Xiaojiang Liu

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Xiaojiang Liu is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
Comparing the pulses of categorical hot events in Twitter and Weibo Click log mining
Community comparison
Information diffusion
Information retrieval
Social media
HT 2014 - Proceedings of the 25th ACM Conference on Hypertext and Social Media English 2014 The fragility and interconnectivity of the planet argue compellingly for a greater understanding of how different communities make sense of their world. One of such critical demands relies on comparing the Chinese and the rest of the world (e.g., Americans), where communities' ideological and cultural backgrounds can be significantly different. While traditional studies aim to learn the similarities and differences between these communities via high-cost user studies, in this paper we propose a much more efficient method to compare different communities by utilizing social media. Specifically, Weibo and Twitter, the two largest microblogging systems, are employed to represent the target communities, i.e. China and the Western world (mainly United States), respectively. Meanwhile, through the analysis of the Wikipedia page-click log, we identify a set of categorical 'hot events' for one month in 2012 and search those hot events in Weibo and Twitter corpora along with timestamps via information retrieval methods. We further quantitatively and qualitatively compare users' responses to those events in Twitter and Weibo in terms of three aspects: popularity, temporal dynamic, and information diffusion. The comparative results show that although the popularity ranking of those events are very similar, the patterns of temporal dynamics and information diffusion can be quite different. 0 0
Wikimantic: Toward effective disambiguation and expansion of queries Disambiguation
Query expansion
Search queries
Data and Knowledge Engineering English 2014 This paper presents an implemented and evaluated methodology for disambiguating terms in search queries and for augmenting queries with expansion terms. By exploiting Wikipedia articles and their reference relations, our method is able to disambiguate terms in particularly short queries with few context words and to effectively expand queries for retrieval of short documents such as tweets. Our strategy can determine when a sequence of words should be treated as a single entity rather than as a sequence of individual entities. This work is part of a larger project to retrieve information graphics in response to user queries. © 2013 Elsevier B.V. 0 0
A comparative study of academic and wikipedia ranking Citation analysis
Scholar impact
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries English 2013 In addition to its broad popularity Wikipedia is also widely used for scholarly purposes. Many Wikipedia pages pertain to academic papers, scholars and topics providing a rich ecology for scholarly uses. Scholarly references and mentions on Wikipedia may thus shape the \societal impact" of a certain scholarly communication item, but it is not clear whether they shape actual \academic impact". In this paper we compare the impact of papers, scholars, and topics according to two different measures, namely scholarly citations and Wikipedia mentions. Our results show that academic and Wikipedia impact are positively correlated. Papers, authors, and topics that are mentioned on Wikipedia have higher academic impact than those are not mentioned. Our findings validate the hypothesis that Wikipedia can help assess the impact of scholarly publications and underpin relevance indicators for scholarly retrieval or recommendation systems. Copyright © 2013 by the Association for Computing Machinery, Inc. (ACM). 0 0
Community detection from signed networks Community detection
Signed network
Transactions of the Japanese Society for Artificial Intelligence English 2013 Many real-world complex systems can be modeled as networks, and most of them exhibit community structures. Community detection from networks is one of the important topics in link mining. In order to evaluate the goodness of detected communities, Newman modularity is widely used. In real world, however, many complex systems can be modeled as signed networks composed of positive and negative edges. Community detection from signed networks is not an easy task, because the conventional detection methods for normal networks cannot be applied directly. In this paper, we extend Newman modularity for signed networks. We also propose a method for optimizing our modularity, which is an efficient hierarchical agglomeration algorithm for detecting communities from signed networks. Our method enables us to detect communities from large scale real-world signed networks which represent relationship between users on websites such as Wikipedia, Slashdot and Epinions. 0 0
Scientific cyberlearning resources referential metadata creation via information retrieval Cyberlearning resource
Metadata generation
Proceedings of the ACM/IEEE Joint Conference on Digital Libraries English 2012 The goal of this research is to describe an innovative method of creating scientific referential metadata for a cyberinfrastructure-enabled learning environment to enhance student and scholar learning experiences. By using information retrieval and meta-search approaches, different types of referential metadata, such as related Wikipedia Pages, Datasets, Source Code, Video Lectures, Presentation Slides, and (online) Tutorials, for an assortment of publications and scientific topics will be automatically retrieved, associated, and ranked. 0 0
Automatically weighting tags in XML collection Tag weighting model
Topic generalization
XML retrieval
International Conference on Information and Knowledge Management, Proceedings English 2010 In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly. 0 0
BioSnowball: Automated population of wikis Bootstrapping
Fact extraction
Markov Logic Networks
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining English 2010 Internet users regularly have the need to find biographies and facts of people of interest. Wikipedia has become the first stop for celebrity biographies and facts. However, Wiki-pedia can only provide information for celebrities because of its neutral point of view (NPOV) editorial policy. In this paper we propose an integrated bootstrapping framework named BioSnowball to automatically summarize the Web to generate Wikipedia-style pages for any person with a modest web presence. In BioSnowball, biography ranking and fact extraction are performed together in a single integrated training and inference process using Markov Logic Networks (MLNs) as its underlying statistical model. The bootstrapping framework starts with only a small number of seeds and iteratively finds new facts and biographies. As biography paragraphs on the Web are composed of the most important facts, our joint summarization model can improve the accuracy of both fact extraction and biography ranking compared to decoupled methods in the literature. Empirical results on both a small labeled data set and a real Web-scale data set show the effectiveness of BioSnowball. We also empirically show that BioSnowball outperforms the decoupled methods. 0 0
Empirical testing of a theoretical extension of the technology acceptance model: An exploratory study of educational wikis Educational wikis
Empirical testing
Technology acceptance model
Theoretical extension
Communication Education English 2010 This study extended the technology acceptance model and empirically tested the new model with wikis, a new type of educational technology. Based on social cognitive theory and the theory of planned behavior, three new variables, wiki self-efficacy, online posting anxiety, and perceived behavioral control, were added to the original technology acceptance model. A structural equation model was developed. Both qualitative and quantitative methods were conducted to test the structural model. The study found that wiki self-efficacy, perceived ease of use, perceived usefulness, and wiki use intention have direct and indirect significant impact on wiki usage in the classroom. 0 0