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We constructed a corpus of digitized texts containing about 4% of all books ever printed. Analysis of this corpus enables us to investigate cultural trends quantitatively. We survey the vast terrain of 'culturomics,' focusing on linguistic and cultural phenomena that were reflected in the English language between 1800 and 2000. We show how this approach can provide insights about fields as diverse as lexicography, the evolution of grammar, collective memory, the adoption of technology, the pursuit of fame, censorship, and historical epidemiology. Culturomics extends the boundaries of rigorous quantitative inquiry to a wide array of new phenomena spanning the social sciences and the humanities.

Purpose - The purpose of this paper is to investigate the linking of a folksonomy (user vocabulary) and LCSH (controlled vocabulary) on the basis of word matching, for the potential use of LCSH in bringing order to folksonomies.Design methodology approach - A selected sample of a folksonomy from a popular collaborative tagging system, Delicious, was word-matched with LCSH. LCSH was transformed into a tree structure called an LCSH tree for the matching. A close examination was conducted on the characteristics of folksonomies, the overlap of folksonomies with LCSH, and the distribution of folksonomies over the LCSH tree.Findings - The experimental results showed that the total proportion of tags being matched with LC subject headings constituted approximately two-thirds of all tags involved, with an additional 10 percent of the remaining tags having potential matches. A number of barriers for the linking as well as two areas in need of improving the matching are identified and described. Three important tag distribution patterns over the LCSH tree were identified and supported: skewedness, multifacet, and Zipfian-pattern.Research limitations implications - The results of the study can be adopted for the development of innovative methods of mapping between folksonomy and LCSH, which directly contributes to effective access and retrieval of tagged web resources and to the integration of multiple information repositories based on the two vocabularies.Practical implications - The linking of controlled vocabularies can be applicable to enhance information retrieval capability within collaborative tagging systems as well as across various tagging system information depositories and bibliographic databases.Originality value - This is among frontier works that examines the potential of linking a folksonomy, extracted from a collaborative tagging system, to an authority-maintained subject heading system. It provides exploratory data to support further advanced mapping methods for linking the two vocabularies.

Purpose - The purpose of this paper is to establish pluralism as the basis for bibliographic classification theory and practice and examine the possibility of establishing trustworthy classifications.Design methodology approach - The paper examines several key notions in classification and extends previous frameworks by combining an explanation-based approach to classification with the concepts of cognitive authority and trust.Findings - The paper presents an understanding of classification that allows designers and editors to establish trust through the principle of transparency. It demonstrates that modern classification theory and practice are tied to users' activities and domains of knowledge and that trustworthy classification systems are in close dialogue with users to handle bias responsible and establish trust.Originality value - The paper establishes a foundation for exploring trust and authority for classification systems.

Purpose – The purpose of this conceptual paper is to consider the possibility of designing a story-based image indexing system based on users’ descriptions of images. It reports a pilot study which uses users’ descriptions of two images. Design/methodology/approach – Eight interviews were undertaken to investigate storytelling in user interpretations of the images. Following this, storytelling was explored as an indexing input method. In all, 26 research subjects were asked to create stories about the images, which were then considered in relation to conventional story elements and in relation to Hidderley and Rafferty's (2005) image modality model. Findings – The results of the semi-structured interviews revealed that the majority of interpretations incorporated story elements related to setting, character, plot, literary devices, and themes. The 52 image stories included story elements identified in the first part of the project, and suggested that the image modality model is robust enough to deal with the “writerly” images used in this study. In addition, using storytelling as an input method encourages the use of verbs and connotative level responses. Originality/value – User indexing is generally based on paradigmatic approaches to concept analysis and interpretation in the form of tagging; the novelty of the current study is its exploration of syntagmatic approaches to user indexing in the form of storytelling. It is a pilot, proof of concept study, but it is hoped that it might stimulate further interest in syntagmatic approaches to user indexing.

In recent years graph-ranking based algorithms have been proposed for single document summarization and generic multi-document summarization. The algorithms make use of the “votings” or “recommendations” between sentences to evaluate the importance of the sentences in the documents. This study aims to differentiate the cross-document and within-document relationships between sentences for generic multi-document summarization and adapt the graph-ranking based algorithm for topic-focused summarization. The contributions of this study are two-fold: (1) For generic multi-document summarization, we apply the graph-based ranking algorithm based on each kind of sentence relationship and explore their relative importance for summarization performance. (2) For topic-focused multi-document summarization, we propose to integrate the relevance of the sentences to the specified topic into the graph-ranking based method. Each individual kind of sentence relationship is also differentiated and investigated in the algorithm. Experimental results on DUC 2002–DUC 2005 data demonstrate the great importance of the cross-document relationships between sentences for both generic and topic-focused multi-document summarizations. Even the approach based only on the cross-document relationships can perform better than or at least as well as the approaches based on both kinds of relationships between sentences.

