AUGUR: Forecasting the Emergence of New Research Topics

Being able to rapidly recognise new research trends is strategic for many stakeholders, including universities, institutional funding bodies, academic publishers and companies. The literature presents several approaches to identifying the emergence of new research topics, which rely on the assumption that the topic is already exhibiting a certain degree of popularity and consistently referred to by a community of researchers. However, detecting the emergence of a new research area at an embryonic stage, i.e., before the topic has been consistently labelled by a community of researchers and associated with a number of publications, is still an open challenge. We address this issue by introducing Augur, a novel approach to the early detection of research topics. Augur analyses the diachronic relationships between research areas and is able to detect clusters of topics that exhibit dynamics correlated with the emergence of new research topics. Here we also present the Advanced Clique Percolation Method (ACPM), a new community detection algorithm developed specifically for supporting this task. Augur was evaluated on a gold standard of 1,408 debutant topics in the 2000-2011 interval and outperformed four alternative approaches in terms of both precision and recall

Emergence of influential spreaders in modified rumor models

The burst in the use of online social networks over the last decade has provided evidence that current rumor spreading models miss some fundamental ingredients in order to reproduce how information is disseminated. In particular, recent literature has revealed that these models fail to reproduce the fact that some nodes in a network have an influential role when it comes to spread a piece of information. In this work, we introduce two mechanisms with the aim of filling the gap between theoretical and experimental results. The first model introduces the assumption that spreaders are not always active whereas the second model considers the possibility that an ignorant is not interested in spreading the rumor. In both cases, results from numerical simulations show a higher adhesion to real data than classical rumor spreading models. Our results shed some light on the mechanisms underlying the spreading of information and ideas in large social systems and pave the way for more realistic diffusion models.Comment: 14 Pages, 6 figures, accepted for publication in Journal of Statistical Physic

Dynamics of private social networks

Social networks, have been a significant turning point in ways individuals and companies interact. Various research has also revolved around public social networks, such as Twitter and Facebook. In most cases trying to understand what's happening in the network such predicting trends, and identifying natural phenomenon. Seeing the growth of public social networks several corporations have sought to build their own private networks to enable their staff to share knowledge, and expertise. Little research has been done in regards to the value private networks give to their stake holders. This is primarily due to the fact as their name implies, these networks are private, thus access to internal data is limited to a trusted few. This paper looks at a particular online private social network, and seeks to investigate the research possibilities made available, and how this can bring value to the organisation which runs the network. Notwithstanding the limitations of the network, this paper seeks to explore the connections graph between members of the network, as well as understanding the topics discussed within the network. The findings show that by visualising a social network one can assess the success or failure of their online networks. The Analysis conducted can also identify skill shortages within areas of the network, thus allowing corporations to take action and rectify any potential problems.peer-reviewe

Twitter financial community sentiment and its predictive relationship to stock market movement

Twitter, one of the several major social media platforms, has been identified as an influential factor for financial markets by multiple academic and professional publications in recent years. The motivation of this study hinges on the growing popularity of the use of Twitter and the increasing prevalence of its influence among the financial investment community. This paper presents empirical evidence of the existence of a financial community on Twitter in which users’ interests align with financial market-related topics. We establish a methodology to identify relevant Twitter users who form the financial community, and we also present the empirical findings of network characteristics of the financial community. We observe that this financial community behaves similarly to a small-world network, and we further identify groups of critical nodes and analyse their influence within the financial community based on several network centrality measures. Using a novel sentiment analysis algorithm, we construct a weighted sentiment measure using tweet messages from these critical nodes, and we discover that it is significantly correlated with the returns of the major financial market indices. By forming a financial community within the Twitter universe, we argue that the influential Twitter users within the financial community provide a proxy for the relationship between social sentiment and financial market movement. Hence, we conclude that the weighted sentiment constructed from these critical nodes within the financial community provides a more robust predictor of financial markets than the general social sentiment

Bridging the Gap Between the Least and the Most Influential Twitter Users

Social networks play an increasingly important role in shaping the behaviour of users of the Web. Conceivably Twitter stands out from the others, not only for the platform's simplicity but also for the great influence that the messages sent over the network can have. The impact of such messages determines the influence of a Twitter user and is what tools such as Klout, PeerIndex or TwitterGrader aim to calculate. Reducing all the factors that make a person influential into a single number is not an easy task, and the effort involved could become useless if the Twitter users do not know how to improve it. In this paper we identify what specific actions should be carried out for a Twitterer to increase their influence in each of above-mentioned tools applying, for this purpose, data mining techniques based on classification and regression algorithms to the information collected from a set of Twitter users.This work has been partially founded by the European Commission Project ”SiSOB: An Observatorium for Science in Society based in Social Models” (http://sisob.lcc.uma.es) (Contract no.: FP7 266588), ”Sistemas Inalámbricos de Gestión de Información Crítica” (with code number TIN2011-23795 and granted by the MEC, Spain) and ”3DTUTOR: Sistema Interoperable de Asistencia y Tutoría Virtual e Inteligente 3D” (with code number IPT-2011-0889- 900000 and granted by the MINECO, Spain