Summary Astroturfing is appearing in numerous contexts in social media, with individuals posting product reviews or political commentary under a number of different names, and is of concern because of the intended deception. An astroturfer works with the aim of making it seem that a large number of people hold the same opinion, promoting a consensus based on the astroturfer's intentions. It is generally done for commercial or political advantage, often by paid writers or ideologically motivated writers. This paper brings the notion of authorship attribution to bear on the astroturfing problem, collecting quantities of data from public social media sites and analyzing the putative individual authors to see if they appear to be the same person. The analysis comprises a binaryn‐gram method, which was previously shown to be effective at accurately identifying authors on a training set from the same authors, while this paper shows how authors on different social media turn out to be the same author. The method has identified numerous instances where multiple accounts are apparently being operated by a single individual.

Protecting Web applications is increasingly important due to their high popularity and wide adoption. Therefore, a multitude of protection techniques emerged in effort to secure Web applications, specifically considering valuable and private data commonly processed by such applications. Based on an overview of currently existing protection techniques, a generic and extensible PHP-oriented protection framework is proposed. The concept of application developer intent is introduced and compared with other concepts such as enforced security policies commonly used in existing protection approaches. The proposed framework is mainly focused on application developer intention understanding. Supervising the application execution in real-time makes it possible to detect deviations from the intended behavior and prevent potentially malicious activity. The additional aspects of application behavior, such as database-related communications or generated Web page structure, can be analyzed due to the extensible architecture of the framework.

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web-based services, physical things are becoming an integral part of the emerging ubiquitous Web. In this paper, we focus on the things recommendation problem in Internet of Things (IoT). In particular, we propose a unified probabilistic based framework by fusing information across relationships between users (i.e., users'social network) and things (i.e., things correlations) to make more accurate recommendations. The proposed approach not only inherits the advantages of the matrix factorization, but also exploits the merits of social relationships and thing-thing correlations. We validate our approach based on an Internet of Things platform and the experimental results demonstrate its feasibility and effectiveness.

The interactions of users with search engines can be seen as implicit relevance feedback by the user on the results offered to them. In particular, the selection of results by users can be interpreted as a confirmation of the relevance of those results, and used to reorder or prioritize subsequent search results. This collection of search/result pairings is called clickthrough data, and many uses for it have been proposed. However, the reliability of clickthrough data has been challenged and it has been suggested that clickthrough data are not a completely accurate measure of relevance between search term and results. This paper reports on an experiment evaluating the reliability of clickthrough data as a measure of the mutual relevance of search term and result. The experiment comprised a user study involving over 67 participants and determines the reliability of image search clickthrough data, using factors identified in previous similar studies. A major difference in this work to previous work is that the source of clickthrough data comes from image searches, rather than the traditional text page searches. Image search clickthrough data were rarely examined in prior works but has differences that impact the accuracy of clickthrough data. These differences include a more complete representation of the results in image search, allowing users to scrutinize the results more closely before selecting them, as well as presenting the results in a less obviously ordered way. The experiment reported here demonstrates that image clickthrough data can be more reliable as a relevance feedback measure than has been the case with traditional text‐based search. There is also evidence that the precision of the search system influences the accuracy of click data when users make searches in an information‐seeking capacity.

No abstract available.

Previous work has established that search engine queries can be classified according to the intent of the searcher (i.e., why is the user searching, what specifically do they intend to do). In this article, we describe an experiment in which four sets of queries, each set representing a different user intent, are repeatedly submitted to three search engines over a period of 60 days. Using a variety of measurements, we describe the overall stability of the search engine results recorded for each group. Our findings suggest that search engine results for informational queries are significantly more stable than the results obtained using transactional, navigational, or commercial queries.

