Trustworthiness in Social Big Data Incorporating Semantic Analysis, Machine Learning and Distributed Data Processing

This thesis presents several state-of-the-art approaches constructed for the purpose of (i) studying the trustworthiness of users in Online Social Network platforms, (ii) deriving concealed knowledge from their textual content, and (iii) classifying and predicting the domain knowledge of users and their content. The developed approaches are refined through proof-of-concept experiments, several benchmark comparisons, and appropriate and rigorous evaluation metrics to verify and validate their effectiveness and efficiency, and hence, those of the applied frameworks

Unlocking social media and user generated content as a data source for knowledge management

The pervasiveness of Social Media and user-generated content has triggered an exponential increase in global data volumes. However, due to collection and extraction challenges, data in many feeds, embedded comments, reviews and testimonials are inaccessible as a generic data source. This paper incorporates Knowledge Management framework as a paradigm for knowledge management and data value extraction. This framework embodies solutions to unlock the potential of UGC as a rich, real-time data source for analytical applications. The contributions described in this paper are threefold. Firstly, a method for automatically navigating pagination systems to expose UGC for collection is presented. This is evaluated using browser emulation integrated with dynamic data collection. Secondly, a new method for collecting social data without any a priori knowledge of the sites is introduced. Finally, a new testbed is developed to reflect the current state of internet sites and shared publicly to encourage future research. The discussion benchmarks the new algorithm alongside existing data extraction techniques and provides evidence of the increased amount of UGC data made accessible by the new algorithm

Use of social networking in the Middle East: student perspectives in higher education

This study aims to determine the benefits, risks, awareness, cultural factors, and sustainability, allied to social networking (SN) use in the higher education (HE) sector in Middle Eastern countries, namely Jordan, Saudi Arabia, and Turkey. Using an online survey, 1180 complete responses were collected and analyzed using the statistical confirmatory factor analysis method. The use of SN in the Middle Eastern HE sector has the capacity to promote and motivate students to acquire professional and personal skills for their studies and future workplace; however, the use of SN by tertiary students is also associated with several risks: isolation, depression, privacy, and security. Furthermore, culture is influenced by using SN use, since some countries shifted from one dimension to another based on Hofstede's cultural framework. The study new findings are based on a sample at a specific point in time within a culture. The study findings encourage academics to include SN in unit activities and assessments to reap the benefits of SN, while taking steps to mitigate any risks that SN poses to students. Although other studies in the Middle East examined the use of Learning Management System and Facebook in, HE as a means of engaging students in discussions and communications, however, this study contributes a better understanding of the benefits and risks, awareness, culture, and sustainability, associated with the use of SN in the HE sector in the Middle East. Finally, the paper concludes with an acknowledgment of the study limitations and suggestions for future research

Affective design using machine learning : a survey and its prospect of conjoining big data

Customer satisfaction in purchasing new products is an important issue that needs to be addressed in today’s competitive markets. Consumers not only need to be solely satisfied with the functional requirements of a product, and they are also concerned with the affective needs and aesthetic appreciation of the product. A product with good affective design excites consumer emotional feelings so as to buy the product. However, affective design often involves complex and multi-dimensional problems for modelling and maximising affective satisfaction of customers. Machine learning is commonly used to model and maximise the affective satisfaction, since it is effective in modelling nonlinear patterns when numerical data relevant to the patterns is available. This article presents a survey of commonly used machine learning approaches for affective design when two data streams namely traditional survey data and modern big data are used. A classification of machine learning technologies is first provided which is developed using traditional survey data for affective design. The limitations and advantages of each machine learning technology are also discussed and we summarize the uses of machine learning technologies for affective design. This review article is useful for those who use machine learning technologies for affective design. The limitations of using traditional survey data are then discussed which is time consuming to collect and cannot fully cover all the affective domains for product development. Nowadays, big data related to affective design can be captured from social media. The prospects and challenges in using big data are discussed so as to enhance affective design, in which very limited research has so far been attempted. This article provides guidelines for researchers who are interested in exploring big data and machine learning technologies for affective design

