752 research outputs found

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    Social Media Analytics in Disaster Response: A Comprehensive Review

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    Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

    A Review of Deep Learning Models for Twitter Sentiment Analysis: Challenges and Opportunities

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    Microblogging site Twitter (re-branded to X since July 2023) is one of the most influential online social media websites, which offers a platform for the masses to communicate, expresses their opinions, and shares information on a wide range of subjects and products, resulting in the creation of a large amount of unstructured data. This has attracted significant attention from researchers who seek to understand and analyze the sentiments contained within this massive user-generated text. The task of sentiment analysis (SA) entails extracting and identifying user opinions from the text, and various lexicon-and machine learning-based methods have been developed over the years to accomplish this. However, deep learning (DL)-based approaches have recently become dominant due to their superior performance. This study briefs on standard preprocessing techniques and various word embeddings for data preparation. It then delves into a taxonomy to provide a comprehensive summary of DL-based approaches. In addition, the work compiles popular benchmark datasets and highlights evaluation metrics employed for performance measures and the resources available in the public domain to aid SA tasks. Furthermore, the survey discusses domain-specific practical applications of SA tasks. Finally, the study concludes with various research challenges and outlines future outlooks for further investigation

    The Influencer Effect: Exploring the Persuasive Communication Tactics of Social Media Influencers in the Health and Wellness Industry

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    With the emergence of social media platforms such as Instagram and TikTok, social media influencers (SMIs) have been a growing source of information in the health and wellness industry. Through their creative, informative, and appealing content, SMIs have the innate ability to reach and attain a large following on social media platforms. The purpose of this study is to ascertain an understanding of the persuasive tactics employed by SMIs in the creation and dissemination of information in the health and wellness industry. Using the theoretical framework of Aristotle’s Rhetorical Appeals and Fisher’s Narrative Paradigm, this qualitative study seeks to examine the key persuasive tactics used by SMIs in the health and wellness industry. Using content analysis, the social media content of SMIs was collected and analyzed to find emerging themes related to the rhetorical appeals and narration. In addition, a comparative analysis of the persuasive tactics used by SMIs and subject-matter experts (SMEs) was conducted. Findings showed that SMIs rely heavily on the appeals that allowed them to present themselves as credible, relatable, and similar to their followers; SMEs rely strongly on the logos appeal using technical language, memes, and textual graphics to educate the audience. Through this study, using the findings of the content and comparative analysis, a list of best practices of key persuasive tactics has been established to enable SMEs to be more effective in encouraging online users to adopt health information

    A BERT Framework to Sentiment Analysis of Tweets

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    Sentiment analysis has been widely used in microblogging sites such as Twitter in recent decades, where millions of users express their opinions and thoughts because of its short and simple manner of expression. Several studies reveal the state of sentiment which does not express sentiment based on the user context because of different lengths and ambiguous emotional information. Hence, this study proposes text classification with the use of bidirectional encoder representations from transformers (BERT) for natural language processing with other variants. The experimental findings demonstrate that the combination of BERT with CNN, BERT with RNN, and BERT with BiLSTM performs well in terms of accuracy rate, precision rate, recall rate, and F1-score compared to when it was used with Word2vec and when it was used with no variant

    Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals

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    General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/

    The effects of twitter sentiment on renewable energy stock's returns : a Portuguese study about EDP renováveis stocks

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    Investors’ rationality in the decision-making process has been topic of discussion in the last decades due to conflicts between schools of thought. Several anomalies in the Efficient Market Hypothesis (EMH) led to a new line of thought in the matter of rationality called behavior finance. Sentiment analysis is one branch of this new school of thought who studies investors’ emotions influence on economic variables. There is no consensus between academics if these emotions can make the investment decision biased or not. The aim of this paper is to observe if the prevailing sentiment in tweets can predict the stock returns for a renewable energy company of the Portuguese market. This study looks at the second biggest company by capitalizations of the Portuguese market, EDP Renováveis (EDPR), in the period from the June 1st 2021, to June 1st 2022, and finds no significant evidence of a relationship between Twitter mood and EDP Renováveis stock returns. The reasons for this result might be explained by EDPR belonging to a very small and concentrated market, corroborating the existing theory, as well as the stakeholder composition of the company only having a very small percentage of individual investors, being this kind of investors the most influenced by biases and heuristics present in the tweets. These findings have implications for the development of the sentiment analysis theory, giving more details of the influence of sentiment in smaller and concentrated market, in the renewable energy branch, and in the period of the beginning of the war between Ukraine and Russia and the worldwide economic recovery from the Covid-19 pandemic.A racionalidade dos investidores no processo de decisão de investimento tem sido tópico de discussão nas últimas décadas devido ao conflito entre duas linhas de pensamento diferentes. Várias anomalias que não iam de encontro com a hipótese do mercado eficiente deram origem a uma nova escola de pensamento em relação à racionalidade dos investidores chamada de finanças comportamentais. Análise de sentimentos é um dos ramos desta nova linha de pensamento que estuda a influência das emoções dos investidores em diferentes variáveis económicas. Não existe consenso entre académicos se estas emoções conseguem enviesar as decisões de investimento ou não. O objetivo desta tese é observar se o sentimento presente em tweets consegue fazer prever os retornos das ações de uma empresa de energias renováveis do mercado português. Este estudo analisa a segunda maior empresa portuguesa por capitalizações, a EDP Renováveis (EDPR), no período temporal entre o dia 1 de junho de 2021 e o dia 1 de julho de 2022, e não encontrou evidência com significância de uma relação entre o estado de espírito do Twitter e os retornos das ações da EDP Renováveis. As razões que justificam estes resultados podem ser o facto da EDPR pertencer a um mercado muito pequeno e concentrado como o português, indo de encontro com a evidência empírica, assim como a composição dos proprietários das ações da empresa ter uma percentagem muito reduzida de investidores individuais, que são o tipo de investidor mais facilmente influenciado por heurísticas presentes nos tweets. Este resultado tem implicações para o desenvolvimento da teoria de análise do sentimento, dando mais detalhes da influência deste em mercados mais pequenos e concentrados, no ramo das energias Renováveis, no período de tempo do início da guerra entre a Ucrânia e a Rússia e a recuperação financeira mundial pós-Covid-19

    Advanced analytical methods for fraud detection: a systematic literature review

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    The developments of the digital era demand new ways of producing goods and rendering services. This fast-paced evolution in the companies implies a new approach from the auditors, who must keep up with the constant transformation. With the dynamic dimensions of data, it is important to seize the opportunity to add value to the companies. The need to apply more robust methods to detect fraud is evident. In this thesis the use of advanced analytical methods for fraud detection will be investigated, through the analysis of the existent literature on this topic. Both a systematic review of the literature and a bibliometric approach will be applied to the most appropriate database to measure the scientific production and current trends. This study intends to contribute to the academic research that have been conducted, in order to centralize the existing information on this topic

    Connected World:Insights from 100 academics on how to build better connections

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