Changing seasons: examining three decades of women's writing in Greater Syria and Egypt

Throughout the last three decades, the Arab region has attracted the unwanted attention of the rest of the world because of its spiralling political upheaval. This unrest has caused migration, economic and cultural changes, and eventually a spring of revolutions and protests in demand of reform. Arab countries are now in the spotlight of global current affairs, and all the imperfections regarding their cultural, social, and gender inequalities have surfaced to the foreground. Arab women novelists have been addressing feminist issues for centuries, chipping away at the stereotypical image of the meek and voiceless Arab woman that comes hand in hand with Orientalism. Through their fiction, writers such as Nawal El Saadawi, Hanan Al- Shaykh and Fadia Faqir have promulgated a bold brand of Arab feminist thought. This interdisciplinary thesis explores the Greater Syrian and Egyptian woman's novel written between 1975 and 2007. Through the in-depth analysis of Arab women's novels available in English, I attempt to uncover the many reasons behind today's gender inequality in Greater Syria and Egypt. By examining contemporary Arabic narrative styles and cultivating traditional Arab story-telling methods, the creative element of this thesis uses fiction to expose social and political injustice. The novel within this thesis challenges different forms of patriarchy that are dominant in the region, and endeavours to document a historical, on-going revolution

Tweeting Your Mental Health: an Exploration of Different Classifiers and Features with Emotional Signals in Identifying Mental Health Conditions

Applying simple natural language processing methods on social media data have shown to be able to reveal insights of specific mental disorders. However, few studies have employed fine-grained sentiment or emotion related analysis approaches in the detection of mental health conditions from social media messages. This work, for the first time, employed fine-grained emotions as features and examined five popular machine learning classifiers in the task of identifying users with self-reported mental health conditions (i.e. Bipolar, Depression, PTSD, and SAD) from the general public. We demonstrated that the support vector machines and the random forests classifiers with emotion-based features and combined features showed promising improvements to the performance on this task

Detecting suicide ideation in the era of social media: the population neuroscience perspective

Social media platforms are increasingly used across many population groups not only to communicate and consume information, but also to express symptoms of psychological distress and suicidal thoughts. The detection of suicidal ideation (SI) can contribute to suicide prevention. Twitter data suggesting SI have been associated with negative emotions (e.g., shame, sadness) and a number of geographical and ecological variables (e.g., geographic location, environmental stress). Other important research contributions on SI come from studies in neuroscience. To date, very few research studies have been conducted that combine different disciplines (epidemiology, health geography, neurosciences, psychology, and social media big data science), to build innovative research directions on this topic. This article aims to offer a new interdisciplinary perspective, that is, a Population Neuroscience perspective on SI in order to highlight new ways in which multiple scientific fields interact to successfully investigate emotions and stress in social media to detect SI in the population. We argue that a Population Neuroscience perspective may help to better understand the mechanisms underpinning SI and to promote more effective strategies to prevent suicide timely and at scale

Emotive ontology: extracting fine-grained emotions from terse, informal messages

A range of new biphenylazepinium salt organocatalysts effective for asymmetric epoxidation has been developed incorporating an additional substituted oxazolidine ring, and providing improved enantiocontrol in alkene epoxidation over the parent structure. Starting from enantiomerically pure amino-alcohols, tetracyclic iminium salts were obtained as single diastereoisomers through an atroposelective oxazolidine formation

What about mood swings? Identifying depression on Twitter with temporal measures of emotions

Depression is among the most commonly diagnosed mental disorders around the world. With the increasing popularity of online social network platforms and the advances in data science, more research efforts have been spent on understanding mental disorders through social media by analysing linguistic style, sentiment, online social networks and other activity traces. However, the role of basic emotions and their changes over time, have not yet been fully explored in extant work. In this paper, we proposed a novel approach for identifying users with or at risk of depression by incorporating measures of eight basic emotions as features from Twitter posts over time, including a temporal analysis of these features. The results showed that emotion-related expressions can reveal insights of individuals’ psychological states and emotions measured from such expressions show predictive power of identifying depression on Twitter. We also demonstrated that the changes in an individual’s emotions as measured over time bear additional information and can further improve the effectiveness of emotions as features, hence, improve the performance of our proposed model in this task

Emotive ontology: extracting fine-grained emotions from terse, informal messages

With the uptake of social media, such as Facebook and Twitter, there is now a vast amount of new user generated content on a daily basis, much of it in the form of short, informal free-form text. Businesses, institutions, governments and law enforcement organisations are now actively seeking ways to monitor and more generally analyse public response to various events, products and services. Our primary aim in this project was the development of an approach for capturing a wide and comprehensive range of emotions from sparse, text based messages in social-media, such as Twitter, to help monitor emotional responses to events. Prior work has focused mostly on negative / positive sentiment classification tasks, and although numerous approaches employ highly elaborate and effective techniques with some success, the sentiment or emotion granularity is generally limiting and arguably not always most appropriate for real-world problems. In this paper we employ an ontology engineering approach to the problem of fine-grained emotion detection in sparse messages. Messages are also processed using a custom NLP pipeline, which is appropriate for the sparse and informal nature of text encountered on micro-blogs. Our approach detects a range of eight high-level emotions; anger, confusion, disgust, fear, happiness, sadness, shame and surprise. We report f-measures (recall and precision) and compare our approach to two related approaches from recent literature. © 2013 IADIS

National security and social media monitoring: a presentation of the emotive and related systems

