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Metadata onlyBroad issue scanning is the task of identifying important public debates arising in a given broad issue; really simple syndication (RSS) feeds are a natural information source for investigating broad issues. RSS, as originally conceived, is a method for publishing timely and concise information on the Internet, for example, about the main stories in a news site or the latest postings in a blog. RSS feeds are potentially a nonintrusive source of high-quality data about public opinion: Monitoring a large number may allow quantitative methods to extract information relevant to a given need. In this article we describe an RSS feed-based coword frequency method to identify bursts of discussion relevant to a given broad issue. A case study of public science concerns is used to demonstrate the method and assess the suitability of raw RSS feeds for broad issue scanning (i.e., without data cleansing). An attempt to identify genuine science concern debates from the corpus through investigating the top 1,000 burst words found only two genuine debates, however. The low success rate was mainly caused by a few pathological feeds that dominated the results and obscured any significant debates. The results point to the need to develop effective data cleansing procedures for RSS feeds, particularly if there is not a large quantity of discussion about the broad issue, and a range of potential techniques is suggested. Finally, the analysis confirmed that the time series information generated by real-time monitoring of RSS feeds could usefully illustrate the evolution of new debates relevant to a broad issue

A feature of modern democracies is public mistrust of scientists and the politicization of science policy, e.g., concerning stem cell research and genetically modified food. While the extent of this mistrust is debatable, its political influence is tangible. Hence, science policy researchers and science policy makers need early warning of issues that resonate with a wide public so that they can make timely and informed decisions. In this article, a semi-automatic method for identifying significant public science-related concerns from a corpus of Internet-based RSS (Really Simple Syndication) feeds is described and shown to be an improvement on a previous similar system because of the introduction of feedbased aggregation. In addition, both the RSS corpus and the concept of public science-related fears are deconstructed, revealing hidden complexity. This article also provides evidence that genetically modified organisms and stem cell research were the two major policyrelevant science concern issues, although mobile phone radiation and software security also generated significant interest. IntroductionPublic mistrust of science has probably existed as long as science itself. Historical manifestations have varied from popular culture such as Mary Shelley's Frankenstein and H. G. Wells' The Island of Doctor Moreau (Pinsky, 2003;Wilt, 2003;Wolpert, 2005), to mass antinuclear power movements (Herring, 2006;Hsu, 2005). In recent years, many scientific issues have become politicised and have given rise to pressure groups, media coverage, and public debates. Two of the most prominent have been stem cell research and genetically modified food, both of which have triggered significant social sciences and ethics research (Hagendijk, 2004;Tait, 2001;Tsai, 2005). The politicization of science debates has lead to government policy and legislation curtailing researchers' activities in response to public pressure. The importance of this is potentially great (Leydesdorff & Etzkowitz, 2003). Given the key role of research within a profitable modern knowledge-based economy (Etzkowitz & Leydesdorff, 1997;Gibbons, 1994), for example, in the biotechnology and computing industries, falling behind with a newly emerging technology is a national potential disaster. Conversely, allowing science to continue "unchecked" (except for the normal self-regulation process) may cost humanity too high a price if the critics are correct (Chadwick, 2005;London, 2005). In consequence, a large amount of research has been devoted to topics such as the sociology and ethics of individual science policy debates (Klotzko, 2004). It is hence particularly important to be able to identify the next significant science concern debate as early as possible so that research into the social, ethical, legal, and policy implications can begin and support politicians to make wellinformed, timely decisions. The research reported here is part of an international European Union-funded project (www.creen.org) that aims to develop automatic methods to help identify critical p...

In recent years, we have witnessed the continual growth in the use of ontologies in order to provide a mechanism to enable machine reasoning. This paper describes an automatic classifier, which focuses on the use of ontologies for classifying Web pages with respect to the Dewey Decimal Classification (DDC) and Library of Congress Classification (LCC) schemes. Firstly, we explain how these ontologies can be built in a modular fashion, and mapped into DDC and LCC. Secondly, we propose the formal definition of a DDC-LCC and an ontology-classification-scheme mapping. Thirdly, we explain the way the classifier uses these ontologies to assist classification. Finally, an experiment in which the accuracy of the classifier was evaluated is presented. The experiment shows that our approach results an improved classification in terms of accuracy. This improvement, however, comes at a cost in a low overage ratio due to the incompleteness of the ontologies used. c

Purpose -The aim of this paper is to analyse the structure of evolving debates in online discussion forums to see how science-related debates evolve over time. Design/methodology/approach -A graph-based approach is applied to analyse the structure of graphs of connected terms in online debates. A number of different graph properties, such as the Densification Power Law (DPL), diameter (g) and effective diameter (d), are used to observe the properties of the graphs over time. Findings -The graphs of connected terms obey the DPL and the effective diameters (d) of the graphs tend to shrink as the debates progress. Slight fluctuations can occur, however, when new terms are integrated into the graphs. These two properties suggest that a graph of connected terms can be modelled through a number of blocks of terms, each of which becomes densely connected over time as indicated by d and DPL plots. Originality/value -This paper proposes observing the dynamic changes of evolving debates by using graphs of connected terms. The structures and properties of these graphs may be useful for understanding the evolution of public debates about controversial science-related topics, such as embryonic stem cell research, and to track debates that can potentially explode into major issues.

Previous researchers have attempted to detect significant topics in news stories and blogs through the use of word frequency-based methods applied to RSS feeds. In this paper, the three statistical feature selection methods: χ 2 , Mutual Information (MI) and Information Gain (I) are proposed as alternative approaches for ranking term significance in an evolving RSS feed corpus. The extent to which the three methods agree with each other on determining the degree of the significance of a term on a certain date is investigated as well as the assumption that larger values tend to indicate more significant terms. An experimental evaluation was carried out with 39 different levels of data reduction to evaluate the three methods for differing degrees of significance. The three methods showed a significant degree of disagreement for a number of terms assigned an extremely large value. Hence, the assumption that the larger a value, the higher the degree of the significance of a term should be treated cautiously. Moreover, MI and I show significant disagreement. This suggests that MI is different in the way it ranks significant terms, as MI does not take the absence of a term into account, although I does. I, however, has a higher degree of term reduction than MI and χ 2 . This can result in loosing some significant terms. In summary, χ 2 seems to be the best method to determine term significance for RSS feeds, as χ 2 identifies both types of significant behavior. The χ 2 method, however, is far from perfect as an extremely high value can be assigned to relatively insignificant terms.

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Recommendation System is able to help users to choose items, including movies, that match their interests. One of the problems faced by recommendation system is cold-start problem. Cold start problem can be categorized into three types, they are: recommending existed item for new user, recommending new item for existed user, and recommending new item for new user. Pairwise preference regression is a method that directly deals with cold-start problem. This method can suggest a recommendation, not only for users who have no historical rating, but also for those who only have less demographic info. From the experiment result, the best score of Normalized Discounted Cumulative Gain (nDGC) from the system is 0.8484. The standard deviation of rating resulted by the recommendation system is 1.24, the average is 3.82. Consequently, the distribution of recommendation result is around rating 5 to 3. Those results mean that this recommendation system is good to solving cold-start problem in movie recommendation system.