Sentiment analysis, also called opinion mining, is a form of information extraction from text of growing research and commercial interest. In this paper we present our machine learning experiments with regard to sentiment analysis in blog, review and forum texts found on the World Wide Web and written in English, Dutch and French. We train from a set of example sentences or statements that are manually annotated as positive, negative or neutral with regard to a certain entity. We are interested in the feelings that people express with regard to certain consumption products. We learn and evaluate several classification models that can be configured in a cascaded pipeline. We have to deal with several problems, being the noisy character of the input texts, the attribution of the sentiment to a particular entity and the small size of the training set. We succeed to identify positive, negative and neutral feelings to the entity under consideration with ca. 83% accuracy for English texts based on unigram features augmented with linguistic features. The accuracy results of processing the Dutch and French texts are ca. 70 and 68% respectively due to the larger variety of the linguistic expressions that more often diverge from standard language, thus demanding more training patterns. In addition, our experiments give us insights into the portability of the learned models across domains and languages. A substantial part of the article investigates the role of active learning techniques for reducing the number of examples to be manually annotated.

Purpose - This paper aims to respond to the 2005 paper by Hjørland and Nissen Pedersen by suggesting that an exhaustive and universal classification of the phenomena that scholars study, and the methods and theories they apply, is feasible. It seeks to argue that such a classification is critical for interdisciplinary scholarship.Design methodology approach - The paper presents a literature-based conceptual analysis, taking Hjørland and Nissen Pedersen as its starting point. Hjørland and Nissen Pedersen had identified several difficulties that would be encountered in developing such a classification; the paper suggests how each of these can be overcome. It also urges a deductive approach as complementary to the inductive approach recommended by Hjørland and Nissen Pedersen.Findings - The paper finds that an exhaustive and universal classification of scholarly documents in terms of (at least) the phenomena that scholars study, and the theories and methods they apply, appears to be both possible and desirable.Practical implications - The paper suggests how such a project can be begun. In particular it stresses the importance of classifying documents in terms of causal links between phenomena.Originality value - The paper links the information science, interdisciplinary, and study of science literatures, and suggests that the types of classification outlined above would be of great value to scientists scholars, and that they are possible.

Purpose - This paper aims to examine how systems for organizing information construct rhetorical arguments for a particular interpretation of their subject matter.Design methodology approach - The paper synthesizes a conceptual framework from the field of rhetoric and uses that framework to analyze how existing organizational schemes present evidence in support of arguments regarding the material being organized.Findings - Organizational schemes can present logical arguments as posed in rhetoric, using two forms of evidence for their claims: relationship evidence from the category structure and resource evidence from the ways that items are assigned to categories.Research limitations implications - This study does not attempt to identify all types of evidence that organizational schemes might use in argumentation. Further research may describe additional forms of evidence and argumentative structures.Practical implications - When creating organizational schemes, designers might develop a strategy to facilitate persuasive argumentation. Moreover, because arguments may be either strengthened or undermined through the assignment of resources to categories, both indexing and collection development may be seen as contributing to the overall design of an organizational scheme.Originality value - While many researchers have asserted that organizational schemes form arguments, and while various studies have described what information systems might be said to communicate, this study focuses on how such communication may take place more or less effectively. This analysis foregrounds the potential for organizational schemes to be systematically and purposefully designed as rhetorical communication, to express particular ideas.

Purpose - Social tagging systems allow users to assign keywords (tags) to useful resources, facilitating their future access by the tag creator and possibly by other users. Social tagging has both proponents and critics, and this paper aims to investigate if tags are an effective means of resource discovery.Design methodology approach - The paper adopts techniques from text categorisation in which webpages and their associated tags from del.icio.us and trained Support Vector Machine (SVM) classifiers are downloaded to determine if the documents could be assigned to their associated tags. Two text categorisation experiments were conducted. The first used only the terms from the documents as features while the second experiment included tags in addition to terms as part of its feature set. Performance metrics used were precision, recall, accuracy and F1 score. A content analysis was also conducted to uncover characteristics of effective and ineffective tags for resource discovery.Findings - Results from the classifiers were mixed, and the inclusion of tags as part of the feature set did not result in a statistically significant improvement (or degradation) of the performance of the SVM classifiers. This suggests that not all tags can be used for resource discovery by public users, confirming earlier work that there are many dynamic reasons for tagging documents that may not be apparent to others.Originality value - The authors extend their understanding of social classification and its utility in sharing and accessing resources. Results of this work may be used to guide development in social tagging systems as well as social tagging practices.