Routledge international handbook of therapeutic stories and storytelling

This unique book explores stories from educational, community, social, health, therapeutic and therapy perspectives, acknowledging a range of diverse social and cultural views in which stories are used and written by esteemed storytellers, artists, therapists and academics from around the globe. Storytelling is a major activity of human communication;it is an age-old tradition, used in many ways by different societies at different moments. Storytelling and stories can be entertaining, therapeutic and educative. The book is like the old saying a 'stitch in time'-stories are a way of dealing with difficulties before they become real problems. The book perfectly fits the context of arts, arts in health and creative arts therapies in that, through the cross-section of chapters, it touches on every single function of storytelling. The book is fascinating in the way it harnesses our day-to-day realities as seen from the storytelling perspective. It is divided into five parts, each created around a particular theme, with chapters from renowned world-class scholars on aspects of stories and storytelling. The first part is dedicated to COVID-19 stories. Part II delves into stories and therapeutic texts. Part III paints a picture of how stories can be used in educational, community and social settings for general therapeutic purposes. This somehow connects with Part IV, which examines stories and therapeutic texts in a health and therapy context. The book provides a deeper understanding of the different contexts and settings in which stories are, can and should be used. Finally, it finishes with a moving story about memory loss. It is evident in this book that stories provide consolation and encouragement to continue search for answers to our human condition. The stories and therapeutic stories and ideas around them presented in this international handbook tell the underlying truth of human existence. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A comparison study of topic modeling based literature analysis by using full texts and abstracts of scientific articles: a case of COVID-19 research

PurposeHow to extract useful information from a very large volume of literature is a great challenge for librarians. Topic modeling technique, which is a machine learning algorithm to uncover latent thematic structures from large collections of documents, is a widespread approach in literature analysis, especially with the rapid growth of academic literature. In this paper, a comparison of topic modeling based literature analysis has been done using full texts and s of articles.Design/methodology/approachThe authors conduct a comparison study of topic modeling on full-text paper and corresponding to assess the influence of the different types of documents been used as input for topic modeling. In particular, the authors use the large volumes of COVID-19 research literature as a case study for topic modeling based literature analysis. The authors illustrate the research topics, research trends and topic similarity of COVID-19 research by using Latent Dirichlet allocation (LDA) and topic visualization method.FindingsThe authors found 14 research topics for COVID-19 research. The authors also found that the topic similarity between using full-text paper and corresponding is higher when more documents are analyzed.Originality/valueFirst, this study contributes to the literature analysis approach. The comparison study can help us understand the influence of the different types of documents on the results of topic modeling analysis. Second, the authors present an overview of COVID-19 research by summarizing 14 research topics for it. This automated literature analysis can help specialists in the health and medical domain or other people to quickly grasp the structured morphology of the current studies for COVID-19.

Videoed storytelling in primary education EFL: exploring trainees' digital shift

This study explores the challenges and benefits primary education EFL trainees (N = 28) reported when designing and videoing a storytelling session originally intended to be conducted offline with young learners. This change of scenario was caused by the COVID-19 crisis. The data for the study were derived from the trainees' written reflections, focus group interviews, videos of instructional sessions and student-authored multimodal videos, which were explored to interpret trainees' creative processes while engaged in multimodal composing. The results indicate that trainees hold videoed storytelling to have a similar number of challenges and benefits as face-to-face storytelling. However, two of the reported advantages, enhanced creativity and self-confidence, sit at misconceptions based on trainees' limited knowledge of the pedagogical potential of multimodal resources. The findings have important educational implications in helping develop a pedagogy of videoed storytelling, while also highlighting the need for teacher training programs to specifically target the development of teachers' competence in multimodal pedagogy.

Network Public Opinion Detection During the Coronavirus Pandemic: A Short-Text Relational Topic Model

Online social media provides rich and varied information reflecting the significant concerns of the public during the coronavirus pandemic. Analyzing what the public is concerned with from social media information can support policy-makers to maintain the stability of the social economy and life of the society. In this article, we focus on the detection of the network public opinions during the coronavirus pandemic. We propose a novel Relational Topic Model for Short texts (RTMS) to draw opinion topics from social media data. RTMS exploits the feature of texts in online social media and the opinion propagation patterns among individuals. Moreover, a dynamic version of RTMS (DRTMS) is proposed to capture the evolution of public opinions. Our experiment is conducted on a real-world dataset which includes 67,592 comments from 14,992 users. The results demonstrate that, compared with the benchmark methods, the proposed RTMS and DRTMS models can detect meaningful public opinions by leveraging the feature of social media data. It can also effectively capture the evolution of public concerns during different phases of the coronavirus pandemic. © 2021 Association for Computing Machinery.

