Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods

A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is published on a regular basis and in most occasions is freely distributed. The present Ph.D. Thesis deals with the problem of inferring information - or patterns in general - about events emerging in real life based on the contents of this textual stream. We show that it is possible to extract valuable information about social phenomena, such as an epidemic or even rainfall rates, by automatic analysis of the content published in Social Media, and in particular Twitter, using Statistical Machine Learning methods. An important intermediate task regards the formation and identification of features which characterise a target event; we select and use those textual features in several linear, non-linear and hybrid inference approaches achieving a significantly good performance in terms of the applied loss function. By examining further this rich data set, we also propose methods for extracting various types of mood signals revealing how affective norms - at least within the social web's population - evolve during the day and how significant events emerging in the real world are influencing them. Lastly, we present some preliminary findings showing several spatiotemporal characteristics of this textual information as well as the potential of using it to tackle tasks such as the prediction of voting intentions.Comment: PhD thesis, 238 pages, 9 chapters, 2 appendices, 58 figures, 49 table

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse

COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination

A sentiment based approach to pattern discovery and classification in social media

Social media allows people to participate, express opinions, mediate their own content and interact with other users. As such, sentiment information has become an integral part of social media. This thesis presents a sentiment-based approach to analyse content and social relationships in social media.First, this thesis aims to construct building blocks for sentiment analysis in social media, using sentiment in the form of mood. To that end, the problem of supervised mood classification is investigated. This line of work provides insights into what features in a generic document classification problem can be transferred to a mood classification problem in social media. As data in social media is normally large scale, novel scalable feature sets are introduced for this task. In particular, a novel set of psycholinguistic features is proposed and validated, which does not require a supervised feature selection phase and can therefore be applied for mood analysis at a large scale. Next, under an unsupervised setting, this thesis explores the new problem of pattern discovery in social media using sentiment information. The result is the discovery of intrinsic patterns of moods, each of which can be considered as a group of moods similar to a basic emotion studied in psychology, and therefore providing valuable empirical evidence about the structure of human emotion in the social media domain in a data-driven approach.The second major contribution of this thesis explores the use of sentiment information conveyed in on-line social diaries for detection of real-world events in a large scale setting. In particular, this thesis introduces the novel concept of 'sentiment burst' and employs a stochastic model for detection, and subsequent extraction, of events in social media. The resultant model is a powerful bursty detection algorithm suitable for on-line deployment on ever-growing datasets such as social media. An additional contribution in this line of work is an effective method for evaluating and ranking events using Google Timeline. This offers an objective measure by which to evaluate event detection a topic that is largely under explored in the current literature due to a general lack of human groundtruth.Next, under an egocentric analysis, sentiment information is used to study the impact of the demographics and personalities of users on the messages they create. In particular, we examine how the age and social connectivity of on-line users correlate with the affective, topical and psycholinguistic features of the texts they author. Using a large, ground-truthed dataset of millions of users and on-line diaries, we investigate various important questions posed in social media analysis, psychology and sociology. For example, is there a difference with regard to topic, psycholinguistic features and mood in the messages written by old versus young users? What features are predictive of a user's personality? Of extraversion and introversion? Are there features that are predictive of influence? The results obtained by our sentiment-based approach are encouraging, do not require an expensive feature selection phase and thus suggest a new and promising approach for egocentric analysis in the social media domain.Finally, the sentiment information conveyed in media content is investigated with respect to the networking and interaction aspects of a social media system. Sentiment information is studied in parallel with two other common aspects of social media content: topics and linguistic styles. Sentiment information is proved in this thesis to provide additional insights into the process of community formation. It is also shown to be a powerful predictor of community membership for a message or a user at a lighter computational cost

Machine learning techniques for identification using mobile and social media data

Networked access and mobile devices provide near constant data generation and collection. Users, environments, applications, each generate different types of data; from the voluntarily provided data posted in social networks to data collected by sensors on mobile devices, it is becoming trivial to access big data caches. Processing sufficiently large amounts of data results in inferences that can be characterized as privacy invasive. In order to address privacy risks we must understand the limits of the data exploring relationships between variables and how the user is reflected in them. In this dissertation we look at data collected from social networks and sensors to identify some aspect of the user or their surroundings. In particular, we find that from social media metadata we identify individual user accounts and from the magnetic field readings we identify both the (unique) cellphone device owned by the user and their course-grained location. In each project we collect real-world datasets and apply supervised learning techniques, particularly multi-class classification algorithms to test our hypotheses. We use both leave-one-out cross validation as well as k-fold cross validation to reduce any bias in the results. Throughout the dissertation we find that unprotected data reveals sensitive information about users. Each chapter also contains a discussion about possible obfuscation techniques or countermeasures and their effectiveness with regards to the conclusions we present. Overall our results show that deriving information about users is attainable and, with each of these results, users would have limited if any indication that any type of analysis was taking place

Internet and Smartphone Use-Related Addiction Health Problems: Treatment, Education and Research

This Special Issue presents some of the main emerging research on technological topics of health and education approaches to Internet use-related problems, before and during the beginning of coronavirus disease 2019 (COVID-19). The objective is to provide an overview to facilitate a comprehensive and practical approach to these new trends to promote research, interventions, education, and prevention. It contains 40 papers, four reviews and thirty-five empirical papers and an editorial introducing everything in a rapid review format. Overall, the empirical ones are of a relational type, associating specific behavioral addictive problems with individual factors, and a few with contextual factors, generally in adult populations. Many have adapted scales to measure these problems, and a few cover experiments and mixed methods studies. The reviews tend to be about the concepts and measures of these problems, intervention options, and prevention. In summary, it seems that these are a global culture trend impacting health and educational domains. Internet use-related addiction problems have emerged in almost all societies, and strategies to cope with them are under development to offer solutions to these contemporary challenges, especially during the pandemic situation that has highlighted the global health problems that we have, and how to holistically tackle them

Live blogging criminal trials: An exploration the impact on Danish and Swedish legal professionals

Live blogging from legal trials has become one of the most accessi-ble ways in which the public can gain direct insight into legal proceedings. Whilst live blogging constitutes an important way of ensuring open justice – a key component of many democratic societies – how this immediate and detailed surveillance impacts on the legal professionals being depicted is currently unknown. By drawing on interviews with legal professionals, this article asks how live blogs impact on the legal professionals represented as well as how they interact with and incorporate live blogs into their work life practices. The article finds that live blogs have quickly become a normalized aspect of legal professionals’ courtroom work in Den-mark and Sweden, however live blogs’ impact on their work life remains ambiguous and contentious