Social Media Geographic Information: Current developments and opportunities in urban and regional planning

This paper deals with the convergence of Social Media and Geographic Information and discusses its potential as useful source of knowledge in spatial planning. With the underlying assumption of the acknowledgement of the innovation that digital geographic information- including Spatial Data Infrastructures (SDI) and Volunteered Geographic Information (VGI)- is already bringing to urban and regional planning, the authors argue Social Media may also play an important role due to both their pervasiveness in content exchange and their emerging spatial convergence. To support this thesis, a review of best practice examples in different domain is presented in order to understand what tools are currently available and what kind of knowledge can be extracted from Social Media. On the base of this analysis, the paper present an original user-friendly tool developed by the authors to extract information from Social Media and to perform Spatial-Temporal Textual (STTx) analysis. The paper ends with some brief conclusions on the opportunities for the application of STTx analysis in urban and regional planning

Digital Shoeboxes: the history and future of personal performance archiving

Personal performance archiving describes a practice in which individuals who regularly attend live performances document their experiences, usually through the collection of documents such as programmes, playbills, cast sheets, ticket stubs, posters and leaflets. This is a form of documenting performance which intersects with the related field of serious leisure. Personal performance archiving relies on the collection and storage of physical documents, yet in this age of rapidly advancing digital technologies and social media, born-digital documents are beginning to take precedence in event management. This will undoubtedly affect these kinds of hobbyist archivists. This project strives to understand three main topics; what information can be taken from archived performance documents, how audience members are currently documenting and archiving their experience, and how the increase of digitisation and born-digital documents will affect this practice. This project used a survey to determine the current collecting and archiving preferences of modern theatregoers, several collections of physical and digitised programmes to compare style and content over different eras, and contains a literature review concerning current and future digital modes of performance documentation

Open source intelligence gathering for hate speech in Kenya

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Systems Security (MSc.ISS) at Strathmore UniversityThe Internet has been celebrated for its ability to erode barriers between nations. Social media is a powerful medium that can unite, inform, and move people. One post can start a chain of events that changes the world. It gives users fast access to and sharing of information and facilitates ease of communication. However, the Internet allows for a lot of negativity as well. There has been an increase in hate speech activities on social media in the Kenyan cyber space. The National Cohesion and Integration Commission (NCIC) was established to facilitate and promote equality of opportunity, good relations, harmony and peaceful co-existence between persons of the different ethnic and racial communities of Kenya, and to advise the Government on all aspects thereof (Act No, 12, 2008). In particular, the NCIC Act of 2008 is mandated to curb hate speech. This research studied existing hate speech detection tools in use by NCIC, then identified gaps and challenges faced. A technical solution (tool for analyzing hate speech) was proposed that can be implemented by the NCIC and the government to respond to hate-speech cases perpetrated through social media platforms. The developed tool tracked challenges and gaps in the existing tools currently in use by NCIC for hate speech monitoring, detection and analysis. Due to the differences in Application Programming Interface (API) implementation on the variety of social media platforms used in Kenya, the scope of this research is limited to Twitter. This research employed the use of predictive analytics for text classification using Naïve Bayes. A tool that uses the predictive model in assistance to detection of hate-speech online was developed to conceptualize the solutions discussed in this research

Artificial Intelligence & Machine Learning in Finance: A literature review

In the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical ResearchIn the 2020s, Artificial Intelligence (AI) has been increasingly becoming a dominant technology, and thanks to new computer technologies, Machine Learning (ML) has also experienced remarkable growth in recent years; however, Artificial Intelligence (AI) needs notable data scientist and engineers’ innovation to evolve. Hence, in this paper, we aim to infer the intellectual development of AI and ML in finance research, adopting a scoping review combined with an embedded review to pursue and scrutinize the services of these concepts. For a technical literature review, we goose-step the five stages of the scoping review methodology along with Donthu et al.’s (2021) bibliometric review method. This article highlights the trends in AI and ML applications (from 1989 to 2022) in the financial field of both developed and emerging countries. The main purpose is to emphasize the minutiae of several types of research that elucidate the employment of AI and ML in finance. The findings of our study are summarized and developed into seven fields: (1) Portfolio Management and Robo-Advisory, (2) Risk Management and Financial Distress (3), Financial Fraud Detection and Anti-money laundering, (4) Sentiment Analysis and Investor Behaviour, (5) Algorithmic Stock Market Prediction and High-frequency Trading, (6) Data Protection and Cybersecurity, (7) Big Data Analytics, Blockchain, FinTech. Further, we demonstrate in each field, how research in AI and ML enhances the current financial sector, as well as their contribution in terms of possibilities and solutions for myriad financial institutions and organizations. We conclude with a global map review of 110 documents per the seven fields of AI and ML application.   Keywords: Artificial Intelligence, Machine Learning, Finance, Scoping review, Casablanca Exchange Market. JEL Classification: C80 Paper type: Theoretical Researc

