The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have effective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their effectiveness in a variety of context on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of “authorative” information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of “hub pages” that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristrics for link-based analysis.

/pdf/authoritative-sources-in-a-hyperlinked-environment-1p23r698i9.pdf

Authoritative sources in a hyperlinked environment

An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

Evaluating scientific quality is a notoriously difficult problem which has no standard solution. Ideally, published scientific results should be scrutinised by true experts in the field and given scores for quality and quantity according to established rules. In practice, however, what is called peer review is usually performed by committees with general competence rather than with the specialist's insight that is needed to assess primary research data. Committees tend, therefore, to resort to secondary criteria like crude publication counts, journal prestige, the reputation of authors and institutions, and estimated importance and relevance of the research field,1 making peer review as much of a lottery as of a rational process.2 3

On this background, it is hardly surprising that alternative methods for evaluating research are being sought, such as citation rates and journal impact factors, which seem to be quantitative and objective indicators directly related to published science. The citation data are obtained from a database produced by the Institute for Scientific Information (ISI) in Philadelphia, which continuously records scientific citations as represented by the reference lists of articles from a large number of the world's scientific journals. The references are rearranged in the database to show how many times each publication has been cited within a certain period, and by whom, and the results are published as the Science Citation Index (SCI) . On the basis of the Science Citation Index and authors' publication lists, the annual citation rate of papers by a scientific author or research group can thus be calculated. Similarly, the citation rate of a scientific journal—known as the journal impact factor—can be calculated as the mean citation rate of all the articles contained in the journal.4 Journal impact factors, which are published annually in SCI Journal Citation Reports , are widely regarded as …

/pdf/why-the-impact-factor-of-journals-should-not-be-used-for-2rame3warm.pdf

Why the impact factor of journals should not be used for evaluating research

Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in partic...

/pdf/social-network-analysis-a-powerful-strategy-also-for-the-1v0viv3lp8.pdf

Social network analysis: a powerful strategy, also for the information sciences:

The network structure of a hyperlinked environment can be a rich source of information about the content of the environment, provided we have eective means for understanding it. We develop a set of algorithmic tools for extracting information from the link structures of such environments, and report on experiments that demonstrate their eectiveness in a variety of contexts on the World Wide Web. The central issue we address within our framework is the distillation of broad search topics, through the discovery of \authoritative" information sources on such topics. We propose and test an algorithmic formulation of the notion of authority, based on the relationship between a set of relevant authoritative pages and the set of \hub pages" that join them together in the link structure. Our formulation has connections to the eigenvectors of certain matrices associated with the link graph; these connections in turn motivate additional heuristics for link-based analysis.

http://delab.csd.auth.gr/~dimitris/courses/ir_spring07/papers/JACM_46_5_1999_Authoritative%20sources%20in%20a%20hyperlinked%20environment.pdf

A self-consistent methodology is developed for determining citation based influence measures for scientific journals, subfields and fields. Starting with the cross citing matrix between journals or between aggregates of journals, an eigenvalue problem is formulated leading to a size independent influence weight for each journal or aggregate. Two other measures, the influence per publication and the total influence are then defined. Hierarchical influence diagrams and numerical data are presented to display journal interrelationships for journals within the subfields of physics. A wide range in influence is found between the most influential and least influential or peripheral journals.

/pdf/citation-influence-for-journal-aggregates-of-scientific-2p262y4g8h.pdf

Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics

The structure and interrelations of the biomedical journal literature are investigated as a preparatory step for studies of biomedical research activity. Using newly developed methods of bibliographic citation analysis, approximately 900 biomedical journals are classified into approximately 50 separate fields and into four research levels. The research-level scale indicates research orientation ranging from clinical observation to basic research. Measures of influence are then obtained for individual journals, for biomedical fields and for research levels. The fields of biochemistry and physiology are shown to have the highest citation influence.



A hierarchical influence diagram is presented to display the influence of 42 fields within biomedicine. Hierarchical influence diagrams are also presented for several individual fields showing the influence structure and citation relationships among their component journals. The combination of a subject, a level and an influence measure provides a unified framework for planned research activity analysis.

Structure of the Biomedical Literature

The structure and interrelations of the psychological journal literature are investigated as a preparatory step for studies of psychological research activity. Newly developed techniques of bibliographic citation analysis are used to construct influence measures for individual journals and for the subfields of psychology into which the journals have been classified. Hierarchical influence diagrams are presented to display the influence and interrelations of the psychological subfields and of the individual journals themselves.

Gabriel Pinski

Papers

Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics

Structure of the Biomedical Literature

Structure of the psychological literature