Overview of the FIRE 2022 track: Information Retrieval from Microblogs during Disasters (IRMiDis)

Microblogging sites such as Twitter play an important role in dealing with various mass emergencies including natural disasters and pandemics. The FIRE 2022 track on Information Retrieval from Microblogs during Disasters (IRMiDis) focused on two important tasks – (i) to detect the vaccine-related stance of tweets related to COVID-19 vaccines, and (ii) to detect reporting of COVID-19 symptom in tweets. © 2022 Copyright for this paper by its authors.

Classification of Covid-19 Vaccine Opinion and Detection of Symptom-Reporting on Twitter Using Neural Networks

This paper describes my work for the Information Retrieval from Microblogs during Disasters.This track is divided into two sub-tasks. Task 1 is to build an effective classifier for 3-class classification on tweets with respect to the stance reflected towards COVID-19 vaccines.Task 2 is to devise an effective classifier for 4-class classification on tweets that can detect tweets that report someone experiencing COVID-19 symptoms.This paper proposes a classification method based on MLP classifier model.The evaluation shows the performance of our approach, which achieved 0.304 on F-Score in Task 1 and 0.239 on F-Score in Task 2. © 2022 Copyright for this paper by its authors.

CTC: COVID-19 Tweet Classification using CT-BERT

CTC is my submitted work to the Information Retrieval from Microblogs during Disasters (IRMiDis) Track at the Forum for Information Retrieval Evaluation (FIRE) 2022. Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus experience a mild to moderate respiratory illness and recover without requiring special treatment. However, some become seriously ill and require medical attention. Vaccines against coronavirus and prompt reporting of symptoms saved many lives during the pandemic. The analysis of COVID-19-related tweets can provide valuable insights regarding the stance of people toward the new vaccine. It can also help the authorities to plan their strategies based on people's opinions about the vaccine and ensure the effectiveness of vaccination campaigns. Tweets describing symptoms can also aid in identifying high-alert zones and determining quarantine regulations. The IRMiDis track focuses on these COVID-19-related tweets that flooded Twitter. I developed an effective classifier for both Tasks 1 and 2. The evaluation score of my submitted run is reported in terms of accuracy and macro-F1 score. I achieved an accuracy of 0.770, a macro-F1 score of 0.773 in Task 1, and an accuracy of 0.820, a macro-F1 score of 0.746 in Task 2. I enjoyed the first rank among other submissions in both the tasks. © 2022 Copyright for this paper by its authors.

Detecting COVID-19 Vaccine Stance and Symptom Reporting from Tweets using Contextual Embeddings

Determining sentiments of the public with regard to COVID-19 vaccines is crucial for nations to efficiently carry out vaccination drives and spread awareness. Hence, it is a field requiring accurate analysis and captures the interest of many researchers. Microblogs from social media websites such as Twitter sometimes contain colloquial expressions or terminology difficult to interpret making the task a challenging one. In this paper, we propose a method for multi-label text classification for the track of”Information Retrieval from Microblogs during Disasters (IRMiDis)” presented by the”Forum of Information Retrieval Evaluation” in 2022, related to vaccine sentiment among the public and reporting of someone experiencing COVID-19 symptoms. The following methodologies have been utilised: (i) Word2Vec and (ii) BERT, which uses contextual embedding rather than the fixed embedding used by conventional natural language models. For Task 1, the overall F1 score and Accuracy are 0.503 and 0.529, respectively, placing us fourth among all the teams, while for Task 2, they are 0.740 and 0.790, placing us second among all the teams who submitted their work. Our code is openly accessible through GitHub. 1 © 2022 Copyright for this paper by its authors.

Intensive care nurses' experiences of caring for patients during the COVID-19 pandemic based on an analysis of blog posts.

