The Experts below are selected from a list of 9678 Experts worldwide ranked by ideXlab platform
Sapna R Kudchadkar - One of the best experts on this subject based on the ideXlab platform.
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social media engagement and the critical care medicine community
Journal of Intensive Care Medicine, 2019Co-Authors: Sean Barnes, Viren Kaul, Sapna R KudchadkarAbstract:Over the last decade, social media has transformed how we communicate in the medical community. Microblogging through platforms such as Twitter has made social media a vehicle for succinct, targete...
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social media engagement and the critical care medicine community
Journal of Intensive Care Medicine, 2019Co-Authors: Sean Barnes, Viren Kaul, Sapna R KudchadkarAbstract:Over the last decade, social media has transformed how we communicate in the medical community. Microblogging through platforms such as Twitter has made social media a vehicle for succinct, targeted, and innovative dissemination of content in critical care medicine. Common uses of social media in medicine include dissemination of information, knowledge acquisition, professional networking, and patient advocacy. Social media engagement at conferences represents all of these categories and is often the first time health-care providers are introduced to Twitter. Most of the major critical care medicine conferences, journals, and societies leverage social media for education, research, and advocacy, and social media users can tailor the inflow of content based on their own interests. From these interactions, networks and communities are built within critical care medicine and beyond, overcoming the barriers of physical proximity. In this review, we summarize the history and current status of health-care social media as it relates to critical care medicine and provide a primer for those new to health-care social media with a focus on Twitter, one of the most popular Microblogging platforms.
Huan Liu - One of the best experts on this subject based on the ideXlab platform.
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leveraging knowledge across media for spammer detection in Microblogging
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:While Microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for spammers to overwhelm other users with unwanted content. Currently, spammer detection in Microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of Microblogging messages. First, label information is hard to obtain. Second, the texts in Microblogging are short and noisy. As we know, spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for spammer detection in Microblogging. While people accept that texts in Microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of spammer detection in Microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for spammer detection in Microblogging.
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social spammer detection in Microblogging
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Jiliang Tang, Yanchao Zhang, Huan LiuAbstract:The availability of Microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of Microblogging systems for effective information dissemination and sharing. Distinct features of Microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, Microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, Microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to Microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in Microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.
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actnet active learning for networked texts in Microblogging
SIAM International Conference on Data Mining, 2013Co-Authors: Jiliang Tang, Huiji Gao, Huan LiuAbstract:Supervised learning, e.g., classification, plays an important role in processing and organizing Microblogging data. In Microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in Microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to Microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in Microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from Microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.
Chunghong Lee - One of the best experts on this subject based on the ideXlab platform.
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mining spatio temporal information on Microblogging streams using a density based online clustering method
Expert Systems With Applications, 2012Co-Authors: Chunghong LeeAbstract:Highlights? We applied a density-based stream clustering method for mining Twitter data. ? The developed method can detect real-time and geospatial event features. ? Using the detection results can estimate the temporal and spatial impacts of events. ? Our method is well suited for awareness of large-scale events and risk management. Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular Microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users for picking up all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to investigate how Microblogging social networks (i.e. Twitter) can be used as a reliable information source of emerging events by extracting their spatio-temporal features from the messages to enhance event awareness. In this work, we applied a density-based online clustering method for mining Microblogging text streams, in order to obtain temporal and geospatial features of real-world events. By analyzing the events detected by our system, the temporal and spatial impacts of the emerging events can be estimated, for achieving the goals of situational awareness and risk management.
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a novel approach for event detection by mining spatio temporal information on microblogs
Advances in Social Networks Analysis and Mining, 2011Co-Authors: Chunghong Lee, Hsinchang Yang, Tzanfeng Chien, Weishiang WenAbstract:Social networks have been regarded as a timely and cost-effective source of spatio-temporal information for many fields of application. However, while some research groups have successfully developed topic detection methods from the text streams for a while, and even some popular Microblogging services such as Twitter did provide information of top trending topics for selection, it is still unable to fully support users pickup all of the real-time event topics with a comprehensive spatio-temporal viewpoint to satisfy their information needs. This paper aims to enhance the understanding on how social networks can be used as a reliable source of spatio-temporal information, by analyzing the temporal and spatial dynamics of Twitter activity. In this work, we developed several algorithms for mining Microblogging text stream to obtain real-time and geospatial event information. The goal of our approach is to effectively detecting and grouping emerging topics by making use of real-time messages and geolocation data provided by social network services.
Sean Barnes - One of the best experts on this subject based on the ideXlab platform.
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social media engagement and the critical care medicine community
Journal of Intensive Care Medicine, 2019Co-Authors: Sean Barnes, Viren Kaul, Sapna R KudchadkarAbstract:Over the last decade, social media has transformed how we communicate in the medical community. Microblogging through platforms such as Twitter has made social media a vehicle for succinct, targete...
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social media engagement and the critical care medicine community
Journal of Intensive Care Medicine, 2019Co-Authors: Sean Barnes, Viren Kaul, Sapna R KudchadkarAbstract:Over the last decade, social media has transformed how we communicate in the medical community. Microblogging through platforms such as Twitter has made social media a vehicle for succinct, targeted, and innovative dissemination of content in critical care medicine. Common uses of social media in medicine include dissemination of information, knowledge acquisition, professional networking, and patient advocacy. Social media engagement at conferences represents all of these categories and is often the first time health-care providers are introduced to Twitter. Most of the major critical care medicine conferences, journals, and societies leverage social media for education, research, and advocacy, and social media users can tailor the inflow of content based on their own interests. From these interactions, networks and communities are built within critical care medicine and beyond, overcoming the barriers of physical proximity. In this review, we summarize the history and current status of health-care social media as it relates to critical care medicine and provide a primer for those new to health-care social media with a focus on Twitter, one of the most popular Microblogging platforms.
Jiliang Tang - One of the best experts on this subject based on the ideXlab platform.
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leveraging knowledge across media for spammer detection in Microblogging
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2014Co-Authors: Jiliang Tang, Huan LiuAbstract:While Microblogging has emerged as an important information sharing and communication platform, it has also become a convenient venue for spammers to overwhelm other users with unwanted content. Currently, spammer detection in Microblogging focuses on using social networking information, but little on content analysis due to the distinct nature of Microblogging messages. First, label information is hard to obtain. Second, the texts in Microblogging are short and noisy. As we know, spammer detection has been extensively studied for years in various media, e.g., emails, SMS and the web. Motivated by abundant resources available in the other media, we investigate whether we can take advantage of the existing resources for spammer detection in Microblogging. While people accept that texts in Microblogging are different from those in other media, there is no quantitative analysis to show how different they are. In this paper, we first perform a comprehensive linguistic study to compare spam across different media. Inspired by the findings, we present an optimization formulation that enables the design of spammer detection in Microblogging using knowledge from external media. We conduct experiments on real-world Twitter datasets to verify (1) whether email, SMS and web spam resources help and (2) how different media help for spammer detection in Microblogging.
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social spammer detection in Microblogging
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Jiliang Tang, Yanchao Zhang, Huan LiuAbstract:The availability of Microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of Microblogging systems for effective information dissemination and sharing. Distinct features of Microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, Microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, Microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to Microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in Microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.
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actnet active learning for networked texts in Microblogging
SIAM International Conference on Data Mining, 2013Co-Authors: Jiliang Tang, Huiji Gao, Huan LiuAbstract:Supervised learning, e.g., classification, plays an important role in processing and organizing Microblogging data. In Microblogging, it is easy to mass vast quantities of unlabeled data, but would be costly to obtain labels, which are essential for supervised learning algorithms. In order to reduce the labeling cost, active learning is an effective way to select representative and informative instances to query for labels for improving the learned model. Different from traditional data in which the instances are assumed to be independent and identically distributed (i.i.d.), instances in Microblogging are networked with each other. This presents both opportunities and challenges for applying active learning to Microblogging data. Inspired by social correlation theories, we investigate whether social relations can help perform effective active learning on networked data. In this paper, we propose a novel Active learning framework for the classification of Networked Texts in Microblogging (ActNeT). In particular, we study how to incorporate network information into text content modeling, and design strategies to select the most representative and informative instances from Microblogging for labeling by taking advantage of social network structure. Experimental results on Twitter datasets show the benefit of incorporating network information in active learning and that the proposed framework outperforms existing state-of-the-art methods.