Investigating Opinions on Public Policies in Digital Media: Setting up a Supervised Machine Learning Tool for Stance Classification

Supervised machine learning (SML) provides us with tools to efficiently scrutinize large corpora of communication texts. Yet, setting up such a tool involves plenty of decisions starting with the data needed for training, the selection of an algorithm, and the details of model training. We aim at establishing a firm link between communication research tasks and the corresponding state-of-the-art in natural language processing research by systematically comparing the performance of different automatic text analysis approaches. We do this for a challenging task – stance detection of opinions on policy measures to tackle the COVID-19 pandemic in Germany voiced on Twitter. Our results add evidence that pre-trained language models such as BERT outperform feature-based and other neural network approaches. Yet, the gains one can achieve differ greatly depending on the specific merits of pre-training (i.e., use of different language models). Adding to the robustness of our conclusions, we run a generalizability check with a different use case in terms of language and topic. Additionally, we illustrate how the amount and quality of training data affect model performance pointing to potential compensation effects. Based on our results, we derive important practical recommendations for setting up such SML tools to study communication texts.

A Cross-Corpus Deep Learning Approach to Social Media Emotion Classification

Emotion Detection refers to the identification of emotions from contextual data in the form of written text, such as comments, posts, reviews, publications, articles, recommendations, conversations, and so on. Because of the Internet's exponential uptake and the recent coronavirus outbreak, social media platforms have become a crucial means of sharing thoughts and ideas throughout the entire globe, creating rapid data growth through users' contributions on various platforms. The necessity to acquire knowledge of their behaviors is a matter of great concern for both internet safety and privacy. In this study, we categorize emotional sentiments using deep learning models along with hybrid approaches such as LSTM, Bi-LSTM, and CNN+LSTM. When compared to existing state-of-the-art methods, the experiments showed that the suggested strategy is more robust and achieves an expressively higher quality of emotion detection with an accuracy rate of 94.16%, including strong F1-scores on complex and difficult emotion categories such as Fear (93.85%) and Anger (94.66%) through CNN+LSTM. © 2022 IEEE.

The Corpus of Discourses on the COVID-19 Pandemic in Poland. Research Project Assumptions

The article presents a research project on linguistically profiled (quantitative and qualitative) analyses of the (sub)space of pandemic-related discourses, as well as the corpus of Polish texts concerning the SARS-CoV-2 pandemic that broke out in 2020, prepared for analytical purposes. The authors describe the following: 1. the reasons for the interest in this issue, the subject and purpose of the research and the research theoretical and methodological background -- discourse linguistics (mainly from the perspective of Jurgen Spizmuller and Ingo Warnke);2. source material of the project (mainly individual/non-institutional Internet statements that constitute the basis for the shaping of specific systems of meaning, i.e. comments posted under posts on Facebook or Twitter and the dialogical relations among them);3. problems related to the development of the pandemic discourses corpus (criteria for the selection of texts, methods of the corpus balancing, categories of metadata that shall be used for the material description);4. conclusions drawn from an exemplary analytical procedure where a section of the corpus was used;5. the potential of the above-mentioned research and possible applications of the research results.

Multimodal negative sentiment recognition of online public opinion on public health emergencies based on graph convolutional networks and ensemble learning

To improve the effect of multimodal negative sentiment recognition of online public opinion on public health emergencies, we constructed a novel multimodal fine-grained negative sentiment recognition model based on graph convolutional networks (GCN) and ensemble learning. This model comprises BERT and ViT-based multimodal feature representation, GCN-based feature fusion, multiple classifiers, and ensemble learning-based decision fusion. Firstly, the image-text data about COVID-19 is collected from Sina Weibo, and the text and image features are extracted through BERT and ViT, respectively. Secondly, the image-text fused features are generated through GCN in the constructed microblog graph. Finally, AdaBoost is trained to decide the final sentiments recognized by the best classifiers in image, text, and image-text fused features. The results show that the F1-score of this model is 84.13% in sentiment polarity recognition and 82.06% in fine-grained negative sentiment recognition, improved by 4.13% and 7.55% compared to the optimal recognition effect of image-text feature fusion, respectively. © 2023 Elsevier Ltd

Research on sentiment classification algorithm for Corona Virus Disease 2019 Weibo text

