What makes bloggers share knowledge? An investigation on the role of trust

As a Web 2.0 technology, blogs are gaining attention as useful knowledge sharing platforms for knowledge management in a collaborative work environment. This study investigates the relationship between trust and bloggers’ knowledge sharing practices. Based on an analysis of results from the 485 survey respondents, the research found that there is the positive relationship between bloggers’ trust and their knowledge sharing practices. This study explores trust in multiple dimensions including economy-based trust, trust in bloggers, and trust in the Internet and trust in blog providers. The detailed research findings are presented.     

BLOG与知识管理

在知识管理因实践效果不佳而陷于低潮的同时，Blog的兴起带动了大量的网民在无边界的网络上自发地进行知识分享、协同创作的潮流，一些知识管理学者因此开始关注Blog在知识管理中运用的可能。然而，如果用静止的、分析还原的思维方式去研究Blog，很难把握Blog的交互机制在知识管理和创建学习型组织中的作用。本文用复杂适应系统理论分析了Blog协同机制，阐述了基于Blog的知识管理体系的工作模式和特点，最后针对如何在组织中具体推动Blog知识管理实践，给出了一些操作性的策略。     

从图书馆学学术信息非正式传播的角度分析我国图林博客

博客作为一种具有个性化、交互性、开放性等众多特点的信息传播工具,为人们进行更便捷的信息交流提供了一个新的平台,受到图书馆学界的青睐。本文通过对图林博客的概念、结构功能以及国内多个图书馆人的博客内容进行比较研究,结合综合性博客、专门性博客和潜水者博客评论这3种博文类型,分析了图林博客中图书馆学学术信息非正式传播的内容丰富,语言平民化、个性化,传播不受时空限制,意见领袖影响传播等鲜明特点,进而论证了其对图书馆学学术信息非正式传播的作用。     

移动环境下基于隐性评分的博客推荐技术

在移动环境下让用户对博客进行直接评分有很多弊端.因此,如何获取用户对博客的评分信息已成为一个亟待解决的问题.基于隐性评分技术,通过分析用户阅读博客时的阅读速度和阅读文章的比例,计算出用户对博客的偏好信息,进而将传统的基于项目的协同过滤技术应用到博客推荐中,提出了移动环境下基于隐性评分的协同过滤博客推荐技术.最后,通过实验证明该技术可以在移动环境下有效地为用户推荐符合其兴趣的博客.     

基于链接分析法的网络社区影响力研究 ——以国内30个网络社区网站为例

论文通过Yahoo！和Bing搜索引擎获取30个网络社区网站的网页总数、链接总数、内、外部链接数、PR值,并计算了网络影响因子等,运用灰色关联分析对以上多项链接指标数据进行综合排序。研究结果表明:这30个网络社区网站网络影响力前几位是:51.com、腾讯微博、腾讯博客、腾讯论坛、网易微博、网易博客、新浪博客、豆瓣网。最后通过对比Yahoo!和Bing搜索引擎获取的链接数据,验证了两大搜索引擎对于网站链接分析是可行的,但是用Yahoo搜索引擎统计的数据来分析更为准确一些。     

Computing controversy: Formal model and algorithms for detecting controversy on Wikipedia and in search queries

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble.Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.     

Linguistic aggregation methods in blog retrieval

This paper addresses the blog distillation problem, that is, given a user query find the blogs that are most related to the query topic. We model each post as evidence of the relevance of a blog to the query, and use aggregation methods like Ordered Weighted Averaging (OWA) operators to combine the evidence. We show that using only highly relevant evidence (posts) for each blog can result in an effective retrieval system. We also take into account the importance of the posts in a query-based cluster and investigate its effect in the aggregation results. We use prioritized OWA operators and show that considering the importance is effective when the number of aggregated posts from each blog is high. We carry out our experiments on three different data sets (TREC07, TREC08 and TREC09) and show statistically significant improvements over state of the art model called voting model.     

图情与互联网博客交流网络实证分析

作为Web2.0环境下学术交流的有利工具,博客得到了广泛的应用,图情领域的许多专家、学者也建立了自己的博客。本文利用社会网络分析法,对50个图书情报学博客和互联网博客构成的学术交流网络进行了定量分析,从中心度、网络密度、凝聚子群分析、小世界效应等角度对该网络进行了实证分析,并比较了两个专业圈子的不同结构特征和影响力,以期从完善结构的角度,促进科研人员的学术交流。     

Multimedia search capabilities of Chinese language search engines

This paper reports results from a study exploring the multimedia search functionality of Chinese language search engines. Web searching in Chinese (Mandarin) is a growing research area and a technical challenge for popular commercial Web search engines. Few studies have been conducted on Chinese language search engines. We investigate two research questions: which Chinese language search engines provide multimedia searching, and what multimedia search functionalities are available in Chinese language Web search engines. Specifically, we examine each Web search engine’s (1) features permitting Chinese language multimedia searches, (2) extent of search personalization and user control of multimedia search variables, and (3) the relationships between Web search engines and their features in the Chinese context. Key findings show that Chinese language Web search engines offer limited multimedia search functionality, and general search engines provide a wider range of features than specialized multimedia search engines. Study results have implications for Chinese Web users, Website designers and Web search engine developers.     

