Sehaa: A big data analytics tool for healthcare symptoms and diseases detection using Twitter, Apache Spark, and Machine Learning

Smartness, which underpins smart cities and societies, is defined by our ability to engage with our environments, analyze them, and make decisions, all in a timely manner. Healthcare is the prime candidate needing the transformative capability of this smartness. Social media could enable a ubiquitous and continuous engagement between healthcare stakeholders, leading to better public health. Current works are limited in their scope, functionality, and scalability. This paper proposes Sehaa, a big data analytics tool for healthcare in the Kingdom of Saudi Arabia (KSA) using Twitter data in Arabic. Sehaa uses Naive Bayes, Logistic Regression, and multiple feature extraction methods to detect various diseases in the KSA. Sehaa found that the top five diseases in Saudi Arabia in terms of the actual aicted cases are dermal diseases, heart diseases, hypertension, cancer, and diabetes. Riyadh and Jeddah need to do more in creating awareness about the top diseases. Taif is the healthiest city in the KSA in terms of the detected diseases and awareness activities. Sehaa is developed over Apache Spark allowing true scalability. The dataset used comprises 18.9 million tweets collected from November 2018 to September 2019. The results are evaluated using well-known numerical criteria (Accuracy and F1-Score) and are validated against externally available statistics

Inventor Business Card: Prof. Naila Rabbani

The launch of "Qatar University Research Magazine" marks the university's numerous achievements in the field of scientific research. It will also serve as a platform to highlight all our research related initiatives and activities carried out by the various research centers and colleges within the university

Off and Online Journalism and Corruption

This book provides a new theoretical framework of determinants that interact together in five hierarchical levels to restrain or produce corruption. The theory suggests a multilevel analysis that tests hypotheses regarding the relations of journalism and corruption within each level and across levels in international comparative research designs. Corruption as the abuse of power for private gain is built into the journalistic, economic, political, and cultural structures of any society and is affected by its interaction within the international system. The important questions of how differences in corruption across countries can be explained or what makes it more or less in a particular society and how press freedom and social media contribute to the fight against corruption are still unanswered. This book represents a significant contribution on the way to answer these critical questions. It discusses a variety of journalism-corruption experiences that provide a wealth of results and analyses. The cases it examines extend from Cuba to Algeria, India, Saudi Arabia, Sub-Saharan African, Gulf Cooperation Countries, Arab World, and Japan. The primary contribution of this book is both theoretical and empirical. Its details as well as the general theoretical frameworks make it a useful book for scholars, academics, undergraduate and graduate students, journalists, and policy makers

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201

The Iranian-Saudi Rivalry: Prolonging the War in Yemen. External Actors, Securitisation, Sectarianisation, and Digital Media.

Despite the scale of the conflict in Yemen and the influence of external actors, few studies to date have analysed the nature, impact, and scope of their media campaigns surrounding the war. Across digital media, especially on online news platforms and social media, Iran and Saudi Arabia have exhibited a range of behaviours, in attempts to frame their involvement in the conflict. Thus, this thesis addresses the following research question: How have Saudi Arabia and Iran used digital propaganda to legitimise and frame their involvement in Yemen to international audiences? This is the first study to examine the impact of these two competing propaganda networks on the Yemen War. In doing so, it traces Iranian and Saudi securitisation narratives across the conflict, testing their success in gaining the support of elite and non-elite actors in the international arena. It also shows the ways in which these narratives have aided Iran and Saudi Arabia in their struggle for supremacy in the region. The thesis develops an innovative approach to securitisation theory. It also incorporates critical discourse analysis and visual analysis to explore how Tehran and Riyadh have used digital media as part of their regional competition. Using evidence from the most intense periods of fighting in Yemen and tension between the two actors between 2015 and 2021, the thesis show that Saudi Arabia successfully securitised their intervention in Yemen. Ironically, however, this worked to benefit Tehran far more than it did Riyadh. Several episodes of significance for the Saudi-Iranian relationship, and for the war in Yemen, are analysed, including: Operation Decisive Storm in 2015, The Riyadh Conference in 2017, instances of prominent Saudi airstrikes in 2017-18, the murder of Jamal Khashoggi in 2018 and the Houthi ‘Operation Victory from God’ in 2019. Through discursive and visual analysis, the thesis explores the ways in which the representation of these events had an impact on framing the conflict, to the detriment of the people of Yemen. Securitisation narratives, dispersed across the Internet, regularly had a sectarian tone. These narratives fanned the flames of war, preventing any room for a meaningful prospect for peace. They also exacerbated the humanitarian situation, a dynamic properly detailed in the thesis’ conclusion. Such narratives created a deeply polarising environment, in which extraordinary measures were justified. Through visual analysis, critical discourse tracing, and analysis of dynamics specific to the world of digital media, this thesis traces this process, providing a holistic analysis of the impact of the Iranian-Saudi rivalry on the war in Yemen. The thesis offers new methodological, theoretical, and empirical insights, emphasizing the importance of digital narrative warfare as a worthwhile and insightful field of study

Art and Medicine: A Collaborative Project Between Virginia Commonwealth University in Qatar and Weill Cornell Medicine in Qatar

