Location-based social networks (LBSNs) are a recent phenomenon for sharing a presence at everyday locations with others and have the potential to give new insights into human behaviour. To date, due to barriers in data collection, there has been little research into how our personality relates to the categories of place that we visit. Using the Foursquare LBSN, we have released a web-based participatory application that examines the personality characteristics and checkin behaviour of volunteer Foursquare users. Over a four-month period, we examine the behaviour and the “Big Five” personality traits of 174 anonymous users who had collectively checked in 487,396 times at 119,746 venues. Significant correlations are found for Conscientiousness, Openness and Neuroticism. In contrast to some previous findings about online social networks, Conscientiousness is positively correlated with LBSN usage. Openness correlates mainly with location-based variables (average distance between venues visited, venue popularity, number of checkins at sociable venues). For Neuroticism, further negative correlations are found (number of venues visited, number of sociable venues visited). No correlations are found for the other personality traits, which is surprising for Extroversion. The study concludes that personality traits help to explain individual differences in LBSN usage and the type of places visited

In this paper we consider whether people with similar personality traits have a preference for common locations. Due to the difficulty in tracking and categorising the places that individuals choose to visit, this is largely unexplored. However, the recent popularity of location-based social networks (LBSNs) provides a means to gain new insight into this question through checkins - records that are made by LBSN users of their presence at specific street level locations. A web-based participatory survey was used to collect the personality traits and checkin behaviour of 174 anonymous users, who, through their common check-ins, formed a network with 5373 edges and an approximate edge density of 35%. We assess the degree of overlap in personality traits for users visiting common locations, as detected by user checkins. We find that people with similar high levels of conscientiousness, openness or agreeableness tended to have checked-in locations in common. The findings for extraverts were unexpected in that they did not provide evidence of individuals assorting at the same locations, contrary to predictions. Individuals high in neuroticism were in line with expectations, they did not tend to have locations in common. Unanticipated results concerning disagreeableness are of particular interest and suggest that different venue types and distinctive characteristics may act as attractors for people with particularly selective tendencies. These findings have important implications for decision-making and location

Micro-blogging services have recently been experiencing increasing success among Web users. Different to traditional online social applications, micro-blogs are lightweight, require small cognitive effort and help share real-time information about personal activities and interests. In this article, we explore scalable pushing protocols that are particularly suited for the delivery of this type of service in a mobile pervasive environment. Here, micro-blog updates are generated and carried by mobile (smart-phone type) devices and are exchanged through opportunistic encounters. We enhance primitive push mechanisms using social information concerning the interests of network nodes as well as the frequency of encounters with them. This information is collected and shared dynamically, as nodes initially encounter each other and exchange their preferences, and directs the forwarding of micro-blog updates across the network. Also incorporated is the spatiotemporal scope of the updates, which is only partially considered in current Internet services.We introduce several new protocol variants that differentiate the forwarding strategy towards interest-similar and frequently encountered nodes, as well as the amount of updates forwarded upon each encounter. In all cases, the proposed scheme outperforms the basic flooding dissemination mechanism in delivering high numbers of micro-blog updates to the nodes interested in them. Our extensive evaluation highlights how use can be made of different amounts of social information to trade performance with complexity and computational effort. However, hard performance bounds appear to be set by the level of coincidence between interest-similar node communities and meeting groups emerging due to the mobility patterns of the nodes

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.

Social micro-blogging systems such as Twitter are designed for rapid and informal communication from a large potential number of participants. Due to the volume of content received, human users must typically skim their timeline of received content and exercise judgement in selecting items for consumption, necessitating a selection process based on heuristics and content meta-data. This selection process is not well understood, yet is important due to its potential use in content management systems.In this research we have conducted an open online experiment in which participants are shown quantitative and qualitative meta-data describing two pieces of Twitter content. Without revealing the text of the tweet, participants are asked to make a selection. We observe the decisions made from 239 surveys and discover insights into human behaviour on decision making for content selection. We find that for qualitative meta-data consumption decisions are driven by online friendship and for quantitative meta-data the largest numerical value presented influences choice. Overall, the ‘number of retweets’ is found to be the most influential quantitative meta-data, while displaying multiple cues about an author׳s identity provides the strongest qualitative meta-data. When both quantitative and qualitative meta-data is presented, it is the qualitative meta-data (friendship information) that drives selection. The results are consistent with application of the Recognition heuristic, which postulates that when faced with constrained decision-making, humans will tend to exercise judgement based on cues representing familiarity. These findings are useful for future interface design for content filtering and recommendation systems