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

Deliberative qualities of online abortion discourse : incivility and intolerance in the American and Irish abortion discussions on Twitter

This paper provides a big-data-scale assessment of the deliberative qualities of online abortion discussions on Twitter in the United States (2020) and Ireland (2018) by specifically focusing on two standards: civility and tolerance for constructive disagreements. Using diverse computational methods and classification, our regression analysis provides mixed evaluations. We find that incivility and intolerance are uncommon behaviours in American and Irish abortion discourse on Twitter, but we also find that these anti-deliberative behaviours are (a) generating more engagements and thereby distorting the overall discussion atmosphere; (b) largely coming from the pro-life tweets; (c) dominated by a small set of hyperactive participants; and that (d) intolerant users tend to communicate within homogeneous echo chambers. Our results indicate that it is crucial for online deliberation to curtail the capabilities of these superparticipants distorting and radicalising the overall online political discourse. By studying two national contexts, our results provide comparability of our findings and insights that can improve our understanding of other contentious and polarised issues more broadly

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

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

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

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

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

Unpacking uncivil society

In the era of rising populist sentiment, deep social and political polarisations, and a growing crisis of online harms, numerous scholars share concern about the impact of such uncivil populist forces on the health of liberal democracy. This article argues that we should first normatively distinguish between incivility and intolerance. We contend that the core problem of uncivil society is intolerance, not incivility. We then empirically analyse incivility and intolerance during the 2018 Irish abortion referendum and its discussions on Twitter by conducting a content analysis and qualitative textual analysis of 3,000 tweets posted between April and June 2018. The results show that despite selecting a highly emotive and polarised issue, incivility and intolerance do not dominate the Twittersphere. Furthermore, gender and political position of users were found to be associated with use of incivility and intolerance, which increased as the referendum approached