Sepideh Mesbah, Jie Yang, Robert-Jan Sips, Manuel Valle Torre, Christoph Lofi, Alessandro Bozzon, Geert-Jan Houben. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Health literacy, i.e. the ability to read and understand medical text, is a relevant component of public health. Unfortunately, many medical texts are hard to grasp by the general population as they are targeted at highly-skilled professionals and use complex language and domain-specific terms. Here, automatic text simplification making text commonly understandable would be very beneficial. However, research and development into medical text simplification is hindered by the lack of openly available training and test corpora which contain complex medical sentences and their aligned simplified versions. In this paper, we introduce such a dataset to aid medical text simplification research. The dataset is created by filtering aligned health sentences using expert knowledge from an existing aligned corpus and a novel simple, language independent monolingual text alignment method. Furthermore, we use the dataset to train a state-of-the-art neural machine translation model, and compare it to a model trained on a general simplification dataset using an automatic evaluation, and an extensive human-expert evaluation.

As crowdsourcing gains popularity, organisations seek ways to systematically and reliably involve their workforce with data processing pipelines. Mobile crowdsourcing allows for opportunistic task executions and thus, potentially, for higher throughput. However, how to engage and to retain employees in enterprise crowdsourcing campaigns is still an open research topic. This paper discusses the results of a study performed in IBM Benelux. We surveyed 93 employees to discover the factors that might affect engagement in mobile enterprise crowdsourcing. The survey informed the design of an experiment that aimed at investigating the effectiveness of different task notification strategies. We studied how factors such as time and context of notification can affect the participation and retention of employees. Results show that break times are the most suitable for crowd work, and that ``aggressive'' notification strategies act as deterrent for participation, while moderate yet regular nudges are the most likely to retain contributors.

In 2010 the popular paper by Kwak et al. [11] presented the first comprehensive study of Twitter as it appeared in 2009, using most of the Twitter network at the time. Since then, Twitter's popularity and usage has exploded, experiencing a 10-fold increase. As of 2015, it has more than 500 million users, out of which 316 million are active, i.e. logging into the service at least once a month.1In this study we revisit the network observed by Kwak et al. to examine the changes exhibited in both the graph and the behavior of the users in it. Our results conclude to a denser network, showing an increase in the number of reciprocal edges, despite the fact that around 12.5% of the 2009 users have now left Twitter. However, the network's largest strongly connected component seems to be significantly decreasing, suggesting a movement of edges towards popular users. Furthermore, we observe numerous changes in the lists of influential Twitter users, having several accounts that where not popular in the past securing a position in the top-20 list as new entries.

In recent years, gamification, “the use of game design elements in non-game contexts”, has drawn the attention of an increasing number of scientists. Although several studies highlighted the benefits of gamification in many applications, its potential in the \emph{enterprise} environment still needs to be fully understood.