{"title":"Session details: Salton Award","authors":"C. Clarke","doi":"10.1145/3255914","DOIUrl":"https://doi.org/10.1145/3255914","url":null,"abstract":"","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115492268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anjie Fang, I. Ounis, P. Habel, C. Macdonald, Nut Limsopatham
In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions ("Yes"--in favour of Independence vs. "No"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline.
{"title":"Topic-centric Classification of Twitter User's Political Orientation","authors":"Anjie Fang, I. Ounis, P. Habel, C. Macdonald, Nut Limsopatham","doi":"10.1145/2766462.2767833","DOIUrl":"https://doi.org/10.1145/2766462.2767833","url":null,"abstract":"In the recent Scottish Independence Referendum (hereafter, IndyRef), Twitter offered a broad platform for people to express their opinions, with millions of IndyRef tweets posted over the campaign period. In this paper, we aim to classify people's voting intentions by the content of their tweets---their short messages communicated on Twitter. By observing tweets related to the IndyRef, we find that people not only discussed the vote, but raised topics related to an independent Scotland including oil reserves, currency, nuclear weapons, and national debt. We show that the views communicated on these topics can inform us of the individuals' voting intentions (\"Yes\"--in favour of Independence vs. \"No\"--Opposed). In particular, we argue that an accurate classifier can be designed by leveraging the differences in the features' usage across different topics related to voting intentions. We demonstrate improvements upon a Naive Bayesian classifier using the topics enrichment method. Our new classifier identifies the closest topic for each unseen tweet, based on those topics identified in the training data. Our experiments show that our Topics-Based Naive Bayesian classifier improves accuracy by 7.8% over the classical Naive Bayesian baseline.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124396325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We demonstrate an exploration tool that organizes social media content under diverse aspects enabling comprehensive explorations. Unlike existing approaches that group content by trending topics, we present a holistic view of diverse and relevant content with respect to a given query.
{"title":"An Aspect-driven Social Media Explorer","authors":"Nedim Lipka, W. Bruce Croft","doi":"10.1145/2766462.2767864","DOIUrl":"https://doi.org/10.1145/2766462.2767864","url":null,"abstract":"We demonstrate an exploration tool that organizes social media content under diverse aspects enabling comprehensive explorations. Unlike existing approaches that group content by trending topics, we present a holistic view of diverse and relevant content with respect to a given query.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127090063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.
{"title":"Inferring Searcher Attention by Jointly Modeling User Interactions and Content Salience","authors":"Dmitry Lagun, Eugene Agichtein","doi":"10.1145/2766462.2767745","DOIUrl":"https://doi.org/10.1145/2766462.2767745","url":null,"abstract":"Modeling and predicting user attention is crucial for interpreting search behavior. The numerous applications include quantifying web search satisfaction, estimating search quality, and measuring and predicting online user engagement. While prior research has demonstrated the value of mouse cursor data and other interactions as a rough proxy of user attention, precisely predicting where a user is looking on a page remains a challenge, exacerbated in Web pages beyond the traditional search results. To improve attention prediction on a wider variety of Web pages, we propose a new way of modeling searcher behavior data by connecting the user interactions to the underlying Web page content. Specifically, we propose a principled model for predicting a searcher's gaze position on a page, that we call Mixture of Interactions and Content Salience (MICS). To our knowledge, our model is the first to effectively combine user interaction data, such as mouse cursor and scrolling positions, with the visual prominence, or salience, of the page content elements. Extensive experiments on multiple popular types of Web content demonstrate that the proposed MICS model significantly outperforms previous approaches to searcher gaze prediction that use only the interaction information. Grounding the observed interactions to the underlying page content provides a general and robust approach to user attention modeling, enabling more powerful tool for search behavior interpretation and ultimately search quality improvements.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125836644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As users turn increasingly to handheld devices to find information, the research community has focused on real-time location signals (GPS signals) to improve search engine effectiveness. Location signals have been investigated for predicting businesses the user will frequent[3], assigning geographic coordinates to media files[1], and to improve mobile search ranking[2]. While the increased focus on real-time user location has produced excellent research, there remains a gap between the capabilities being developed in the research community, and the capabilities being developed by commercial search engines. The core of this discrepancy between the advances in research and advances in industry is understanding the user's location. The vast majority of research on user location assumes that the user's location is known, because the user has provided a GPS signal. For many systems, there is no GPS signal available. The user may choose not enable it, or the system chooses not to prompt the user for the location because doing so degrades the user experience. For these interactions, the system relies on the user's IP address for location information. Further, much of the current research uses public geocoded data such as Foursquare (http://www.foursquare.com visited June 2015), and Twitter (http://www.twitter.com visited June 2015). These data are an incomplete picture of places a user may visit, and are potentially biased in their representation of actual users. The information contained in these data is not the same type of information typically available to a commercial search engine. In this talk we discuss gaps between current research on location, and industry advances in using location signals to improve search results. We focus on user location as one example of a gap between research and development.
