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The use of articles from scientific journals is an important part of research-based teaching at universities. The selection of relevant work from among the increasing amount of scientific literature can be problematic; the challenge is to find relevant recommendations, especially when the related articles are not obviously linked. This paper seeks to discuss these issues.

This paper focuses on the analysis of user activity traces in journals using the open source software “Open Journal Systems” (OJS). The research questions to what extent end users follow a certain link structure given within OJS or immediately select the articles according to their interests. In the latter case, the recorded data sets are used for creating further recommendations. The analysis is based on an article matrix, displaying the usage frequency of articles and their user selected successive articles within the OJS. Furthermore, the navigation paths are analysed.

It was found that the users tend to follow a set navigation structure. Moreover, a hybrid recommendation system for OJS is described, which uses content based filtering as the basic system extended by the results of a collaborative filtering approach.

The paper presents two original contributions: the analysis of user path tracing and a novel algorithm that allows smooth integration of new articles into the existing recommendations, due to the fact that scientific journals are published in a frequent and regular time sequence.

The information needs of the users of literature database systems often come from the task at hand, which is short term and can be represented as a small number of articles. Previous works on recommending articles to satisfy users’ short-term interests have utilized article content, usage logs, and more recently, coauthorship networks. The usefulness of coauthorship has been demonstrated by some research works, which, however, tend to adopt a simple coauthorship network that records only the strength of coauthorships. The purpose of this paper is to enhance the effectiveness of coauthorship-based recommendation by incorporating scholars’ collaboration topics into the coauthorship network.

The authors propose a latent Dirichlet allocation (LDA)-coauthorship-network-based method that integrates topic information into the links of the coauthorship networks using LDA, and a task-focused technique is developed for recommending literature articles.

The experimental results using information systems journal articles show that the proposed method is more effective than the previous coauthorship network-based method over all scenarios examined. The authors further develop a hybrid method that combines the results of content-based and LDA-coauthorship-network-based recommendations. The resulting hybrid method achieves greater or comparable recommendation effectiveness under all scenarios when compared to the content-based method.

This paper makes two contributions. The authors first show that topic model is indeed useful and can be incorporated into the construction of coaurthoship-network to improve literature recommendation. The authors subsequently demonstrate that coauthorship-network-based and content-based recommendations are complementary in their hit article rank distributions, and then devise a hybrid recommendation method to further improve the effectiveness of literature recommendation.

The purpose of this paper is to investigate the extent to which textual characteristics of online reviews help identify authentic entries from manipulative ones across positive and negative comments.

A theoretical framework is proposed to identify authentic online reviews from manipulative ones based on three textual characteristics, namely, comprehensibility, informativeness, and writing style. The framework is tested using two publicly available data sets, one comprising positive reviews to hype own offerings, and the other including negative reviews to slander competing offerings. Logistic regression is used for analysis.

The three textual characteristics offered useful insights to identify authentic online reviews from manipulative ones. In particular, the differences between authentic and manipulative reviews in terms of comprehensibility and informativeness were more conspicuous for negative entries. On the other hand, the differences between authentic and manipulative reviews in terms of writing style were more conspicuous for positive entries.

The findings of this paper are somewhat constrained by the scope of the data sets used for analysis.

The paper represents one of the earliest attempts to develop a theoretical framework to identify authentic online reviews. Prior research has shed light on ways to classify reviews as authentic or manipulative. However, literature on specific differences between the two in terms of textual characteristics is relatively limited. Moreover, by suggesting differences between authentic and manipulative reviews across positive and negative comments, the findings offer nuanced insights into a research area that is growing in importance.