Since the outbreak of Corona Virus Disease 2019, it has had a significant impact on people's lives. In order to help the government grasp the social opinion and do more scientific and practical propaganda and public opinion guidance for prevention and control, and to fully reflect people's attitude toward the epidemic and provide data support for government departments to release epidemic prevention measures. This paper uses Corona Virus Disease 2019-related Weibo comments as the research object and analyzes their sentiment using deep learning algorithms. The number of characters in Weibo comments is usually less than 140, which belongs to the category of short texts. Due to the use of few words, random user language, and irregular grammar, these texts have poor performance in text separation and word vector expression, adversely affecting sentiment classification. In order to solve this problem, this paper constructs the BERT-DPCNN model for sentiment analysis of epidemic short texts, which can not only extract the sentence-level text dependencies but also effectively avoid the problem of gradient disappearance of deep neural networks. The experiments show that the BERT-DPCNN model has the best effect and is of great value for the sentiment classification of short epidemic text. © 2022 IEEE.

Tutoring during the pandemic: mentoring tutors' formative experiences using digital and digital multimodal texts

This survey-design study examined how 228 middle school preservice teachers perceived the implementation of digital and digital multimodal texts during course-required, mentored, tutoring sessions delivered in face-to-face and online settings prior to, during and toward the end of the COVID-19 pandemic. Tutors were able to recognize that texts could be used to elicit affective responses from their students, and had the potential to differentiate their lessons in accordance with learners' needs, but the technology challenges they faced seemed insurmountable to some. Given their lack of teaching experience, tutors struggled to determine the appropriateness of the resources and they held distinct perceptions of the accomplishments and challenges related to their tutoring sessions. Mentor responsiveness exhibited by honouring tutors' adaptive expertise can be seen as an important aspect of fostering tutors' confidence. Focusing on the role of the mentor in preservice teachers' tutoring field placements is a suggested area for future research. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Enriching contextualized semantic representation with textual information transmission for COVID-19 fake news detection: A study on English and Persian

The COVID-19 pandemic provided an infodemic situation to face people in the society with a massive amount of information due to accessing social media, such as Twitter and Instagram. These platforms have made the information circulation easy and paved the ground to mix information and misinformation. One solution to prevent an infodemic situation is avoiding false information distribution and filtering the fake news to reduce the negative impact of such news in the society. This article aims at studying the properties of fake news in English and Persian using the textual information transmitted through language in the news. To this end, the properties existed in a text based on information theory, stylometry information from raw texts, readability of the texts, and linguistic information, such as phonology, syntax, and morphology, are studied. In this study, we use the XLM-RoBERTa representation with a convolutional neural network classifier as the basic model to detect English and Persian COVID-19 fake news. In addition, we propose different learning scenarios such that different feature sets are concatenated with the contextualized representation. According to the experimental results, adding any of the textual information to the basic model has improved the performance of the classifier for both English and Persian. Information about readability of the texts and stylometry features have been the most effective features for detecting English fake news and improved the performance by 2.72% based on F-measure. Augmenting this feature setting with the information amount and linguistic morphological information improved the performance of the classifier by 3.79% based on F-measure for Persian. [ FROM AUTHOR] Copyright of Digital Scholarship in the Humanities is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Meta-framework for Spatiotemporal Quantity Extraction from Text

News events are often associated with quantities (e.g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events. This paper thus formulates the NLP problem of spatiotemporal quantity extraction, and proposes the first meta-framework for solving it. This meta-framework contains a formalism that decomposes the problem into several information extraction tasks, a shareable crowdsourcing pipeline, and transformer-based baseline models. We demonstrate the meta-framework in three domains-the COVID-19 pandemic, Black Lives Matter protests, and 2020 California wildfires-to show that the formalism is general and extensible, the crowdsourcing pipeline facilitates fast and high-quality data annotation, and the baseline system can handle spatiotemporal quantity extraction well enough to be practically useful. We release all resources for future research on this topic. © 2022 Association for Computational Linguistics.

A user-oriented multi-input Sentiment Analysis and Feedback System for mental state awareness necessitated due to COVID-19

Development in technology has led to a spike in sharing of opinions about different subjects on social media, for instance, movie or product reviews. Unprecedented COVID-19 led to forced isolation and affected mental health negatively. This paper introduces a system to detect users' emotions and mental states based on provided input. Among the different data sources available on social media, real-time Twitter data is used in this analysis. Sentiment analysis can be used as a tool at various levels, right from individual to organizational development. Deep learning algorithms like LSTM and CNN lay the foundation of this system. Python libraries and Google APIs are used to add functionalities. Earlier studies only focused on detecting emotions, whereas the proposed system provides the user with a graphical analysis of detected emotions and apt suggestions like motivational quotes or videos. The system accepts multilingual text input, speech, or video input. The scope of this system is not restricted to COVID-19 related texts. This research will assist individuals and businesses and aid future development. © 2022 IEEE.