Today social media streams, such as Twitter, represent vast amounts of 'real-time' daily streaming data. Topics on these streams cover every range of human communication, ranging from banal banter, to serious reactions to events and information sharing regarding any imaginable product, item or entity. It has now become the norm for publicly visible events to break news over social media streams first, and only then followed by main stream media picking up on the news. It has been suggested in literature that social-media are a valid, valuable and effective real-time tool for gauging public subjective reactions to events and entities. Due to the vast big-data that is generated on a daily basis on social media streams, monitoring and gauging public reactions has to be automated and most of all scalable - i.e. human, expert monitoring is generally unfeasible. In this paper the EMOTIVE system, a project funded jointly by the DSTL (Defence Science and Technology Laboratory) and EPSRC, which focuses on monitoring fine-grained emotional responses relating to events of national security importance, will be presented. Similar systems for monitoring national security events are also presented and the primary traits of such national security social media monitoring systems are introduced and discussed

The role of visualisations in social media monitoring systems

Social-Media streams are constantly supplying vast volumes of real-time User Generated Content through platforms such as Twitter, Facebook, and Instagram, which makes it a challenge to monitor and understand. Understanding social conversations has now become a major interest for businesses, PR and advertising agencies, as well as law enforcement and government bodies. Monitoring of social-media allows us to observe large numbers of spontaneous, real-time interactions and varied expression of opinion, often fleeting and private. However, human, expert monitoring is generally unfeasible due to the high volumes of data. This has been a major reason for recent research and development work looking at automated social-media monitoring systems. Such systems often keep the human "out of the loop" as an NLP (Natural Language Processing) pipeline and other data-mining algorithms deal with analysing and extracting features and meaning from the data. This is plagued by a variety of problems, mostly due to the heterogenic, inconsistent and context-poor nature of social-media data, where as a result the accuracy and efficacy of such systems suffers. Nevertheless, automated social-media monitoring systems provide for a scalable, streamlined and often efficient way of dealing with big-data streams. The integration of processing outputs from automated systems and feedback to human experts is a challenge and deserves to be addressed in research literature. This paper will establish the role of the human in the social-media monitoring loop, based on prior systems work in this area. The focus of our investigation will be on use of visualisations for effective feedback to human experts. A specific, custom built system’s case-study in a social-media monitoring scenario will be considered and suggestions on how to bring back the human “into the loop” will be provided. Also some related ethical questions will be briefly considered. It is hoped that this work will inform and provide valuable insight to help improve development of automated social-media monitoring systems

National Security and Social Media Monitoring: A Presentation of the EMOTIVE and Related Systems

Today social media streams, such as Twitter, represent vast amounts of 'real-time' daily streaming data. Topics on these streams cover every range of human communication, ranging from banal banter, to serious reactions to events and information sharing regarding any imaginable product, item or entity. It has now become the norm for publicly visible events to break news over social media streams first, and only then followed by main stream media picking up on the news. It has been suggested in literature that social-media are a valid, valuable and effective real-time tool for gauging public subjective reactions to events and entities. Due to the vast big-data that is generated on a daily basis on social media streams, monitoring and gauging public reactions has to be automated and most of all scalable - i.e. human, expert monitoring is generally unfeasible. In this paper the EMOTIVE system, a project funded jointly by the DSTL (Defence Science and Technology Laboratory) and EPSRC, which focuses on monitoring fine-grained emotional responses relating to events of national security importance, will be presented. Similar systems for monitoring national security events are also presented and the primary traits of such national security social media monitoring systems are introduced and discussed

Digital Platform Uses for Help and Support Seeking of Parents With Children Affected by Disabilities: Scoping Review

Background: Receiving a diagnosis that leads to severe disability in childhood can cause a traumatic experience with long-lasting emotional stress for patients and family members. In recent decades, emerging digital technologies have transformed how patients or caregivers of persons with disabilities manage their health conditions. As a result, information (eg, on treatment and resources) has become widely available to patients and their families. Parents and other caregivers can use digital platforms such as websites or social media to derive social support, usually from other patients and caregivers who share their lived experiences, challenges, and successes on these platforms. However, gaps remain in our understanding of platforms that are most frequently used or preferred among parents and caregivers of children with disabilities. In particular, it is not clear what factors primarily drive or discourage engagement with these digital tools and what the main ethical considerations are in relation to these tools. Objective: We aimed to (1) identify prominent digital platforms used by parents or caregivers of children with disabilities; (2) explore the theoretical contexts and reasons for digital platform use, as well as the experiences made with using these platforms reported in the included studies; and (3) identify any privacy and ethical concerns emerging in the available literature in relation to the use of these platforms. Methods: We conducted a scoping review of 5 academic databases of English-language articles published within the last 10 years for diseases with childhood onset disability and self-help or parent/caregiver-led digital platforms. Results: We identified 17 papers in which digital platforms used by parents of affected children predominantly included social media elements but also search engines, health-related apps, and medical websites. Information retrieval and social support were the main reasons for their utilization. Nearly all studies were exploratory and applied either quantitative, qualitative, or mixed methods. The main ethical concerns for digital platform users included hampered access due to language barriers, privacy issues, and perceived suboptimal advice (eg, due to missing empathy of medical professionals). Older and non–college-educated individuals and ethnic minorities appeared less likely to access information online. Conclusions: This review showed that limited scientifically sound knowledge exists on digital platform use and needs in the context of disabling conditions in children, as the evidence consists mostly of exploratory studies. We could highlight that affected families seek information and support from digital platforms, as health care systems seem to be insufficient for satisfying knowledge and support needs through traditional channels