Non-invasive diagnostic tests for Helicobacter pylori infection

BACKGROUND: Helicobacter pylori (H pylori) infection has been implicated in a number of malignancies and non-malignant conditions including peptic ulcers, non-ulcer dyspepsia, recurrent peptic ulcer bleeding, unexplained iron deficiency anaemia, idiopathic thrombocytopaenia purpura, and colorectal adenomas. The confirmatory diagnosis of H pylori is by endoscopic biopsy, followed by histopathological examination using haemotoxylin and eosin (H & E) stain or special stains such as Giemsa stain and Warthin-Starry stain. Special stains are more accurate than H & E stain. There is significant uncertainty about the diagnostic accuracy of non-invasive tests for diagnosis of H pylori. OBJECTIVES: To compare the diagnostic accuracy of urea breath test, serology, and stool antigen test, used alone or in combination, for diagnosis of H pylori infection in symptomatic and asymptomatic people, so that eradication therapy for H pylori can be started. SEARCH METHODS: We searched MEDLINE, Embase, the Science Citation Index and the National Institute for Health Research Health Technology Assessment Database on 4 March 2016. We screened references in the included studies to identify additional studies. We also conducted citation searches of relevant studies, most recently on 4 December 2016. We did not restrict studies by language or publication status, or whether data were collected prospectively or retrospectively. SELECTION CRITERIA: We included diagnostic accuracy studies that evaluated at least one of the index tests (urea breath test using isotopes such as13C or14C, serology and stool antigen test) against the reference standard (histopathological examination using H & E stain, special stains or immunohistochemical stain) in people suspected of having H pylori infection. DATA COLLECTION AND ANALYSIS: Two review authors independently screened the references to identify relevant studies and independently extracted data. We assessed the methodological quality of studies using the QUADAS-2 tool. We performed meta-analysis by using the hierarchical summary receiver operating characteristic (HSROC) model to estimate and compare SROC curves. Where appropriate, we used bivariate or univariate logistic regression models to estimate summary sensitivities and specificities. MAIN RESULTS: We included 101 studies involving 11,003 participants, of which 5839 participants (53.1%) had H pylori infection. The prevalence of H pylori infection in the studies ranged from 15.2% to 94.7%, with a median prevalence of 53.7% (interquartile range 42.0% to 66.5%). Most of the studies (57%) included participants with dyspepsia and 53 studies excluded participants who recently had proton pump inhibitors or antibiotics.There was at least an unclear risk of bias or unclear applicability concern for each study.Of the 101 studies, 15 compared the accuracy of two index tests and two studies compared the accuracy of three index tests. Thirty-four studies (4242 participants) evaluated serology; 29 studies (2988 participants) evaluated stool antigen test; 34 studies (3139 participants) evaluated urea breath test-13C; 21 studies (1810 participants) evaluated urea breath test-14C; and two studies (127 participants) evaluated urea breath test but did not report the isotope used. The thresholds used to define test positivity and the staining techniques used for histopathological examination (reference standard) varied between studies. Due to sparse data for each threshold reported, it was not possible to identify the best threshold for each test.Using data from 99 studies in an indirect test comparison, there was statistical evidence of a difference in diagnostic accuracy between urea breath test-13C, urea breath test-14C, serology and stool antigen test (P = 0.024). The diagnostic odds ratios for urea breath test-13C, urea breath test-14C, serology, and stool antigen test were 153 (95% confidence interval (CI) 73.7 to 316), 105 (95% CI 74.0 to 150), 47.4 (95% CI 25.5 to 88.1) and 45.1 (95% CI 24.2 to 84.1). The sensitivity (95% CI) estimated at a fixed specificity of 0.90 (median from studies across the four tests), was 0.94 (95% CI 0.89 to 0.97) for urea breath test-13C, 0.92 (95% CI 0.89 to 0.94) for urea breath test-14C, 0.84 (95% CI 0.74 to 0.91) for serology, and 0.83 (95% CI 0.73 to 0.90) for stool antigen test. This implies that on average, given a specificity of 0.90 and prevalence of 53.7% (median specificity and prevalence in the studies), out of 1000 people tested for H pylori infection, there will be 46 false positives (people without H pylori infection who will be diagnosed as having H pylori infection). In this hypothetical cohort, urea breath test-13C, urea breath test-14C, serology, and stool antigen test will give 30 (95% CI 15 to 58), 42 (95% CI 30 to 58), 86 (95% CI 50 to 140), and 89 (95% CI 52 to 146) false negatives respectively (people with H pylori infection for whom the diagnosis of H pylori will be missed).Direct comparisons were based on few head-to-head studies. The ratios of diagnostic odds ratios (DORs) were 0.68 (95% CI 0.12 to 3.70; P = 0.56) for urea breath test-13C versus serology (seven studies), and 0.88 (95% CI 0.14 to 5.56; P = 0.84) for urea breath test-13C versus stool antigen test (seven studies). The 95% CIs of these estimates overlap with those of the ratios of DORs from the indirect comparison. Data were limited or unavailable for meta-analysis of other direct comparisons. AUTHORS' CONCLUSIONS: In people without a history of gastrectomy and those who have not recently had antibiotics or proton ,pump inhibitors, urea breath tests had high diagnostic accuracy while serology and stool antigen tests were less accurate for diagnosis of Helicobacter pylori infection.This is based on an indirect test comparison (with potential for bias due to confounding), as evidence from direct comparisons was limited or unavailable. The thresholds used for these tests were highly variable and we were unable to identify specific thresholds that might be useful in clinical practice.We need further comparative studies of high methodological quality to obtain more reliable evidence of relative accuracy between the tests. Such studies should be conducted prospectively in a representative spectrum of participants and clearly reported to ensure low risk of bias. Most importantly, studies should prespecify and clearly report thresholds used, and should avoid inappropriate exclusions