Four faculty researchers, two from Virginia Commonwealth University in Qatar, and two from Weill Cornell Medicine in Qatar developed a one semester workshop-based course in Qatar exploring the connections between art and medicine in a contemporary context. Students (6 art / 6 medicine) were enrolled in the course. The course included presentations by clinicians, medical engineers, artists, computing engineers, an art historian, a graphic designer, a painter, and other experts from the fields of art, design, and medicine. To measure the student experience of interdisciplinarity, the faculty researchers employed a mixed methods approach involving psychometric tests and observational ethnography. Data instruments included pre- and post-course semi-structured audio interviews, pre-test / post-test psychometric instruments (Budner Scale and Torrance Tests of Creativity), observational field notes, self-reflective blogging, and videography. This book describes the course and the experience of the students. It also contains images of the interdisciplinary work they created for a culminating class exhibition. Finally, the book provides insight on how different fields in a Middle Eastern context can share critical /analytical thinking tools to refine their own professional practices

The Adoption of Antimicrobial Stewardship Programmes in Ministry of Health Hospitals in Saudi Arabia

Aim: This thesis aims to explore and investigate the level and process of adoption of Antimicrobial Stewardship Programmes (ASPs) and factors influencing their implementation in Saudi Ministry of Health (MOH) hospitals. The findings of this study will provide hospitals and policy makers with evidence-based recommendations on how barriers to ASPs adoption can be overcome, which will ultimately improve antimicrobial use and reduce antimicrobial resistance (AMR). Method: A mixed method approach was carried out using both qualitative and quantitative research methods. Semi-structured interviews were conducted with healthcare professionals in three Saudi hospitals to explore the enablers and barriers to their adoption of ASPs. A survey was then developed based on these findings to investigate the level of hospitals’ adoption of ASPs and factors influencing their implementation at a national level. Further, a case study using in-depth interviews was utilised to understand the process of ASP adoption in a Saudi hospital, and how adoption challenges were addressed. Finally, a self-administered questionnaire was used to examine patients’ knowledge and perceptions of antimicrobial use and resistance, and to evaluate the institutional role of patient education on antimicrobial use in two Saudi hospitals. The overall methodology of the research is summarised in Figure I. Results: Despite the introduction of a national ASP strategy, adoption of ASPs in Saudi MOH hospitals remains low. Organisational barriers such as the lack of senior management support, lack of supportive IT infrastructure and the shortage of ASP team members hinder hospitals’ efforts to adopt ASPs. Further barriers relate to the lack of formal enforcement by MOH and the physicians fears of patients' complications and clinical liability. Patients admitted to Saudi hospitals lack knowledge and perceptions of AMR, and the adoption of ASPs may improve hospitals’ role in patients' education. Conclusions: Despite the established benefits of ASPs, their adoption in Saudi MOH hospitals remains low. Urgent action is needed to address the strategies priorities associated with AMR, including access to antimicrobials, antimicrobial stewardship and education and research. Policy makers are urged to consider making ASPs adoption in hospitals a regulatory requirement supported by national guidelines and surveillance programmes. It is essential to increase the provision of ID and infection control residency and training programmes to meet the extreme shortage of ID physicians, pharmacists, microbiologists and infection control practitioners. Higher education institutions and teaching hospitals are required to introduce antimicrobial prescribing and stewardship competencies into undergraduate Medical, Pharmacy, Dental, Nursing and Veterinary curriculum, as well as introduction of AMR topics in order to increase knowledge and awareness of ASPs and AMR. Collaboration between ASPs adopting and non-adopting hospitals is essential to share implementation experience, strategies and solutions to overcome barriers. Healthcare specialised associations are needed to be part of AMR conversation and guide healthcare professionals’ training and accreditation. Multiple stakeholders should be actively part of the conversations around tacking AMR. Primary care, secondary care, community pharmacies and policy makers should strive to create a shared culture of responsibility among all healthcare partners to improve antimicrobial therapy and reduce risks of AMR

Event identification in social media using classification-clustering framework

In recent years, there has been increased interest in real-world event detection using publicly accessible data made available through Internet technology such as Twitter, Facebook and YouTube. In these highly interactive systems the general public are able to post real-time reactions to “real world" events - thereby acting as social sensors of terrestrial activity. Automatically detecting and categorizing events, particularly smallscale incidents, using streamed data is a non-trivial task, due to the heterogeneity, the scalability and the varied quality of the data as well as the presence of noise and irrelevant information. However, it would be of high value to public safety organisations such as local police, who need to respond accordingly. To address these challenges we present an end-to-end integrated event detection framework which comprises five main components: data collection, pre-processing, classification, online clustering and summarization. The integration between classification and clustering enables events to be detected, especially “disruptive events" - incidents that threaten social safety and security, or that could disrupt social order. We present an evaluation of the effectiveness of detecting events using a variety of features derived from Twitter posts, namely: temporal, spatial and textual content. We evaluate our framework on large-scale, realworld datasets from Twitter and Flickr. Furthermore, we apply our event detection system to a large corpus of tweets posted during the August 2011 riots in England. We show that our system can perform as well as terrestrial sources, such as police reports, traditional surveillance, and emergency calls, even better than local police intelligence in most cases. The framework developed in this thesis provides a scalable, online solution, to handle the high volume of social media documents in different languages including English, Arabic, Eastern languages such as Chinese, and many Latin languages. Moreover, event detection is a concept that is crucial to the assurance of public safety surrounding real-world events. Decision makers use information from a range of terrestrial and online sources to help inform decisions that enable them to develop policies and react appropriately to events as they unfold. Due to the heterogeneity and scale of the data and the fact that some messages are more salient than others for the purposes of understanding any risk to human safety and managing any disruption caused by events, automatic summarization of event-related microblogs is a non-trivial and important problem. In this thesis we tackle the task of automatic summarization of Twitter posts, and present three methods that produce summaries by selecting the most representative posts from real-world tweet-event clusters. To evaluate our approaches, we compare them to the state-of-the-art summarization systems and human generated summaries. Our results show that our proposed methods outperform all the other summarization systems for English and non-English corpora