In this paper we look at how images can be labelled as a result of click throughs from searches. One approach acts as a filter on image searches specifically, while the other approach propagates labels to images from their containing pages, where those pages were labelled themselves using clickthrough as a filter on text search. Then the paper reports on an experiment where users ranked for relevance six methods for labelling images, comparing the two clickthrough-based methods with flickr's amateur explicit labelling, Getty's professional explicit labelling, Google's standard image search, and the new Google Image Labeller. As well as comparing the accuracy of the proposed image labelling methods and discovering that automatic methods outperform explicit human labelling methods, the experiment suggests clickthrough data is reliable with very few clicks for image classification purposes.

In this article we describe a hybrid technique for dictionary-based query translation suitable for English-Chinese cross language information retrieval. This technique marries a graph-based model for the resolution of candidate term ambiguity with a pattern-based method for the translation of out-of-vocabulary (OOV) terms. We evaluate the performance of this hybrid technique in an experiment using several NTCIR test collections. Experimental results indicate a substantial increase in retrieval effectiveness over various baseline systems incorporating machine- and dictionary-based translation.

No abstract available.

This paper presents an approach to resolve the problem of authoring and interchanging educational material, based on Web services. Here we describe the ultimate goal, of reusing and interchanging freely adaptive e-learning material, shortly sketch previous solutions, showing their benefits but also limitations and then introduce our Web-service based approach. Finally, we discuss the gains brought by our proposed solution

The mechanisms for personalisation used in web applications are currently the subject of much debate amongst researchers from many diverse subject areas. One of the most contemporary ideas for user modelling in web applications is that of cognitive styles, where a user's psychological preferences are assessed stored in a database and then used to provide personalised content and/or links. We describe user trials of a case study that utilises visual-verbal preferences in an adaptive web-based educational system (AWBES). Students in this trial were assessed by the Felder-Solomon Inventory of Learning Styles (ILS) instrument, and their preferences were used as a means of content personalisation.Contrary to previous findings by other researchers, we found no significant differences in performance between matched and mismatched students. Conclusions are drawn about the value and validity of using cognitive styles as a way of modelling user preferences in educational web applications.

This article introduces Atomic Hypermedia which, unlike traditional hypermedia approaches, does not use the concept of a node. Instead, all content is represented as single character ‘atoms’ which are placed along an arbitrary number of dimensions. This approach addresses some of the issues with node-based hypermedia. The article describes the basic mechanics of Atomic Data Structure, and how hypermedia behaviours can be obtained.

This paper presents an innovative approach to personalize on-line content to the needs of individual learners. We use a regular educational environment, the blackboard learning management system, with a new approach: we add adaptivity and personalization to it by means of authoring the goal-oriented material in an adaptive hypermedia authoring system, MOT, and delivering it in blackboard via a conversion to the SCORM specification. This represents the first attempt to connect adaptive hypermedia and learning management systems.

Lexical ambiguity in query-based image retrieval is an immemorial problem which has seemingly resisted all countermeasures. In this paper we introduce a methodology that expresses the users of a system and their navigational behaviour as the paramount resource for resolving query term ambiguity. Mass user consensus is modelled within a multi-dimensional feature space and evaluated through cluster analysis. This technique resolves query term ambiguity in a wholly democratic and dynamic fashion, in contrast to the brittle centralised models of contemporary word sense classification systems. The simple approach contained herein leads to several interesting emergent properties.

This paper proposes a mathematical model of sets of hypermedia links and of the navigation of these links. Every set of links can be modelled as a mathematical binary relation and different representations of binary relations then model all the possible implementations of sets of links. Representations are generated by combining the different representations of the sets participating in the relation, although some ‘representations’ thus generated do not genuinely represent binary relations. Despite this, they are still used in hypermedia implementations, although not without some implicit assumption of relatedness in addition to the representation. The navigation of links is modelled by questions asked of binary relations in any of these representations. For some representations and some situations, the questions cannot be answered, so the usefulness of a link set implementation is governed by its underlying representation. The purpose of this model is to create a reference framework for the different implementations of links in hypermedia systems. This reference framework provides a common basis for the analysis and comparison of the mechanics of link implementations, performed independently of the hypermedia system that implements them and independently of the data being linked.