Invited papers from the ACM conference on hypertext and social media

Abstract

This Special Issue presents three invited papers, selected from among the best contributions that were presented at the 2017 ACM International Conference on Hypertext and Social Media (HT 2017) held in Prague, Czech Republic on 4–7th July 2017. Since 1987, HT has successfully brought together leading researchers and developers from the Hypertext community. It is concerned with all aspects of modern hypertext research, including social media, adaptation, personalisation, recommendations, user modelling, linked data and semantic web, dynamic and computed hypertext, and its application in digital humanities, as well as with interplay between those aspects such as linking stories with data or linking people with resources. The call for papers of HT 2017 was organised into four technical tracks: Social Networks and Digital Humanities (Linking people), Semantic Web and Linked Data (Linking data), Adaptive Hypertext and Recommendations (Linking resources), News and Storytelling (Linking stories). The Program Committee of HT 2017 accepted 19 papers (acceptance rate 27%) for regular presentation, and an additional 12 short-presentation papers. In addition, the conference featured four demonstrations and two keynotes: Kristina Lerman and Peter Mika. The three papers selected for this Special Issue cover a diverse set of topics, well representing the spectrum of topics that were discussed at HT 2017. The first paper, entitled “Implicit Negative Link Detection on Online Political Networks via Matrix Tri-Factorizations” (Ozer, Yildirim and Davulcu), deals with the prediction of negative connections between users of online political networks. Currently, the majority of social media sites do not support explicit negative links between participating users. However, the very nature of the political discourse often involves users in discussing controversial political issues, which results in a series of agreements and disagreements. The authors present a technically sound approach to extracting negative links from a variety of online political platforms by using a matrix factorisation approach. Matrix factorisation is extended in multiple ways to reflect the information that can be found in the sentiment of the written comments as well as the social balance theory known from the social sciences. The paper concludes with a range of experiments on the real datasets using the Twitter accounts of the politicians of the major UK political parties. The experiments show an improved accuracy of the community detection methods applied on the networks with the extracted negative interaction links as compared to the application of these methods on the networks having only positive links. The second paper, entitled “Hybrid Recommendations by Content-Aligned Bayesian Personalized Ranking” (Peska) focuses on recommender systems that seek to predict the "rating" or "preference" a user would give to an item and hence enabling to display items in order the user might find interesting. A special problem is cold-start recommendation, i.e. for a new user or of a new item. The author proposes a hybrid recommendation technique “ContentAligned Bayesian Personalized Ranking” (CABPR) with several variants. This is based on an existing Bayesian Personalized Ranking matrix factorization (BPR) by Rendle et al. CABPR