A heuristic hierarchical scheme for academic search and retrieval

We present PubSearch, a hybrid heuristic scheme for re-ranking academic papers retrieved from standard digital libraries such as the ACM Portal. The scheme is based on the hierarchical combination of a custom implementation of the term frequency heuristic, a time-depreciated citation score and a graph-theoretic computed score that relates the paper’s index terms with each other. We designed and developed a meta-search engine that submits user queries to standard digital repositories of academic publications and re-ranks the repository results using the hierarchical heuristic scheme. We evaluate our proposed re-ranking scheme via user feedback against the results of ACM Portal on a total of 58 different user queries specified from 15 different users. The results show that our proposed scheme significantly outperforms ACM Portal in terms of retrieval precision as measured by most common metrics in Information Retrieval including Normalized Discounted Cumulative Gain (NDCG), Expected Reciprocal Rank (ERR) as well as a newly introduced lexicographic rule (LEX) of ranking search results. In particular, PubSearch outperforms ACM Portal by more than 77% in terms of ERR, by more than 11% in terms of NDCG, and by more than 907.5% in terms of LEX. We also re-rank the top-10 results of a subset of the original 58 user queries produced by Google Scholar, Microsoft Academic Search, and ArnetMiner; the results show that PubSearch compares very well against these search engines as well. The proposed scheme can be easily plugged in any existing search engine for retrieval of academic publications.     

Understanding customer experiences in online blog environments

Blogs have recently become an influential medium and have demonstrated enormous marketing power. Consumers can freely conduct ongoing information searches through this new channel. However, the credibility of blogs plays an important role in creating opportunities for positive customer experiences that can shape consumers’ product/service purchase intentions and decisions. In light of this observation, this study proposes a theoretical framework that delineates the relationship among information credibility, customer experiences, and purchase intention in the blog environment. Data collected from 468 subjects in specific corporate blogs provide support for the proposed model using partial least squares (PLS). The results indicate that information credibility is critical for facilitating customer experiences, which, in turn, is necessary to enhance purchase intention. Additionally, greater involvement with blog significantly increases the effect of customer experiences on purchase intention. The detailed theoretical and managerial implications are presented.     

Search engines and the public use of reason

How should the policies of search engines and other information intermediaries be ethically evaluated? It is argued that Kant’s principles for the public use of reason are useful starting points for the formulation of criteria for such an evaluation. The suggestion is, furthermore, that a search engine can be seen to provide a testimony to the user concerning what information that is most relevant to her query. This suggestion is used as the basis for the development of a broadly Kantian account of a rational searcher. It is argued that the search engine companies are morally required to publish their information policies and act in accordance with them but given the threat of search engine spam they can have no obligation to publish the details of their algorithms.     

国外网络搜索引擎优秀资源现状述评——搜索引擎网站、论坛、新闻和学术会议资源

从优秀搜索引擎、搜索引擎网站、搜索引擎论坛、搜索引擎新闻和搜索引擎会议等方面入手,对国外现有论述搜索引擎的主要资源、种类、性能和特色进行了述评.在此基础上,推荐有关搜索引擎的最佳资源,为人们学习掌握搜索引擎的资源、搜索技巧、方法和优化检索策略提供参考资料和学习途径.     

A study of results overlap and uniqueness among major Web search engines

The performance and capabilities of Web search engines is an important and significant area of research. Millions of people world wide use Web search engines very day. This paper reports the results of a major study examining the overlap among results retrieved by multiple Web search engines for a large set of more than 10,000 queries. Previous smaller studies have discussed a lack of overlap in results returned by Web search engines for the same queries. The goal of the current study was to conduct a large-scale study to measure the overlap of search results on the first result page (both non-sponsored and sponsored) across the four most popular Web search engines, at specific points in time using a large number of queries. The Web search engines included in the study were MSN Search, Google, Yahoo! and Ask Jeeves. Our study then compares these results with the first page results retrieved for the same queries by the metasearch engine Dogpile.com. Two sets of randomly selected user-entered queries, one set was 10,316 queries and the other 12,570 queries, from Infospace’s Dogpile.com search engine (the first set was from Dogpile, the second was from across the Infospace Network of search properties were submitted to the four single Web search engines). Findings show that the percent of total results unique to only one of the four Web search engines was 84.9%, shared by two of the three Web search engines was 11.4%, shared by three of the Web search engines was 2.6%, and shared by all four Web search engines was 1.1%. This small degree of overlap shows the significant difference in the way major Web search engines retrieve and rank results in response to given queries. Results point to the value of metasearch engines in Web retrieval to overcome the biases of individual search engines.     

Web2.0环境下图书情报学领域的非正式交流——基于博客好友链接的实证研究

Web2.0环境下博客在科学信息的非正式交流中扮演着越来越重要的角色。对Web2.0以及非正式科学交流进行了概述,用社会网络分析法对收集到的图书情报领域专业博客的链接数据进行处理以及详细的分析,发现本领域博客网络小世界特征明显,但博客间的联系较为松散。     

Opinion spam detection: Using multi-iterative graph-based model

The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called “Multi-iterative Graph-based opinion Spam Detection” (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entities’) relationships. The MGSD model is able to evaluate the ‘spamicity’ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset.     

国外网络搜索引擎最佳资源现状述评(1)——桌面搜索工具、搜索引擎指南、目录和论著资源述评

从桌面搜索工具、搜索引擎指南、目录和论著资源等方面入手,对国外现有论述搜索引擎的主要资源的种类、性能和特色进行了述评。在此基础上,推荐有关搜索引擎的最佳资源,为人们学习掌握搜索引擎的资源、搜索技巧、方法和优化检索策略提供参考资料和学习途径。     

Learning to recognize webpage genres

Webpages are mainly distinguished by their topic (e.g., politics, sports etc.) and genre (e.g., blogs, homepages, e-shops, etc.). Automatic detection of webpage genre could considerably enhance the ability of modern search engines to focus on the requirements of the user’s information need. In this paper, we present an approach to webpage genre detection based on a fully-automated extraction of the feature set that represents the style of webpages. The features we propose (character n-grams of variable length and HTML tags) are language-independent and easily-extracted while they can be adapted to the properties of the still evolving web genres and the noisy environment of the web. Experiments based on two publicly-available corpora show that the performance of the proposed approach is superior in comparison to previously reported results. It is also shown that character n-grams are better features than words when the dimensionality increases while the binary representation is more effective than the term-frequency representation for both feature types. Moreover, we perform a series of cross-check experiments (e.g., training using a genre palette and testing using a different genre palette as well as using the features extracted from one corpus to discriminate the genres of the other corpus) to illustrate the robustness of our approach and its ability to capture the general stylistic properties of genre categories even when the feature set is not optimized for the given corpus.     

Comparing rankings of search results on the Web

The Web has become an information source for professional data gathering. Because of the vast amounts of information on almost all topics, one cannot systematically go over the whole set of results, and therefore must rely on the ordering of the results by the search engine. It is well known that search engines on the Web have low overlap in terms of coverage. In this study we measure how similar are the rankings of search engines on the overlapping results.We compare rankings of results for identical queries retrieved from several search engines. The method is based only on the set of URLs that appear in the answer sets of the engines being compared. For comparing the similarity of rankings of two search engines, the Spearman correlation coefficient is computed. When comparing more than two sets Kendall’s W is used. These are well-known measures and the statistical significance of the results can be computed. The methods are demonstrated on a set of 15 queries that were submitted to four large Web search engines. The findings indicate that the large public search engines on the Web employ considerably different ranking algorithms.     

The influence of task and gender on search and evaluation behavior using Google

To improve search engine effectiveness, we have observed an increased interest in gathering additional feedback about users’ information needs that goes beyond the queries they type in. Adaptive search engines use explicit and implicit feedback indicators to model users or search tasks. In order to create appropriate models, it is essential to understand how users interact with search engines, including the determining factors of their actions. Using eye tracking, we extend this understanding by analyzing the sequences and patterns with which users evaluate query result returned to them when using Google. We find that the query result abstracts are viewed in the order of their ranking in only about one fifth of the cases, and only an average of about three abstracts per result page are viewed at all. We also compare search behavior variability with respect to different classes of users and different classes of search tasks to reveal whether user models or task models may be greater predictors of behavior. We discover that gender and task significantly influence different kinds of search behaviors discussed here. The results are suggestive of improvements to query-based search interface designs with respect to both their use of space and workflow.