Blockchain Value Creation Logics and Financial Returns

With its complexities and portfolio-nature, the advent of blockchain technology presents several use cases to stakeholders for business value appropriation and financial gains. This 3-essay dissertation focuses on three exemplars and research approaches to understanding the value creation logics of blockchain technology for financial gains. The first essay is a conceptual piece that explores five main affordances of blockchain technology and how these can be actualized and assimilated for business value. Based on the analysis of literature findings, an Affordance-Experimentation-Actualization-Assimilation (AEAA) model is proposed. The model suggests five affordance-to-assimilation value chains and eight value interdependencies that firms can leverage to optimize their value creation and capture during blockchain technology implementation. The second essay empirically examines the financial returns of public firms\u27 blockchain adoption investments at the level of the three main blockchain archetypes (private-permissioned, public-permissioned and permissionless. Drawing upon Fichman\u27s model of the option value of innovative IT platform investments, the study examines business value creation through firm blockchain strategy (i.e., archetype instances, decentralization, and complementarity), learning (i.e., blockchain patents and event participation), and bandwagon effects using quarterly data of firm archetype investments from 2015 to 2020. The study\u27s propensity score matching utilization and fixed-effects modeling provide objective quantification of how blockchain adoption leads to increases in firm value (performance measured by Tobin\u27s q) at the archetype level (permissionless, public permissioned, and private permissioned). Surprisingly, a more decentralized archetype and a second different archetype implementation are associated with a lower Tobin\u27s q. In addition, IT-option proxy parameters such as blockchain patent originality, participation in blockchain events, and network externality positively impact firm performance, whereas the effect of blockchain patents is negative. As the foremost and more established use case of blockchain technology whose business value is accessed in either of the five affordances and exemplifies a permissionless archetype for financial gains, bitcoin cryptocurrency behavior is studied through the lens of opinion leaders on Twitter. The third essay this relationship understands the hourly price returns and volatility shocks that sentiments from opinion leaders generate and vice-versa. With a dynamic opinion leader identification strategy, lexicon and rule-based sentiment analytics, I extract sentiments of the top ten per cent bitcoin opinion leaders\u27 tweets. Controlling for various economic indices and contextual factors, the study estimates a vector autoregression model (VAR) and finds that finds that Bitcoin return granger cause Polarity but the influence of sentiment subjectivity is marginal and only stronger on bitcoin price volatility. Several key implications for blockchain practitioners and financial stakeholders and suggestions for future research are discussed

A Structured Narrative Prompt for Prompting Narratives from Large Language Models: Sentiment Assessment of ChatGPT-Generated Narratives and Real Tweets

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents\u27 perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt for sending queries to LLMs, we experiment with the narrative generation process using OpenAI\u27s ChatGPT, and we assess statistically significant differences across 11 Positive and Negative Affect Schedule (PANAS) sentiment levels between the generated narratives and real tweets using chi-squared tests and Fisher\u27s exact tests. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. In four out of forty-four categories, ChatGPT generated narratives which have sentiment scores that were not discernibly different, in terms of statistical significance (alpha level α = 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of an LLM chatbot based on simulated agents\u27 information; (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect a simulated agent\u27s experiences with real people

