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

To follow or not to follow? How Belgian health journalists use Twitter to monitor potential sources

Digital technology, the internet and mobile media are transforming the journalism and media landscape by influencing the news gathering and sourcing process. The empowering capacities of social media applications may constitute a key element for more balanced news access and “inclusive journalism”. We will build on two contrasting views that dominate the social media sourcing debate. On the one hand, literature shows that journalists of legacy media make use of social media sources to diversify their sourcing network including bottom-up sources such as ordinary citizens. On the other hand, various authors conclude that journalists stick with their old sourcing routines and continue to privilege top-down elite sources such as experts and government officials. In order to contribute to this academic debate we want to clarify the Twitter practices of professional Belgian health journalists in terms of how they use the platform to monitor potential sources. Therefore, we examined the 1146 Twitter “followings” of six Belgian health journalists by means of digital methods and social network analysis. Results show that top-down actors are overrepresented in the “following” networks and that Twitter’s “following” function is not used to reach out to bottom-up actors. In the overall network, we found that the health journalists mainly use Twitter as a “press club” (Rupar, 2015) to monitor media actors. If we zoom in specifically on the “following” network of the health-related actors, we found that media actors are still important, but experts become the most followed group. Our findings also underwrite the “power law” or “long tail” distribution of social network sites as very few actors take a central position in the “following” lists while the large majority of actors are not systematically monitored by the journalists

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Brain regions involved in observing and trying to interpret dog behaviour

Humans and dogs have interacted for millennia. As a result, humans (and especially dog owners) sometimes try to interpret dog behaviour. While there is extensive research on the brain regions that are involved in mentalizing about other peoples' behaviour, surprisingly little is known of whether we use these same brain regions to mentalize about animal behaviour. In this fMRI study we investigate whether brain regions involved in mentalizing about human behaviour are also engaged when observing dog behaviour. Here we show that these brain regions are more engaged when observing dog behaviour that is difficult to interpret compared to dog behaviour that is easy to interpret. Interestingly, these results were not only obtained when participants were instructed to infer reasons for the behaviour but also when they passively viewed the behaviour, indicating that these brain regions are activated by spontaneous mentalizing processes

Understanding the corpus of mobile payment services research: an analysis of the literature using co-citation analysis and social network analysis

Mobile Payment Services have advanced in the last two decades, gaining the attention of experts and researchers from around the world. A number of reviews and literature analysis studies have been carried out, aimed at analysing the numerous dimensions of mobile payment services; however, no researcher has attempted a co-citation analysis to scrutinise and comprehend the core knowledge structures that are integral parts of mobile payment services studies. Therefore, in order to fill this research gap, this research article aims to interpret the corpus of mobile payment services research, which was published during the period of 1997 to June 2017. Bibliometric and Social Network Analysis (SNA) methods were employed to formulate the core intellectual structure of research targeting mobile payment services. The Web of Knowledge (WoK) database was the key source from where 406 articles and 3,424 citations were obtained. These documents were analysed using co-citation analysis. UCINET was used to enlist the keynote research papers in the realm of mobile payment services as per factor analysis, citation and co-citation analysis, multidimensional scaling and centrality measurement. Seven core clusters of mobile payment services research emerged as a critical finding of this study; these clusters include (1) Adoption and usage; (2) Trust, risk and security; (3) Application; (4) Scheme; (5) Protocol; (6) Architecture; (7) Mobile payment corporation. The findings of this research study provide crucial guidelines for practitioners and researchers involved in this field.Mobile Payment Services have advanced in the last two decades, gaining the attention of experts and researchers from around the world. A number of reviews and literature analysis studies have been carried out, aimed at analysing the numerous dimensions of mobile payment services; however, no researcher has attempted a co-citation analysis to scrutinise and comprehend the core knowledge structures that are integral parts of mobile payment services studies. Therefore, in order to fill this research gap, this research article aims to interpret the corpus of mobile payment services research, which was published during the period of 1997 to June 2017. Bibliometric and Social Network Analysis (SNA) methods were employed to formulate the core intellectual structure of research targeting mobile payment services. The Web of Knowledge (WoK) database was the key source from where 406 articles and 3,424 citations were obtained. These documents were analysed using co-citation analysis. UCINET was used to enlist the keynote research papers in the realm of mobile payment services as per factor analysis, citation and co-citation analysis, multidimensional scaling and centrality measurement. Seven core clusters of mobile payment services research emerged as a critical finding of this study; these clusters include (1) Adoption and usage; (2) Trust, risk and security; (3) Application; (4) Scheme; (5) Protocol; (6) Architecture; (7) Mobile payment corporation. The findings of this research study provide crucial guidelines for practitioners and researchers involved in this field