Martin J. Chorley

Social media has become a key medium for discussion and dissemination of news stories, fuelled by the low barrier to entry and the ease of interaction. News stories may be propagated through these networks either by official news organisation accounts, by individual journalists or by members of the public, through link sharing, endorsing or commenting. This preliminary research aims to show how computational analysis of large-scale data-sets allows us to investigate the means by which news stories are spread through social media, and how the conversation around them is shaped by journalists and news organisations. Through the capture of more than 11 million tweets relating to 2303 Twitter accounts connected to journalism and news organisations, we are able to analyse the conversation within and around journalism, examining who spreads information about news articles and who interacts in the discussion around them. Capturing the tweets of news organisations and journalists and the replies and retweets of these micro-blogs allows us to build a rich picture of interaction around news media.

The Axelrod library is an open source Python package that allows for reproducible game theoretic research into the Iterated Prisoner’s Dilemma. This area of research began in the 1980s but suffers from a lack of documentation and test code. The goal of the library is to provide such a resource, with facilities for the design of new strategies and interactions between them, as well as conducting tournaments and ecological simulations for populations of strategies. With a growing collection of 139 strategies, the library is a also a platform for an original tournament that, in itself, is of interest to the game theoretic community. This paper describes the Iterated Prisoner’s Dilemma, the Axelrod library and its development, and insights gained from some novel research

We introduce the concept of extended mind crowd sourcing (EMC) which capitalises on the way in which humans naturally extend their cognition into the environment, using external objects such as smartphones and applications to augment their mental capacity. This phenomenon means that human computation is embedded in data and devices, representing a new way through which human cognition can be accessed for collective discoveries. We relate EMC to existing sociological and psychological concepts and argue that it lies at the intersection of human computation, social computing and crowd sourcing. EMC is a way in which new problems and discoveries can be tackled, for example as necessitated by "wicked" problems, ethnography and culture. We relate EMC to diverse disciplines and point to ways in which the concept may develop in future. We exemplify EMC by presenting a case study where participation in location-based social networks is used to discover the correlation between mobility and human personality traits. This has involved participation from 43 countries and resulted in analysis of over half a million check-ins at street-level locations.

In this paper we investigate the properties of a cognitive heuristics based approach, by way of which the mobile devices of users can become aware of the physical environment surrounding them. Cognitive heuristics are the mental models used in cognitive psychology to describe how human brains efficiently process the huge amount of information constantly coming from the environment around us. As personal mobile devices represent proxies in the cyber world of their human users, we investigate how the same cognitive heuristics can be used by mobile devices to become self-aware of the features of the physical environment around their users. Specifically, we assume that physical locations are described as a network of tags. We consider a self-organising opportunistic network environment, where devices exchange information when meeting directly. We propose algorithms based on cognitive heuristics through which users' nodes obtain tags either directly when coming in proximity of locations, or indirectly through other nodes they meet. We analyse the properties of the networks of tags resulting at individual nodes, as they emerge from this process, as a function of various cognitive parameters. We show that using cognitive heuristics leads, under the same resource constraints, to much more effective information diffusion with respect to other reference solutions. Interestingly, we find critical thresholds for key parameters that discriminate between information diffusion and information loss. Finally, we show that, despite resource constraints, the structure of the network of tags at individual nodes is remarkably close to the ideal that would be obtained with infinite resources.

The study of personality can now provide us with greater insights into human behaviour, when combined with data from social and mobile networks. In this paper we study the relationship between the types of locations that Foursquare users visit and their personality. Using the five-factor personality model, personality profiles were obtained for 173 participating Foursquare users whose activities were analysed, representing a total of 474, 539 check-ins across the globe. In this paper we present initial results concerning the number and types of venues and the relationship with personality traits. Particular correlations are identified concerning the openness, conscientiousness and neuroticism of participants and their levels of Foursquare activity. Extracting different categories of venues reveals variation in the correlation strength of personality traits of the users. As far as we are aware this is the first study of its type, linking human personality with spatial behaviour in mobile social networks.

Current applications tend to use personal sensitive information to achieve better quality with respect to their services. Since the third parties are not trusted the data must be protected such that individual data privacy is not compromised but at the same time operations on it would be compatible. A wide range of data analysis operations entails a similarity detection algorithm between user data. For instance clustering on big data groups together objects based on the heuristic that similar objects are likely to be put under the same cluster. Similarity decisions are important for numerous applications such as: online social networks, recommendations systems and behavioral advertisement. In this paper we propose a mechanism that protects user privacy and preserves data similarity results although encrypted. We analyze the security of the scheme and we further demonstrate its correctness and feasibility through a real life experiment where personality traits by users are collected for a 4square application.