How crowdsourcing impacts prices and customer satisfaction: the Airbnb case

The travel accommodation industry has changed drastically due to the sharing economy. This sharing economy depends significantly on crowdsourcing in the form of quantitative and qualitative reviews. In this thesis, we study the impact of expressed sentiments in the text reviews on listing prices; we explore differences in the factors that lead to satisfaction according to customer origin and the effects of external shocks on Airbnb´s prices. We employ NLP to extract the sentiment and emotion scores to be modeled as a function of different characteristics and geophysical information. Our study provides an important contribute to the research on pricing and customer satisfaction in the sharing economy by finding a significant effect of the expression of positive sentiments on prices, describing the differences in the factors that lead to satisfaction according to customer origin, and revealing that guests moved away from main municipalities and valued listings with more bedrooms during the Covid-19 pandemic

Exploring Text Mining and Analytics for Applications in Public Security: An in-depth dive into a systematic literature review

Text mining and related analytics emerge as a technological approach to support human activities in extracting useful knowledge through texts in several formats. From a managerial point of view, it can help organizations in planning and decision-making processes, providing information that was not previously evident through textual materials produced internally or even externally. In this context, within the public/governmental scope, public security agencies are great beneficiaries of the tools associated with text mining, in several aspects, from applications in the criminal area to the collection of people's opinions and sentiments about the actions taken to promote their welfare. This article reports details of a systematic literature review focused on identifying the main areas of text mining application in public security, the most recurrent technological tools, and future research directions. The searches covered four major article bases (Scopus, Web of Science, IEEE Xplore, and ACM Digital Library), selecting 194 materials published between 2014 and the first half of 2021, among journals, conferences, and book chapters. There were several findings concerning the targets of the literature review, as presented in the results of this article

Gratitude in Healthcare an interdisciplinary inquiry

The expression and reception of gratitude is a significant dimension of interpersonal communication in care-giving relationships. Although there is a growing body of evidence that practising gratitude has health and wellbeing benefits for the giver and receiver, gratitude as a social emotion made in interaction has received comparatively little research attention. To address this gap, this thesis draws on a portfolio of qualitative methods to explore the ways in which gratitude is constituted in care provision in personal, professional, and public discourse. This research is informed by a discursive psychology approach in which gratitude is analysed, not as a morally virtuous character trait, but as a purposeful, performative social action that is mutually co-constructed in interaction.I investigate gratitude through studies that approach it on a meta, meso, macro, and micro level. Key intellectual traditions that underpin research literature on gratitude in healthcare are explored through a metanarrative review. Six underlying metanarratives were identified: social capital; gifts; care ethics; benefits of gratitude; staff wellbeing; and gratitude as an indicator of quality of care. At the meso (institutional) level, a narrative analysis of an archive of letters between patients treated for tuberculosis and hospital almoners positions gratitude as participating in a Maussian gift-exchange ritual in which communal ties are created and consolidated.At the macro (societal) level, a discursive analysis of tweets of gratitude to the National Health Service at the outset of the Covid-19 pandemic shows that attitudes to gratitude were dynamic in response to events, with growing unease about deflecting attention from risk reduction for those working in the health and social care sectors. A follow-up analysis of the clap-for-carers movement implicates gratitude in embodied, symbolic, and imagined performances in debates about care justice. At the micro (interpersonal) level, an analysis of gratitude encounters broadcast in the BBC documentary series, Hospital, uses pragmatics and conversation analysis to argue that gratitude is an emotion made in talk, with the uptake of gratitude opportunities influencing the course of conversational sequencing. The findings challenge the oftenmade distinction between task-oriented and relational conversation in healthcare.Moral economics are paradigmatic in the philosophical conceptualisation of gratitude. My research shows that, although balance-sheet reciprocity characterised the institutional culture of the voluntary hospital, it is hardly ever a feature ofinterpersonal gratitude encounters. Instead, gratitude is accomplished as shared moments of humanity through negotiated encounters infused with affect. Gratitude should never be instrumentalised as compensating for unsafe, inadequatelyrenumerated work. Neither should its potential to enhance healthcare encounters be underestimated. Attention to gratitude can participate in culture change by affirming modes of acting, emoting, relating, expressing, and connecting that intersect with care justice.This thesis speaks to gratitude as a culturally salient indicator of what people express as worthy of appreciation. It calls for these expressions to be more closely attended to, not only as useful feedback that can inform change, but also because gratitude is a resource on which we can draw to enhance and enrich healthcare as a communal, collaborative, cooperative endeavour