BACKGROUND: In 2019, coronavirus disease 2019 (COVID-19) broke out worldwide, leading to a pandemic. Studies have shown that COVID-19 patients in intensive care units (ICUs) require more nursing care than other patients. ICU nurses who care for patients with COVID-19 have shown signs of psychological and physical strain. AIM: The aim of this study was to illuminate ICU nurses' experiences of caring for patients with COVID-19 in ICUs during the first wave of the pandemic. DESIGN: A qualitative, descriptive and inductive approach was used. METHOD: A total of 70 blog posts from 13 bloggers in the United States, Great Britain, Finland and Sweden were analysed using qualitative inductive manifest content analysis. RESULTS: The results reveal an overall theme: 'An overturned existence under extreme conditions'. Furthermore, three categories-'the virus caused changes in work and private lives', 'unreasonable demands', and to hold on to caring ideals thanks to the support of others'-and seven subcategories were identified. CONCLUSION: Caring for patients with COVID-19 during the first wave of the pandemic was demanding because of a lack of knowledge about the disease and the severity of the illness. This led to ICU nurses experiencing extreme conditions that affected various aspects of their lives. Support from colleagues and teamwork were revealed to be particularly important for how nurses dealt with the demands of working during a pandemic, as was sufficient recovery time between work shifts. RELEVANCE TO CLINICAL PRACTICE: Work in ICUs was challenging and demanding, even before the pandemic. This study contributes to an understanding of the complex work environment that existed in hospitals during the first wave of the COVID-19 pandemic. The knowledge obtained from this study can be used to revise working conditions and identify health interventions for ICU nurses.

Emotion Recognition and Analysis Of Netizens Based On Micro-Blog During Covid-19 Epidemic

The research is about emotion recognition and analysis based on Micro-blog short text. Emotion recognition is an important field of text classification in Natural Language Processing. The data of this research comes from Micro-blog 100K record related to COVID-19 theme collected by Data fountain platform, the data are manually labeled, and the emotional tendencies of the text are negative, positive and neutral. The empirical part adopts dictionary emotion recognition method and machine learning emotion recognition respectively. The algorithms used include support vector machine and naive Bayes based on TFIDF, support vector machine and LSTM based on wod2vec. The five results are compared. Combined with statistical analysis methods, the emotions of netizens in the early stage of the epidemic are analyzed for public opinion. This research uses machine learning algorithm combined with statistical analysis to analyze current events in real time. It will be of great significance for the introduction and implementation of national policies.

Gaining an Insight into Learner Satisfaction in MOOCs: An Investigation through Blog Mining

MOOCs can be considered as a powerful alternative in extraordinary situations where people cannot reach formal education. In recent years, the widespread use of the internet worldwide and especially the CoVID-19 has increased the need of people for MOOCs. However, in order to increase the effectiveness of MOOCs, and to provide a better learning environment, the need to evaluate MOOCs has arisen. One of the indicators of quality in online learning is student satisfaction. Accordingly, this research aims to reveal learner satisfaction in MOOCs. The most important indicator for measuring this satisfaction in MOOCs is user comments. In this study, 39101 comments of the participants in 960 MOOCs were examined by using text mining techniques within the framework of satisfaction.

Digital Transformation in the Smart City During Covid-19: Perceptions of Citizens

The article presents an analysis of the communicative behavior of actors in cyberspace during Covid-19. The novelty of this study lies in the fact that an algorithm is presented for determining the perception and track opinions and attitude changes of metropolitan residents in terms of digital transformation. The material included Russian-language data from social networks, video hosting services, microblogs, messengers, blogs, news, reviews, and forums. The data was collected at the beginning of the third wave of Covid-19 in Russia from June 2, 2021 to June 29, 2021. The study enabled identification of digital transformation aspects that were positively perceived by the residents of Moscow (RF) and found their support;and it also made possible to identify resources the emergence and development of which could lead to an increase in social tension. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Media use for children with disabilities in the United States during COVID-19

