Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

Impact of information on intentions to vaccinate in a potential epidemic: swine-origin Influenza A (H1N1)

Vaccination campaigns to prevent the spread of epidemics are successful only if the targeted populations subscribe to the recommendations of health authorities. However, because compulsory vaccination is hardly conceivable in modern democracies, governments need to convince their populations through efficient and persuasive information campaigns. In the context of the swine-origin A (H1N1) 2009 pandemic, we use an interactive study among the general public in the South of France, with 175 participants, to explore what type of information can induce change in vaccination intentions at both aggregate and individual levels. We find that individual attitudes to vaccination are based on rational appraisal of the situation, and that it is information of a purely scientific nature that has the only significant positive effect on intention to vaccinate.France; experiment; interactive; information; vaccination; influenza A (H1N1); attitudes

Impact of information on intentions to vaccinate in a potential epidemic : swine-origin Influenza A (H1N1)

Vaccination campaigns to prevent the spread of epidemics are successful only if the targeted populations subscribe to the recommendations of health authorities. However, because compulsory vaccination is hardly conceivable in modern democracies, governments need to convince their populations through efficient and persuasive information campaigns. In the context of the swine-origin A (H1N1) 2009 pandemic, we use an interactive study among the general public in the South of France, with 175 participants, to explore what type of information can induce change in vaccination intentions at both aggregate and individual levels. We find that individual attitudes to vaccination are based on rational appraisal of the situation, and that it is information of a purely scientific nature that has the only significant positive effect on intention to vaccinate.France, experiment, interactive, information, vaccination, influenza A (H1N1), attitudes.

Infectious diseases management framework for Saudi Arabia (SAIF)

A Thesis Submitted to the University of Bedfordshire in partial fulfilment of the requirements for the degree of Doctor of PhilosopyInfectious disease management system area is considered as an emerging field of modern healthcare in the Gulf region. Significant technical and clinical progress and advanced technologies can be utilized to enhance the performance and ubiquity of such systems. Effective infectious disease management (IDM) can be achieved by analysing the disease management issues from the perspectives of healthcare personnel and patients. Hence, it is necessary to identify the needs and requirements of both healthcare personnel and patients for managing the infectious disease. The basic idea behind the proposed mobile IDM system in this thesis is to improve the healthcare processes in managing infectious diseases more effectively. For this purpose, internet and mobile technologies are integrated with social networking, mapping and IDM applications to improve the processes efficiency. Hence, the patients submit their health related data through their devices remotely using our application to our system database (so-called SAIF). The main objective of this PhD project was the design and development of a novel web based architecture of next-generation infectious disease management system embedding the concept of social networking tailored for Saudi patients. Following a detailed literature review which identifies the current status and potential impact of using infectious diseases management system in KSA, this thesis conducts a feasibility user perspective study for identifying the needs and the requirements of healthcare personnel and the patients for managing infectious diseases. Moreover, this thesis proposes a design and development of a novel architecture of next-generation web based infectious disease management system tailored for Saudi patients (i.e., called SAIF – infectious diseases management framework for Saudi Arabia). Further, this thesis introduces a usability study for the SAIF system to validate the acceptability of using mobile technologies amongst infected patient in KSA and Gulf region. The preliminary results of the study indicated general acceptance of the patients in using the system with higher usability rating in high affected patients. In general, the study concluded that the concept of SAIF system is considered acceptable tool in particularly with infected patients

Attributing Meaning to Online Social Network Analysis for Tailored Socio-Behavioral Support Systems

