Jalal S. Alowibdi

Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination

Abstract Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need.

CommuniMents: A Framework for Detecting Community Based Sentiments for Events

Social media has revolutionized human communication and styles of interaction. Due to its effectiveness and ease, people have started using it increasingly to share and exchange information, carry out discussions on various events, and express their opinions. Various communities may have diverse sentiments about events and it is an interesting research problem to understand the sentiments of a particular community for a specific event. In this article, the authors propose a framework CommuniMents which enables us to identify the members of a community and measure the sentiments of the community for a particular event. CommuniMents uses automated snowball sampling to identify the members of a community, then fetches their published contents (specifically tweets), pre-processes the contents and measures the sentiments of the community. The authors perform qualitative and quantitative evaluation for a variety of real world events to validate the effectiveness of the proposed framework.

Post Summarization of Microblogs of Sporting Events

Every day 645 million Twitter users generate approximately 58 million tweets. This motivates the question if it is possible to generate a summary of events from this rich set of tweets only. Key challenges in post summarization from microblog posts include circumnavigating spam and conversational posts. In this study, we present a novel technique called lexi-temporal clustering (LTC), which identifies key events. LTC uses k-means clustering and we explore the use of various distance measures for clustering using Euclidean, cosine similarity and Manhattan distance. We collected three original data sets consisting of Twitter microblog posts covering sporting events, consisting of a cricket and two football matches. The match summaries generated by LTC were compared against standard summaries taken from sports sections of various news outlets, which yielded up to 81% precision, 58% recall and 62% F-measure on different data sets. In addition, we also report results of all three variants of the recall-oriented understudy for gisting evaluation (ROUGE) software, a tool which compares and scores automatically generated summaries against standard summaries.

vIoT: A first step towards a shared, multi-tenant IoT Infrastructure architecture

This paper describes a virtualized Internet of Things (vIoT) testbed. We argue in favor of an IoT Infrastructure-as-a-Service as a possible deployment model for future IoTs. The vIoT testbed is being built from open source components, most notably comprising of OpenStack, Linux containers and Raspberry Pi computers. Results demonstrates vIoT infrastructure configured to be shared by multiple users using with LXC/LXD running containers of Ubuntu Trusty Tahr, Ubuntu Xenial Xerus and CirrOS.

Word cloud segmentation for simplified exploration of trending topics on Twitter

Twitter is a popular microblogging platform, with 310 million monthly active users as of the first quarter of 2016. It is a rapidly growing microblogging platform where people share opinions, news on any topic of their interest. More than 7000 tweets are posted every second. Due to the enormous volume of data being generated, it becomes difficult to extract useful/meaningful information. Tweets collected from Twitter on a certain topic may consist of numerous conversation threads about relevant sub-topics. However, it is difficult to discern these sub-topics if the data is visualised as a single word cloud. The authors transform a corpus of tweets to a spectral domain and evaluate the results from a number of clustering algorithms, including K-means, latent semantic indexing and non-negative matrix factorisation to construct clustered word clouds that helps identify sub-topics under a broader topic.

Analytical Modeling of End-to-End Delay in OpenFlow Based Networks

OpenFlow enabled networks split and separate the data and control planes of traditional networks. This design commodifies network switches and enables centralized control of the network. Control decisions are made by an OpenFlow controller, and locally cached by switches, as directed by controllers. This can significantly impact the forwarding delay incurred by packets in switches, because controllers are not necessarily co-located with switches. Only very few studies have been conducted to evaluate the performance of OpenFlow in terms of end-to-end delay. In this paper, we develop a stochastic model for the end to end delay in OpenFlow switches based on measurements made in Internet-scale experiments performed on three different platforms, i.e., Mininet, the GENI testbed, and the OF@TEIN testbed.

Instance launch-time analysis of OpenStack virtualization technologies with control plane network errors

We analyzed the performance of a multi-node OpenStack cloud amid different types of controlled and self-induced network errors between controller and compute-nodes on the control plane network. These errors included limited bandwidth, delays and packet losses of varying severity. This study compares the effects of network errors on spawning times of batches of instances created using three different virtualization technologies supported by OpenStack, i.e., Docker containers, Linux containers and KVM virtual machines. We identified minimum/maximum thresholds for bandwidth, delay and packet-loss rates below/beyond which instances fail to launch. To the authors’ best knowledge, this is the first comparative measurement study of its kind on OpenStack. The results will be of particular interest to designers and administrators of distributed OpenStack deployments.

Disease Tracking in GCC Region Using Arabic Language Tweets

Several prior studies have demonstrated the possibility of tracking the outbreak and spread of diseases using public tweets and other social media platforms. However, almost all such prior studies were restricted to geographically filtered English language tweets only. This study is the first to attempt a similar approach for Arabic language tweets originating from the Gulf Cooperation Council (GCC) countries. We obtained a list of commonly occurring diseases in the region from the Saudi Ministry of Health. We used both the English disease names as well as their Arabic translations to filter the stream of tweets. We acquired old tweets for a period spanning 29 months. All tweets were geographically filtered for the Middle East and the list of disease names in both English and Arabic languages. We observed that only a small fraction of tweets were in English, demonstrating that prior approaches to disease tracking relying on English language features are less effective for this region. We also demonstrate how Arabic language tweets can be used rather effectively to track the spread of some infectious diseases in the region. We verified our approach by demonstrating that a high degree of correlation between the occurrence of MERS-Coronavirus cases and Arabic language tweets on the disease. We also show that infectious diseases generating fewer tweets and non-infectious diseases do not exhibit the same high correlation. We also verify the usefulness of tracking cases using Twitter mentions by comparing against a ground truth data set of MERS-CoV cases obtained from the Saudi Ministry of Health.

An Adaptive Method for Clustering by Fast Search-and-Find of Density Peaks: Adaptive-DP

Clustering by fast search and find of density peaks (DP) is a method in which density peaks are used to select the number of cluster centers. The DP has two input parameters: 1) the cutoff distance and 2) cluster centers. Also in DP, different methods are used to measure the density of underlying datasets. To overcome the limitations of DP, an Adaptive-DP method is proposed. In Adaptive-DP method, heat-diffusion is used to estimate density, cutoff distance is simplified, and novel method is used to discover exact number of cluster centers, adaptively. To validate the proposed method, we tested it on synthetic and real datasets, and comparison are done with the state of the art clustering methods. The experimental results validate the robustness and effectiveness of proposed method.

MFS-LDA: a multi-feature space tag recommendation model for cold start problem

Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.,MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).,Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.,The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.