The article briefs about the effectiveness of media use for children with disabilities in the U.S. during COVID-19. Since author's son was born with multiple disabilities 14 years ago, author has regularly debated about what media use and screen time rules are best for him and feel like author never get it right. When COVID arrived, students were suddenly home learning on screens, while still using screens for entertainment. Many popular articles were written by bloggers and influencers about managing screen time during the pandemic, but most did not address issues specific to youth with disabilities. Articles also started appearing in academic journals, but again, often lacked mention of youth with disabilities. With no guidance for their unique situation, author was on her own to navigate how to approach screen time with her son. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

An exploration into the educational benefits and challenges of a student-led blog for part-time higher education students

This study investigates the perspectives of part-time students and academics on the uses of blogs within Higher Education. It examines blogging within a socio-cultural framework through the theoretical lens of connectivism (Siemens, 2009, 2018;Downes, 2012). Qualitative methodologies are utilised in the interpretivist paradigm to understand the challenges and benefits of using a blog. This research reports Academic and student views regarding the usefulness of blogging for educational purposes, describes how and why blogs are used and reveals why uptake for some students is limited.A small-scale research project, using thematic analysis to investigate samples of student blogs and examine interview data, involved the analysis of the contents of 12 students' blogs, followed by interviews with students (n=8) and academics (n=4). This research took place at two universities in the East Midlands, and focussed on two professional education courses during the first term of the first year of study. The findings identified benefits for students, both in their academic and reflective writing and in synthesising theory with their professional practice. However, the need for appropriate training to combine pedagogical design with collaborative technologies, accessible to both staff and students, emerges as an essential priority. Moreover, it was important to understand the broader context of multiple online platforms and face-to-face communication that students are already accessing. Finally, traditional delivery models within practices and concepts of academic and student roles, i.e., expert and novice, limit the role of the 'More Knowledgeable Other' (MKO) to the academic alone, which influences how the blog was viewed, used and valued within student groups.The findings further developed Garcia et al. (2013) model of connectivism and supports that learning occurs within a fluid and dynamic context online. In this evolved model, the various students can be centrally vii active or more passive at different times but still engaged. All the actors have agency in this sense, even when they choose to behave as 'lurkers'. The findings suggest that this new model recognises the vital importance of the expert within the system and argues that, for blogs to achieve maximum benefit, the academic needs to play a central role (at least initially).Recommendations are contextualised as part of a set of potential responses to the current COVID-19 pandemic and post-pandemic climate, as blogging could play an important role in a range of online teaching scenarios in higher education (HE). (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Social support and connective affordances: Examining responses to early Covid-19 patient support seeking on microblogs

Focusing on the new Super-Topics Platform (STP) of Weibo, this study examined microblog users' responses to the support seeking of early Covid-19 patients suffering the first outbreak in China. A total of 853 patients' support-seeking messages, along with 81,000 comments to 270 patients' help-requests, were crawled and analyzed. Results showed that content characteristics influenced endorsing, sharing, and commenting by users. Furthermore, the study identified three types of social support present in Weibo viewers' comments: emotional, informational, and diffusional supports. These social support types were inherently linked to the connective affordances, which are more inclusive than paralinguistic digital affordances, of microblogs. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

The creation of educational resources through the use of blogs during the lockdown period: a case study of a nursery school

During the early stages of the Covid-19 pandemic, educational centres made a considerable effort to adapt to the unforeseen circumstances. This article analyses how a nursery school (0-3 years) used a blog between the months of March and June 2020. It examines the use of a blog to develop and implement educational ma-terials and activities that were targeted at the school's students and families who were confined at home. The use of a blog allowed the educational staff to maintain the same educational approach that the nursery school had been using since its beginnings. © GKA Ediciones, authors.

Internet-based health communication opportunities (blogs, podcasts, video channels) and the social media / Internetalapú egészségkommunikációs lehetoségek (blogok, podcastok, videócsatornák) és a közösségi média.