Conflicting discursive representations of overtourism in Lisbon in the Portuguese digital press

PurposeThis study aims to explore the ways in which Portuguese online news reports and opinion studies have framed the discussion about overtourism in Lisbon and its impacts on the city and its inhabitants.Design/methodology/approachDrawing on critical discourse analysis applied to media texts, this paper discusses the discursive representations of overtourism by focusing on how an emerging new discourse which constructs tourism as problematic began to challenge the established discourse – in which tourism is perceived as beneficial.FindingsAs a consequence, and to maintain the status quo, many media texts deploy strong legitimating strategies focusing on the benefits of tourism growth. These are juxtaposed with de-legitimating strategies which serve to deny problems of overtourism. Findings highlight the role the media play in shaping tourism discursively and uncover the complexities of discourses on the effects of (over)tourism and the ways in which they are constructed, disseminated and discussed.Social implicationsThis research is particularly relevant when newspaper opinion articles from 2021 voice the Portuguese Government's concern in bringing back to Portugal the pre-pandemic tourist numbers as soon as possible.Originality/valueThis study attempts to reveal the conflicting interests and imbalances of power among different tourism stakeholders by taking a qualitative, critical approach to the analysis of media discourse as a social practice within the broader socio-political context. This study argues that from an analytical-methodological perspective, media discourse is an optimum research site to critically explore how conflicting interests are positioned in the mass media and how this shapes public opinion.

A natural language processing system for national COVID-19 surveillance in the US Department of Veterans Affairs

Timely and accurate accounting of positive cases has been an important part of the response to the COVID-19 pandemic. While most positive cases within Veterans Affairs (VA) are identified through structured laboratory results, some patients are tested or diagnosed outside VA so their clinical status is documented only in free-text narratives. We developed a Natural Language Processing pipeline for identifying positively diagnosed COVID-19 patients and deployed this system to accelerate chart review. As part of the VA national response to COVID-19, this process identified 6,360 positive cases which did not have corresponding laboratory data. These cases accounted for 36.1% of total confirmed positive cases in VA to date. With available data, performance of the system is estimated as 82.4% precision and 94.2% recall. A public-facing implementation is released as open source and available to the community. © ACL 2020.All right reserved.

Negotiating digital public spaces: context, purpose and audiences

PurposeThe purpose of this article is to investigate digital public spaces and audiences and to explore the relationship of digital public spaces to both ideas of nationhood and physical public institutions.Design/methodology/approachThe article investigates tensions arising from the conjuncture of public spaces and digital culture through the lens of the Digital Public Library of America (DPLA). This research uses qualitative content analysis of a range of data sources including semi-structured interviews, primary texts and secondary texts.FindingsThe construction of the public library space as a digital entity does not attract anticipated audiences. Additionally, the national framing of the DPLA is not compatible with how audiences engage with digital public spaces.Originality/valueDrawing on original, qualitative data, this article engages with the prevalent but undertheorized concept of digital public spaces. The article addresses unreflexive uses of the digital public and the assumptions connected to the imagined audiences for platforms like the DPLA.

CoviFake: A Framework to Detect and Analyze Fake COVID19 Tweets

Along with the unprecedented impact of the COVID-19 pandemic on human lives, a new crisis of fake and false information related to disease has also emerged. Primarily, social media platforms such as Twitter are used to disseminate fake information due to ease of access and their large audience. However, automatic detection and classification of fake tweets is challenging task due to the complexity and lack of contextual features of short text. This paper proposes a novel CoviFake framework to classify and analyze fake tweets related to COVID-19 using vocabulary and non-vocabulary features. For this purpose, first, we combine and enhance 'CTF' and 'COVID19 Rumor' datasets to build our COVID19-sham dataset containing 25,388 labelled tweets. Next, we extract the vocabulary and 12 non-vocabulary features to compare the performance of six state-of-the-art machine learning classifiers. Our results highlight that the Random Forest (RF) classifier achieves the highest accuracy of 94.53% with the combination of top 2,000 vocabulary and 12 non-vocabulary features. In addition, we developed a large-scale dataset of CoviTweets containing 7.88 million English tweets posted by 3.8 million users during two months (March-April, 2020). The analysis of CoviTweets leveraging our framework reveals that the dataset contains 1.64 million (20.87%) fake tweets. Furthermore, we perform an in-depth examination by assigning a 'fakeness score' to hashtags and users in CoviTweets. © 2022 IEEE.

