A study of the online profile of enterprise users in professional social networks

Bozzon, Alessandro; Efstathiades, Hariton; Houben, Geert‐Jan; Sips, Robert-Jan

doi:10.1145/2567948.2576938

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…New work has also emerged on the interpretation and analysis of social web data with a strong focus on cultural differences -for example, a comparison between Twitter and Sina Weibo [11]. Likewise, recent work has also shown how data analytics can benefit the workforce engagement in enterprise contexts [12].…”

Section: Related Workmentioning

confidence: 99%

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

Abolkasim

Lau

Dimitrova

2016

Adaptive and Adaptable Learning

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract. This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling's framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners' perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

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Section: Related Workmentioning

confidence: 99%