Do peers see more in a paper than its authors?

Recent years have shown a gradual shift in the content of biomedical publications that is freely accessible, from titles and abstracts to full text. This has enabled new forms of automatic text analysis and has given rise to some interesting questions: How informative is the abstract compared to the full-text? What important information in the full-text is not present in the abstract? What should a good summary contain that is not already in the abstract? Do authors and peers see an article differently? We answer these questions by comparing the information content of the abstract to that in citances-sentences containing citations to that article. We contrast the important points of an article as judged by its authors versus as seen by peers. Focusing on the area of molecular interactions, we perform manual and automatic analysis, and we find that the set of all citances to a target article not only covers most information (entities, functions, experimental methods, and other biological concepts) found in its abstract, but also contains 20% more concepts. We further present a detailed summary of the differences across information types, and we examine the effects other citations and time have on the content of citances

Learning Reputation in an Authorship Network

The problem of searching for experts in a given academic field is hugely important in both industry and academia. We study exactly this issue with respect to a database of authors and their publications. The idea is to use Latent Semantic Indexing (LSI) and Latent Dirichlet Allocation (LDA) to perform topic modelling in order to find authors who have worked in a query field. We then construct a coauthorship graph and motivate the use of influence maximisation and a variety of graph centrality measures to obtain a ranked list of experts. The ranked lists are further improved using a Markov Chain-based rank aggregation approach. The complete method is readily scalable to large datasets. To demonstrate the efficacy of the approach we report on an extensive set of computational simulations using the Arnetminer dataset. An improvement in mean average precision is demonstrated over the baseline case of simply using the order of authors found by the topic models

Human-computer interaction for development (HCI4D):the Southern African landscape

Human-Computer interaction for development (HCI4D) research aims to maximise the usability of interfaces for interacting with technologies designed specifically for under-served, under-resourced, and under-represented populations. In this paper we provide a snapshot of the Southern African HCI4D research against the background of the global HCI4D research landscape.We commenced with a systematic literature review of HCI4D (2010-2017) then surveyed Southern African researchers working in the area. The contribution is to highlight the context- specific themes and challenges that emerged from our investigation

Mapping the Bid Behavior of Conference Referees

The peer-review process, in its present form, has been repeatedly criticized. Of the many critiques ranging from publication delays to referee bias, this paper will focus specifically on the issue of how submitted manuscripts are distributed to qualified referees. Unqualified referees, without the proper knowledge of a manuscript's domain, may reject a perfectly valid study or potentially more damaging, unknowingly accept a faulty or fraudulent result. In this paper, referee competence is analyzed with respect to referee bid data collected from the 2005 Joint Conference on Digital Libraries (JCDL). The analysis of the referee bid behavior provides a validation of the intuition that referees are bidding on conference submissions with regards to the subject domain of the submission. Unfortunately, this relationship is not strong and therefore suggests that there exists other factors beyond subject domain that may be influencing referees to bid for particular submissions