Ubiquitous online social networks provide us with a unique opportunity to deliver scalable interventions for the support of lifestyle modifications in order to change behaviors that predispose toward cancer and other diseases. At the same time these networks act as rich data sources to inform our understanding of end-user needs. Traditionally, social network analysis is based on communication frequency among members. In this work, I introduce communication content as a complementary frame for studying these networks. QuitNet, an online social network developed to provide smoking cessation support is considered for analysis. Qualitative coding, automated content analysis, and network analysis were used to construct QuitNet sub-networks based on both frequency and content attributes. This merging of qualitative, quantitative, and automated methods expands the depth and breadth of existing network analysis techniques thereby allowing us to characterize the nature of communication among network members. First, grounded-theory based qualitative analysis provides a granular view of the QuitNet messages. Using automated text analysis, the communication links between network members were divided based on the similarity of the content in the exchanged messages to the identified themes. This automated analysis allowed us to expand the otherwise prohibitively labor-intensive qualitative methods to a large data sample using minimal time and resources. The follow-up one-mode and two-mode network analysis allowed us to investigate the content-specific communication patterns of QuitNet members. Qualitative analysis of the QuitNet messages identified themes ranging from “Social support”, “Progress”, and “Traditions” to “Nicotine Replacement Therapy (NRT) entries” and “Craves”. Automated annotation of messages was achieved by using a distributional approach incorporating distributional information from an outside corpus into a model of the QuitNet corpus to generate vector representations of messages. A k- nearest neighbor approach was used to infer themes relating to each message. The recall and precision measures indicate that the performance of the automated classification system is 0.77 and 0.71 for high-level themes. The average agreement of the system with two human raters for high-level themes approached the agreement between these human coders for a subset of 100 messages suggesting that the system is a reasonable substitute for a human rater. Subsequent one-mode network analysis provided insights into different theme-based networks at population level revealing content-specific opinion leaders. Two-mode network analysis allowed us to investigate the content affiliation patterns of QuitNet users and understand the content-specific attributes of social influence on smoking abstinence. These studies provide insights into the nature of communication among members in a smoking cessation related online social network. Ability to identify critical nodes and content-specific network patterns of communication has implications for the development and maintenance of support networks for health behavior change. Analysis of the frequency and content of health-related social network data can inform the development of tailored behavioral interventions that provide persuasive and targeted support for initiating or adhering to a positive behavior change

Personal Learning with Social Media:Reputation, Privacy and Identity Perspectives

Social media platforms are increasingly used in recent years to support learning activities, especially for the construction of activity- and learner-centric personal learning environments (PLEs). This thesis investigates the solutions to four essential design requirements for social media based PLEs: support for help seeking, privacy protection, identity management and activity monitoring, as well as context awareness. Three main components of the thesis, reputation, privacy, and identity, are built upon these four design requirements. We investigate the three components through the following research questions. How do we help learners to find suitable experts or peers who they can learn from or collaborate with in a particular learning context? How can we design a proper privacy mechanism to make sure the information shared by learners is only disclosed to the intended audience in a given context? What identity scheme should be used to preserve the privacy of learners while also providing personalized learning experience, especially for teenage learners? To tackle the design requirement of support for help seeking, we address the reputation dimension in the context of personal learning for doctoral studies, where doctoral students need to find influential experts or peers in a particular domain. We propose an approach to detect a domain-specific community in academic social media platforms. Based on that, we investigate the influence of scholars taking both their academic and social impact into account. We propose a measure called R-Index that aggregates the readership of a scholar's publications to assess her academic impact. Furthermore, we add the social dimension into the influence measure by adopting network centrality metrics in a domain-specific community. Our results show that academic influence and social influence measures do not strongly correlate with each other, which implies that, adding the social dimension could enhance the traditional impact metrics that only take academic influence into account. Moreover, we tackle the privacy dimension of designing a PLE in the context of higher education. To protect against unauthorized access to learners' data, we propose a privacy control approach that allows learners to specify the audience, action, and artifact for their sharing behavior. Then we introduce the notion of privacy protocol with which learners can define fine-grained sharing rules. To provide a usable application of the privacy protocol in social media based PLEs, we exploit the space concept that provides an easy way for users to define the privacy protocols within a particular context. The proposed approach is evaluated through two user studies. The results reveal that learners confirm the usefulness and usability of the privacy enhanced sharing scheme based on spaces. In the last part of the thesis, we study the identity dimension in the context of STEM education at secondary and high schools. To support personalization while also preserving learners' privacy, we propose a classroom-like pseudonymity scheme that allows tracking of learners' activities while keeping their real identities undisclosed. In addition, we present a data storage mechanism called Vault that allows apps to store and exchange data within the scope of a Web-based inquiry learning space