Social influence analysis in microblogging platforms - a topic-sensitive based approach

The use of Social Media, particularly microblogging platforms such as Twitter, has proven to be an effective channel for promoting ideas to online audiences. In a world where information can bias public opinion it is essential to analyse the propagation and influence of information in large-scale networks. Recent research studying social media data to rank users by topical relevance have largely focused on the “retweet", “following" and “mention" relations. In this paper we propose the use of semantic profiles for deriving influential users based on the retweet subgraph of the Twitter graph. We introduce a variation of the PageRank algorithm for analysing users’ topical and entity influence based on the topical/entity relevance of a retweet relation. Experimental results show that our approach outperforms related algorithms including HITS, InDegree and Topic-Sensitive PageRank. We also introduce VisInfluence, a visualisation platform for presenting top influential users based on a topical query need

Participatory Sensing Based Real-time Public Transport Information Service

Abstract—Modern cities continuously struggle with infrastructural problems especially when the population is massively growing. One affected area is public transportation. In default of offering convenient and reliable service the passengers tend to consider other transport alternatives. However, even a relatively simple functional enhancement, such as providing real-time timetable information, requires considerable investment and effort following traditional means, e.g. deploying sensors and building a background communication and processing infrastructure. Using the power of crowd to gather the required data, share information and send feedback is a viable and cost effective alternative. In this demonstration, we present TrafficInfo, our prototype smart phone application to implement a participatory sensing based live public transport information service. TrafficInfo visualizes the actual position of public transport vehicles with live updates on a map, and gives support to crowd sourced data collection and passenger feedback

Live Public Transport Information Service Using Crowdsourced Data (Demo Paper)

Abstract—Infrastructural problems of modern cities cannot be solved through sheer power of will alone. The public transportation system is one of the most effected parts and as the situation is degrading, more and more people become reluctant to take public transport. The fine tuning of the system, or even its restructuring, requires an immense amount of data, which traditionally can only be collected via costly and time consuming ways, like deploying sensors, conveying surveys, just to name a few. Not to mention that during this process, the citizens do not experience too much improvement, and become easily skeptical concerning the outcomes. Is there really no other way? In this demo, we present and demonstrate our approach to solve this problem in the form of a smart phone application providing real-time feedback on public transport, transits, and user-reviews based on crowdsensing

Semantic Web meets Web 2.0 (and vice versa): The Value of the Mundane for the Semantic Web

Web 2.0, not the Semantic Web, has become the face of “the next generation Web” among the tech-literate set, and even among many in the various research communities involved in the Web. Perceptions in these communities of what the Semantic Web is (and who is involved in it) are often misinformed if not misguided. In this paper we identify opportunities for Semantic Web activities to connect with the Web 2.0 community; we explore why this connection is of significant benefit to both groups, and identify how these connections open valuable research opportunities “in the real” for the Semantic Web effort

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201