade-off between anonymity protection and loss in Reputation. We investigate by means of extensive simulations several Reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against Reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal.
Rachel Poeppelman - One of the best experts on this subject based on the ideXlab platform.
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Correlations Between Hospitals' Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis.
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P
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correlations between hospitals social media presence and Reputation Score and ranking cross sectional analysis
Journal of Medical Internet Research, 2018Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children's hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital's social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children's hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital's total points (ie, rank) and Reputation Score for both adult and children's hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children's hospitals' Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital's Reputation Score and total points (ie, overall rank). This association may indicate that a hospital's Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
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Correlations Between Hospitals’ Social Media Presence and Reputation Score and Ranking: Cross-Sectional Analysis (Preprint)
2017Co-Authors: Justin D Triemstra, Rachel Poeppelman, Vineet M. AroraAbstract:BACKGROUND The US News and World Report Reputation Score correlates strongly with overall rank in adult and pediatric hospital rankings. Social media affects how information is disseminated to physicians and is used by hospitals as a marketing tool to recruit patients. It is unclear whether the Reputation Score for adult and children’s hospitals relates to social media presence. OBJECTIVE The objective of our study was to analyze the association between a hospital’s social media metrics and the US News 2017-2018 Best Hospital Rankings for adult and children’s hospitals. METHODS We conducted a cross-sectional analysis of the Reputation Score, total Score, and social media metrics (Twitter, Facebook, and Instagram) of hospitals who received at least one subspecialty ranking in the 2017-2018 US News publicly available annual rankings. Regression analysis was employed to analyze the partial correlation coefficients between social media metrics and a hospital’s total points (ie, rank) and Reputation Score for both adult and children’s hospitals while controlling for the bed size and time on Twitter. RESULTS We observed significant correlations for children’s hospitals’ Reputation Score and total points with the number of Twitter followers (total points: r=.465, P<.001; Reputation: r=.524, P<.001) and Facebook followers (total points: r=.392, P=.002; Reputation: r=.518, P<.001). Significant correlations for the adult hospitals Reputation Score were found with the number of Twitter followers (r=.848, P<.001), number of tweets (r=.535, P<.001), Klout Score (r=.242, P=.02), and Facebook followers (r=.743, P<.001). In addition, significant correlations for adult hospitals total points were found with Twitter followers (r=.548, P<.001), number of tweets (r=.358, P<.001), Klout Score (r=.203, P=.05), Facebook followers (r=.500, P<.001), and Instagram followers (r=.692, P<.001). CONCLUSIONS A statistically significant correlation exists between multiple social media metrics and both a hospital’s Reputation Score and total points (ie, overall rank). This association may indicate that a hospital’s Reputation may be influenced by its social media presence or that the Reputation or rank of a hospital drives social media followers.
Omar Hasan - One of the best experts on this subject based on the ideXlab platform.
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A Survey of Privacy Preserving Reputation Systems
2017Co-Authors: Omar HasanAbstract:Reputation systems make the users of a distributed application accountable for their behavior. The Reputation of a user is computed as an aggregate of the feedback provided by other users in the system. Truthful feedback is clearly a prerequisite for computing a Reputation Score that accurately represents the behavior of a user. However, it has been observed that users often hesitate in providing truthful feedback, mainly due to the fear of retaliation. Privacy preserving Reputation systems enable users to provide feedback in a private and thus uninhibited manner. In this paper, we describe analysis frameworks for Reputation systems and privacy preserving Reputation systems. We use these analysis frameworks to review and compare the existing privacy preserving Reputation systems in the literature. We identify the strengths and weaknesses of the various systems. We also discuss some open challenges.
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Self-reported Verifiable Reputation with Rater Privacy
Trust Management XI, 2017Co-Authors: Rémi Bazin, Alexander Schaub, Omar Hasan, Lionel BrunieAbstract:Reputation systems are a major feature of every modern e-commerce website, helping buyers carefully choose their service providers and products. However, most websites use centralized Reputation systems, where the security of the system rests entirely upon a single Trusted Third Party. Moreover, they often disclose the identities of the raters, which may discourage honest users from posting frank reviews due to the fear of retaliation from the ratees. We present a Reputation system that is decentralized yet secure and efficient, and could therefore be applied in a practical context. In fact, users are able to retrieve the Reputation Score of a service provider directly from it in constant time, with assurance regarding the correctness of the information obtained. Additionally, the Reputation system is anonymity-preserving, which ensures that users can submit feedback without their identities being associated to it. Despite this anonymity, the system still offers robustness against attacks such as ballot-stuffing and Sybil attacks.
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A Decentralized Privacy Preserving Reputation Protocol for the Malicious Adversarial Model
IEEE Transactions on Information Forensics and Security, 2013Co-Authors: Omar Hasan, Lionel Brunie, Elisa Bertino, Ning ShangAbstract:Users hesitate to submit negative feedback in Reputation systems due to the fear of retaliation from the recipient user. A privacy preserving Reputation protocol protects users by hiding their individual feedback and revealing only the Reputation Score. We present a privacy preserving Reputation protocol for the malicious adversarial model. The malicious users in this model actively attempt to learn the private feedback values of honest users as well as to disrupt the protocol. Our protocol does not require centralized entities, trusted third parties, or specialized platforms, such as anonymous networks and trusted hardware. Moreover, our protocol is efficient. It requires an exchange of O(n + log N) messages, where n and N are the number of users in the protocol and the environment respectively.
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Privacy Preserving Reputation Management in Social Networks
2013Co-Authors: Omar Hasan, Lionel BrunieAbstract:Reputation management is a powerful security tool that helps establish the trustworthiness of users in online applications. One of the most successful use of Reputation systems is in e-commerce web sites such as eBay.com and Amazon.com which use Reputation systems to root out fraudulent sellers. Reputation systems can also play an important role in social networks to enforce various security requirements. For example, a Reputation system can help filter fake user profiles. However, a major challenge in developing Reputation systems for social networks is that users often hesitate to publicly rate fellow users or friends due to the fear of retaliation. This trend prevents a Reputation system from accurately computing Reputation Scores. Privacy preserving Reputation systems hide the individual ratings of users about others and only reveal the aggregated community Reputation Score thus allowing users to rate without the fear of retaliation. In this chapter, we describe privacy preserving Reputation management in social networks and the associated challenges. In particular we will look at privacy preserving Reputation management in decentralized social networks, where there is no central authority or trusted third parties, thus making the task of preserving privacy particularly challenging.
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Preserving Privacy of Feedback Providers in Decentralized Reputation Systems
Computers & Security, 2012Co-Authors: Omar Hasan, Lionel Brunie, Elisa BertinoAbstract:Reputation systems make the users of a distributed application accountable for their behavior. The Reputation of a user is computed as an aggregate of the feedback provided by other users in the system. Truthful feedback is clearly a prerequisite for computing a Reputation Score that accurately represents the behavior of a user. However, it has been observed that users often hesitate in providing truthful feedback, mainly due to the fear of retaliation. We present a decentralized privacy preserving Reputation protocol that enables users to provide feedback in a private and thus uninhibited manner. The protocol has linear message complexity, which is an improvement over comparable decentralized Reputation protocols. Moreover, the protocol allows users to quantify and maximize the probability that their privacy will be preserved.
