A perennial criticism regarding the use of social media in social science research is the lack of demographic information associated with naturally occurring mediated data such as that produced by Twitter. However the fact that demographics information is not explicit does not mean that it is not implicitly present. Utilising the Cardiff Online Social Media ObServatory (COSMOS) this paper suggests various techniques for establishing or estimating demographic data from a sample of more than 113 million Twitter users collected during July 2012. We discuss in detail the methods that can be used for identifying gender and language and illustrate that the proportion of males and females using Twitter in the UK reflects the gender balance observed in the 2011 Census. We also expand on the three types of geographical information that can be derived from Tweets either directly or by proxy and how spatial information can be used to link social media with official curated data. Whilst we make no grand claims about the representative nature of Twitter users in relation to the wider UK population, the derivation of demographic data demonstrates the potential of new social media (NSM) for the social sciences. We consider this paper a clarion call and hope that other researchers test the methods we suggest and develop them further.

The Internet of Things needs for computing power and storage are expected to remain on the rise in the next decade. Consequently, the amount of data generated by devices at the edge of the network will also grow. While cloud computing has been an established and effective way of acquiring computation and storage as a service to many applications, it may not be suitable to handle the myriad of data from IoT devices and fulfill largely heterogeneous application requirements. Fog computing has been developed to lie between IoT and the cloud, providing a hierarchy of computing power that can collect, aggregate, and process data from/to IoT devices. Combining fog and cloud may reduce data transfers and communication bottlenecks to the cloud and also contribute to reduced latencies, as fog computing resources exist closer to the edge. This paper examines this IoT-Fog-Cloud ecosystem and provides a literature review from different facets of it: how it can be organized, how management is being addressed, and how applications can benefit from it. Lastly, we present challenging issues yet to be addressed in IoT-Fog-Cloud infrastructures. low latency, and mobile applications. The centralized cloud data centers are often physically and/or logically distant from the cloud client, implying communication and data transfers to traverse multiple hops, which introduces delays and consumes network bandwidth of edge and core networks [2].The widespread adoption of cloud computing, combined with the ever increasing ability of edge devices to run heterogeneous applications that generate and consume all kinds of data from a variety of sources, requires novel distributed computing infrastructures that can cope with such heterogeneous application requirements. Computing infrastructures that enact applications at edge devices have started to appear in recent years [3,4], improving aspects such as response time and reducing bandwidth use. Combining the ability of running smaller, localized applications at the edge with the high-capacity from the cloud, fog computing has emerged as an paradigm that can support heterogeneous requirements of small and large applications through multiple layers of a computational infrastructure that combines resources from the edge of the network as well as from the cloud [5].In this paper, we aim at identifying and reviewing the main aspects and challenges that make the combination of fog computing and cloud computing suitable for all kinds of applications leveraged by the Internet of Things. We discuss aspects from the infrastructure (processing, networking, protocols, and infrastructure for 5G support) to applications (smart cities, urban computing, and industry 4.0), passing through the management complexity of the distributed IoT-fog-cloud system (services, resource allocation and optimization, energy consumption, data management and locality, devices federation and trust, and business and service models).In the next section we introduce concepts and definitions for Internet of Things (IoT), cloud computi...

Little is currently known about the factors that promote the propagation of information in online social networks following terrorist events. In this paper we took the case of the terrorist event in Woolwich, London in 2013 and built models to predict information flow size and survival using data derived from the popular social networking site Twitter. We define information flows as the propagation over time of information posted to Twitter via the action of retweeting. Following a comparison with different predictive methods, and due to the distribution exhibited by our dependent size measure, we used the zerotruncated negative binomial (ZTNB) regression method. To model survival, the Cox regression technique was used because it estimates proportional hazard rates for independent measures. Following a principal component analysis to reduce the dimensionality of the data, social, temporal and content factors of the tweet were used as predictors in both models. Given the likely emotive reaction caused by the event, we emphasize the influence of emotive content on propagation in the discussion section. From a sample of Twitter data collected following the event (N = 427,330) we report novel findings that identify that the sentiment expressed in the tweet is statistically significantly predictive of both size and survival of information flows of this nature. Furthermore, the number of offline press reports relating to the event published on the day the tweet was posted was a significant predictor of size, as was the tension expressed in a tweet in relation to survival. Furthermore, time lags between retweets and the cooccurrence of URLS and hashtags also emerged as significant.

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