{"title":"Location in Search","authors":"Vanessa Murdock","doi":"10.1145/2766462.2776783","DOIUrl":"https://doi.org/10.1145/2766462.2776783","url":null,"abstract":"As users turn increasingly to handheld devices to find information, the research community has focused on real-time location signals (GPS signals) to improve search engine effectiveness. Location signals have been investigated for predicting businesses the user will frequent[3], assigning geographic coordinates to media files[1], and to improve mobile search ranking[2]. While the increased focus on real-time user location has produced excellent research, there remains a gap between the capabilities being developed in the research community, and the capabilities being developed by commercial search engines. The core of this discrepancy between the advances in research and advances in industry is understanding the user's location. The vast majority of research on user location assumes that the user's location is known, because the user has provided a GPS signal. For many systems, there is no GPS signal available. The user may choose not enable it, or the system chooses not to prompt the user for the location because doing so degrades the user experience. For these interactions, the system relies on the user's IP address for location information. Further, much of the current research uses public geocoded data such as Foursquare (http://www.foursquare.com visited June 2015), and Twitter (http://www.twitter.com visited June 2015). These data are an incomplete picture of places a user may visit, and are potentially biased in their representation of actual users. The information contained in these data is not the same type of information typically available to a commercial search engine. In this talk we discuss gaps between current research on location, and industry advances in using location signals to improve search results. We focus on user location as one example of a gap between research and development.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125972686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Information retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies and connections between these two disciplines. However, there have been very limited efforts to connect the two important disciplines. On the other hand, due to lack of mature techniques in privacy-preserving IR, concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, tweets, and medical record retrieval. We propose this privacy-preserving IR workshop to connect the two disciplines of information retrieval and information privacy and security. We look forward to spurring research that aims to bring together the research fields of IR and privacy/security. Last year, the first privacy-preserving IR workshop focused on mitigating privacy threats in information retrieval by novel algorithms and tools that enable web users to better understand associated privacy risks.
{"title":"Privacy-Preserving IR 2015: When Information Retrieval Meets Privacy and Security","authors":"G. Yang, I. Soboroff","doi":"10.1145/2766462.2767857","DOIUrl":"https://doi.org/10.1145/2766462.2767857","url":null,"abstract":"Information retrieval (IR) and information privacy/security are two fast-growing computer science disciplines. There are many synergies and connections between these two disciplines. However, there have been very limited efforts to connect the two important disciplines. On the other hand, due to lack of mature techniques in privacy-preserving IR, concerns about information privacy and security have become serious obstacles that prevent valuable user data to be used in IR research such as studies on query logs, social media, tweets, and medical record retrieval. We propose this privacy-preserving IR workshop to connect the two disciplines of information retrieval and information privacy and security. We look forward to spurring research that aims to bring together the research fields of IR and privacy/security. Last year, the first privacy-preserving IR workshop focused on mitigating privacy threats in information retrieval by novel algorithms and tools that enable web users to better understand associated privacy risks.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125445526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jingang Wang, Dandan Song, Qifan Wang, Zhiwei Zhang, Luo Si, L. Liao, Chin-Yew Lin
This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.
{"title":"An Entity Class-Dependent Discriminative Mixture Model for Cumulative Citation Recommendation","authors":"Jingang Wang, Dandan Song, Qifan Wang, Zhiwei Zhang, Luo Si, L. Liao, Chin-Yew Lin","doi":"10.1145/2766462.2767698","DOIUrl":"https://doi.org/10.1145/2766462.2767698","url":null,"abstract":"This paper studies Cumulative Citation Recommendation (CCR) for Knowledge Base Acceleration (KBA). The CCR task aims to detect potential citations of a set of target entities with priorities from a volume of temporally-ordered stream corpus. Previous approaches for CCR that build an individual relevance model for each entity fail to handle unseen entities without annotation. A baseline solution is to build a global entity-unspecific model for all entities regardless of the relationship information among entities, which cannot guarantee to achieve satisfactory result for each entity. In this paper, we propose a novel entity class-dependent discriminative mixture model by introducing a latent entity class layer to model the correlations between entities and latent entity classes. The model can better adjust to different types of entities and achieve better performance when dealing with a broad range of entities. An extensive set of experiments has been conducted on TREC-KBA-2013 dataset, and the experimental results demonstrate that the proposed model can achieve the state-of-the-art performance.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116089695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
User engagement evaluation task in social networks has recently attracted considerable attention due to its applications in recommender systems. In this task, the posts containing users' opinions about items, e.g., the tweets containing the users' ratings about movies in the IMDb website, are studied. In this paper, we try to make use of tweets from different web applications to improve the user engagement evaluation performance. To this aim, we propose an adaptive method based on multi-task learning. Since in this paper we study the problem of detecting tweets with positive engagement which is a highly imbalanced classification problem, we modify the loss function of multi-task learning algorithms to cope with the imbalanced data. Our evaluations over a dataset including the tweets of four diverse and popular data sources, i.e., IMDb, YouTube, Goodreads, and Pandora, demonstrate the effectiveness of the proposed method. Our findings suggest that transferring knowledge between data sources can improve the user engagement evaluation performance.