Összefoglaló. Az elmúlt években mind laikus, mind szakmai oldalról az internet vált az elso számú egészségügyi információforrássá, amit a COVID-19-pandémia tovább fokozott. Az online térben számos, különbözo jellegu platform áll rendelkezésre egészségkommunikációs célokra, melyek markánsan különböznek egymástól az átadható információ mennyiségében és minoségében, a létrehozásukhoz szükséges anyagi vagy idobeli ráfordításban, továbbá az ott létrehozott tartalom fogyasztási lehetoségeiben. Összefoglaló közleményünkben rendszerezve mutatjuk be a szöveg-, a hang-, illetve a videóalapú online egészségügyi edukációs formák elonyeit és hátrányait. Külön foglalkozunk a közösségi média (social media) egészségügyi vonatkozásaival, a benne rejlo lehetoségekkel, kiemelve a pandémia kapcsán felmerült problémákat. Az egyes platformok egészségüggyel kapcsolatos történelmének feldolgozása mellett gyakorlati oldalról mutatjuk be azok hasznosíthatóságát, elosegítve ezzel az online térbe terelt kollégák munkáját. Orv Hetil. 2022; 163(4): 132-139. Summary. In recent years, the internet has become the leading source of health-related information for both professionals and laymen, and this process has been further speeded up by the Covid-19 pandemic. There are many different platforms available for health communication purposes online, that vary greatly in the quantity and quality of transferable information; the time or financial input, which are necessary to create them; and the possibilities of the utilization of the created content. In our review, we present systematically the advantages and disadvantages of the text-, audio-, and video-based online health-related education platforms. We specify the health-related aspects of social media and its potential usability, focusing on the problems allied to the pandemic. We present the practical use of the different platforms from a healthcare perspective through the review of their respective histories, thus providing guidance to the colleagues working online. Orv Hetil. 2022; 163(4): 132-139.

When falsehood wins? Varied effects of sensational elements on users' engagement with real and fake posts

Based on a quantitative content analysis of microblogs on COVID-19 that is linked to the actual Weibo user engagement (comments, reposts, and likes) they received, this study investigates the role of generic/formal frames, emotional appeals, and visual elements in people's varied levels of engagement with fake and real posts. Results revealed that relative to real posts, fake posts tended to focus more on COVID incidents that happened outside of China, utilize more episodic and human-interest frames, rely more on anger and disgust emotions, and feature more pictures. More importantly, although fake posts initially received fewer user responses than real posts, they earned significantly more reactions through employing sensational elements such as anger, conflict, and morality in their content. Most of these post-level characteristics, however, exerted minimal impact on real microblogs. Consequently, as the emotionally charged and sensationally framed fake posts drive more users to comment on and repost them, fake news may diffuse faster than real news and reach a larger audience. © 2023 Elsevier Ltd

Nurses' Work Concerns and Disenchantment During the COVID-19 Pandemic: Machine Learning Analysis of Web-Based Discussions.

BACKGROUND: Web-based forums provide a space for communities of interest to exchange ideas and experiences. Nurse professionals used these forums during the COVID-19 pandemic to share their experiences and concerns. OBJECTIVE: The objective of this study was to examine the nurse-generated content to capture the evolution of nurses' work concerns during the COVID-19 pandemic. METHODS: We analyzed 14,060 posts related to the COVID-19 pandemic from March 2020 to April 2021. The data analysis stage included unsupervised machine learning and thematic qualitative analysis. We used an unsupervised machine learning approach, latent Dirichlet allocation, to identify salient topics in the collected posts. A human-in-the-loop analysis complemented the machine learning approach, categorizing topics into themes and subthemes. We developed insights into nurses' evolving perspectives based on temporal changes. RESULTS: We identified themes for biweekly periods and grouped them into 20 major themes based on the work concern inventory framework. Dominant work concerns varied throughout the study period. A detailed analysis of the patterns in how themes evolved over time enabled us to create narratives of work concerns. CONCLUSIONS: The analysis demonstrates that professional web-based forums capture nuanced details about nurses' work concerns and workplace stressors during the COVID-19 pandemic. