Search (43 results, page 1 of 3)

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Abstract

This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.

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Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker

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Annual review of information science and technology. 45(2011) no.1, S.121-155

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Abstract

When it comes to evaluating online information experiences, what metrics matter? We conducted a study in which 30 people browsed and selected content within an online news website. Data collected included psychometric scales (User Engagement, Cognitive Absorption, System Usability Scales), self-reported interest in news content, and performance metrics (i.e., reading time, browsing time, total time, number of pages visited, and use of recommended links); a subset of the participants had their physiological responses recorded during the interaction (i.e., heart rate, electrodermal activity, electrocmytogram). Findings demonstrated the concurrent validity of the psychometric scales and interest ratings and revealed that increased time on tasks, number of pages visited, and use of recommended links were not necessarily indicative of greater self-reported engagement, cognitive absorption, or perceived usability. Positive ratings of news content were associated with lower physiological activity. The implications of this research are twofold. First, we propose that user experience is a useful framework for studying online information interactions and will result in a broader conceptualization of information interaction and its evaluation. Second, we advocate a mixed-methods approach to measurement that employs a suite of metrics capable of capturing the pragmatic (e.g., usability) and hedonic (e.g., fun, engagement) aspects of information interactions. We underscore the importance of using multiple measures in information research, because our results emphasize that performance and physiological data must be interpreted in the context of users' subjective experiences.

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Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1543-1556

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Everything changes, everything stays the same? - Understanding information spaces : Proceedings of the 15th International Symposium of Information Science (ISI 2017), Berlin/Germany, 13th - 15th March 2017. Eds.: M. Gäde, V. Trkulja u. V. Petras

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Abstract

Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.

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Journal of the American Society for Information Science and Technology. 62(2011) no.4, S.727-737

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Abstract

In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.

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Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.

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Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1597-1614

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Grundlagen der praktischen Information und Dokumentation. Handbuch zur Einführung in die Informationswissenschaft und -praxis. 6., völlig neu gefaßte Ausgabe. Hrsg. von R. Kuhlen, W. Semar u. D. Strauch. Begründet von Klaus Laisiepen, Ernst Lutterbeck, Karl-Heinrich Meyer-Uhlenried

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Abstract

An organization performing environmental scanning generally monitors or tracks various events concerning its external environment. One of the major resources for environmental scanning is online news documents, which are readily accessible on news websites or infomediaries. However, the proliferation of the World Wide Web, which increases information sources and improves information circulation, has vastly expanded the amount of information to be scanned. Thus, it is essential to develop an effective event episode discovery mechanism to organize news documents pertaining to an event of interest. In this study, we propose two new metrics, Term Frequency × Inverse Document FrequencyTempo (TF×IDFTempo) and TF×Enhanced-IDFTempo, and develop a temporal-based event episode discovery (TEED) technique that uses the proposed metrics for feature selection and document representation. Using a traditional TF×IDF-based hierarchical agglomerative clustering technique as a performance benchmark, our empirical evaluation reveals that the proposed TEED technique outperforms its benchmark, as measured by cluster recall and cluster precision. In addition, the use of TF×Enhanced-IDFTempo significantly improves the effectiveness of event episode discovery when compared with the use of TF×IDFTempo.

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Journal of the Association for Information Science and Technology. 65(2014) no.3, S.621-634

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Abstract

With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.

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Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1170-1186

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Abstract

Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.

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Journal of the Association for Information Science and Technology. 68(2017) no.4, S.811-829

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Abstract

We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.

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Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119

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Abstract

Using Google Earth, Google Maps, and/or network visualization programs such as Pajek, one can overlay the network of relations among addresses in scientific publications onto the geographic map. The authors discuss the pros and cons of various options, and provide software (freeware) for bridging existing gaps between the Science Citation Indices (Thomson Reuters) and Scopus (Elsevier), on the one hand, and these various visualization tools on the other. At the level of city names, the global map can be drawn reliably on the basis of the available address information. At the level of the names of organizations and institutes, there are problems of unification both in the ISI databases and with Scopus. Pajek enables a combination of visualization and statistical analysis, whereas the Google Maps and its derivatives provide superior tools on the Internet.

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Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1622-1634

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Abstract

Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.

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Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a

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Abstract

With the development of Web2.0, social tagging systems in which users can freely choose tags to annotate resources according to their interests have attracted much attention. In particular, literature on the emergence of collective intelligence in social tagging systems has increased. In this article, we propose a probabilistic generative model to detect latent topical communities among users. Social tags and resource contents are leveraged to model user interest in two similar and correlated ways. Our primary goal is to capture user tagging behavior and interest and discover the emergent topical community structure. The communities should be groups of users with frequent social interactions as well as similar topical interests, which would have important research implications for personalized information services. Experimental results on two real social tagging data sets with different genres have shown that the proposed generative model more accurately models user interest and detects high-quality and meaningful topical communities.

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Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.321-333

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Abstract

Text mining and machine learning methodologies have been applied toward knowledge discovery in several domains, such as biomedicine and business. Interestingly, in the business domain, the text mining and machine learning community has minimally explored company annual reports with their mandatory disclosures. In this study, we explore the question "How can annual reports be used to predict change in company performance from one year to the next?" from a text mining perspective. Our article contributes a systematic study of the potential of company mandatory disclosures using a computational viewpoint in the following aspects: (a) We characterize our research problem along distinct dimensions to gain a reasonably comprehensive understanding of the capacity of supervised learning methods in predicting change in company performance using annual reports, and (b) our findings from unbiased systematic experiments provide further evidence about the economic incentives faced by analysts in their stock recommendations and speculations on analysts having access to more information in producing earnings forecast.

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Journal of the Association for Information Science and Technology. 65(2014) no.2, S.400-413

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Abstract

Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.

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Journal of the Association for Information Science and Technology. 69(2018) no.7, S.879-889

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Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,

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Abstract

Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.

Source

Journal of the American Society for Information Science and Technology. 64(2013) no.7, S.1399-1410

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Abstract

Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.

Source

Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22