{"title":"Adaptive User Engagement Evaluation via Multi-task Learning","authors":"Hamed Zamani, Pooya Moradi, A. Shakery","doi":"10.1145/2766462.2767785","DOIUrl":"https://doi.org/10.1145/2766462.2767785","url":null,"abstract":"User engagement evaluation task in social networks has recently attracted considerable attention due to its applications in recommender systems. In this task, the posts containing users' opinions about items, e.g., the tweets containing the users' ratings about movies in the IMDb website, are studied. In this paper, we try to make use of tweets from different web applications to improve the user engagement evaluation performance. To this aim, we propose an adaptive method based on multi-task learning. Since in this paper we study the problem of detecting tweets with positive engagement which is a highly imbalanced classification problem, we modify the loss function of multi-task learning algorithms to cope with the imbalanced data. Our evaluations over a dataset including the tweets of four diverse and popular data sources, i.e., IMDb, YouTube, Goodreads, and Pandora, demonstrate the effectiveness of the proposed method. Our findings suggest that transferring knowledge between data sources can improve the user engagement evaluation performance.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"2528 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128654111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md. Hedayetul Islam Shovon, N. Nandagopal, J. Du, Vijayalakshmi Ramasamy, Bernadine Cocks
Searching on the Web or Net-surfing is a part of everyday life for many people, but little is known about the brain activity during Web searching. Such knowledge is essential for better understanding of the cognitive demands imposed by the search system and search tasks. The current study contributes to this understanding by constructing brain networks from EEG data using normalized transfer entropy (NTE) during three Web search task stages: query formulation, viewing of a search result list and reading each individual content page. This study further contributes to the connectivity analysis of the constructed brain networks, since it is an advanced quantitative technique which enables the exploration of brain function by distinct and varied brain areas. By using this approach, we identified that the cognitive activities during the three stages of Web searching are different, with various brain areas becoming more active during the three Web search task stages. Of note, query formulation generated higher interaction between cortical regions than viewing a result list or reading a content page. These findings will have implications for the improvement of Web search engines and search interfaces.
{"title":"Cognitive Activity during Web Search","authors":"Md. Hedayetul Islam Shovon, N. Nandagopal, J. Du, Vijayalakshmi Ramasamy, Bernadine Cocks","doi":"10.1145/2766462.2767784","DOIUrl":"https://doi.org/10.1145/2766462.2767784","url":null,"abstract":"Searching on the Web or Net-surfing is a part of everyday life for many people, but little is known about the brain activity during Web searching. Such knowledge is essential for better understanding of the cognitive demands imposed by the search system and search tasks. The current study contributes to this understanding by constructing brain networks from EEG data using normalized transfer entropy (NTE) during three Web search task stages: query formulation, viewing of a search result list and reading each individual content page. This study further contributes to the connectivity analysis of the constructed brain networks, since it is an advanced quantitative technique which enables the exploration of brain function by distinct and varied brain areas. By using this approach, we identified that the cognitive activities during the three stages of Web searching are different, with various brain areas becoming more active during the three Web search task stages. Of note, query formulation generated higher interaction between cortical regions than viewing a result list or reading a content page. These findings will have implications for the improvement of Web search engines and search interfaces.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130576445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification.
{"title":"Using Sensor Metadata Streams to Identify Topics of Local Events in the City","authors":"M. Albakour, C. Macdonald, I. Ounis","doi":"10.1145/2766462.2767837","DOIUrl":"https://doi.org/10.1145/2766462.2767837","url":null,"abstract":"In this paper, we study the emerging Information Retrieval (IR) task of local event retrieval using sensor metadata streams. Sensor metadata streams include information such as the crowd density from video processing, audio classifications, and social media activity. We propose to use these metadata streams to identify the topics of local events within a city, where each event topic corresponds to a set of terms representing a type of events such as a concert or a protest. We develop a supervised approach that is capable of mapping sensor metadata observations to an event topic. In addition to using a variety of sensor metadata observations about the current status of the environment as learning features, our approach incorporates additional background features to model cyclic event patterns. Through experimentation with data collected from two locations in a major Spanish city, we show that our approach markedly outperforms an alternative baseline. We also show that modelling background information improves event topic identification.","PeriodicalId":297035,"journal":{"name":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval","volume":"159 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121311970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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