MedConQA: Medical Conversational Question Answering System based on Knowledge Graphs

The medical conversational system can relieve doctors' burden and improve healthcare effi-ciency, especially during the COVID-19 pan-demic. However, the existing medical dialogue systems have die problems of weak scalability, insufficient knowledge, and poor controlla-bility. Thus, we propose a medical conversa-tional question-answering (CQA) system based on the knowledge graph, namely MedConQA, which is designed as a pipeline framework to maintain high flexibility. Our system utilizes automated medical procedures, including medi-cal triage, consultation, image-text drug recom-mendation, and record. Each module has been open-sourced as a tool, which can be used alone or in combination, with robust scalability. Besides, to conduct knowledge-grounded dia-logues with users, we first construct a Chinese Medical Knowledge Graph (CMKG) and col-lect a large-scale Chinese Medical CQA (CM-CQA) dataset, and we design a series of meth-ods for reasoning more intellectually. Finally, we use several state-of-the-art (SOTA) tech-niques to keep the final generated response more controllable, which is further assured by hospital and professional evaluations. We have open-sourced related code, datasets, web pages, and tools, hoping to advance future research. © 2022 Association for Computational Linguistics.

Intensive care unit physicians' challenges on occupational safety and health during the COVID-19 pandemic: a scoping review

IntroductionDemand exceeded capacity during COVID ‘surges' in certain intensive care units around the world, resulting in avoidable deaths, workload pressures on staff, longer and more intensive care, and an increased risk of staff infection during intensive interventions. A limited number of studies examined intensive care physicians' experiences and perceptions during the COVID-19 pandemic. This review summarises the available published articles related to the challenges faced by ICU consultants during the COVID-19 pandemic from an occupational safety and health perspective.Material and MethodsThe PRISMA-ScR guidelines were applied to four online databases, including Medline, Scopus, Web of Science (WOS), and PsycINFO, to identify articles published between January 2020 and October 2022. During the COVID-19 pandemic, ICU consultants' experiences and perspectives on occupational safety and health as a primary outcome are examined.ResultsThe full texts 61 articles were then considered;25 articles met the inclusion criteria, which include English language full texts of available articles, qualitative studies, and ICU consultants. Eight main themes emerged from the synthesis: COVID-19 infection, psychosocial distress, moral distress, physical distress, workplace violence, social stigma, structural and organisational issues, and risk communication. Phenomenological studies make up the majority of the qualitative research, followed by grounded theory studies and case studies.ConclusionsThe global impact of the COVID-19 pandemic on intensive care services has been catastrophic. The key to maintaining ICU services during a pandemic is preparedness, adaptation, and mitigation. Consequently, it is essential to acknowledge the ICU consultant's perspective in order to mitigate all potential ICU service disruptions. However, anticipating action for a variety of issues or challenges is best explored through a qualitative interpretive description study directed at ICU consultants with on-the-ground experience.

Evidence-Based Policymaking during the COVID-19 Crisis: Regulatory Impact Assessments and the Polish COVID-19 Restrictions

The COVID-19 pandemic transformed our understanding of the state's role during a public health crisis and introduced an array of unprecedented policy tools: ever-stricter travel restrictions, lockdowns and closures of whole branches of the economy. Evidence-based policymaking seems to be the gold standard of such high-stakes policy interventions. This article presents an empirical investigation into the regulatory impact assessments accompanying sixty-four executive acts (regulations) introducing anti-pandemic restrictions in Poland over the first year of the pandemic. To this end, the study utilises the so-called scorecard methodology, which is popular in regulatory impact assessment research. This methodology highlights the shallowness of these documents and the accompanying processes, with an absence not only of a sound evidence base behind specific anti-pandemic measures or estimates of their economic impacts, but even of the comparative data on restrictions introduced in other European Union/Organisation for Economic Co-operation and Development (OECD) countries. Overall, the collected data support the hypothesis that the ad hoc pandemic management process crowded out the law-making process through tools such as regulatory impact assessments and consultations. In other words, the genuine decision-making occurred elsewhere (with the exact process being largely invisible to public opinion and scholars) and drafting legal texts simply codified these decisions, with the law-making process becoming mere window-dressing.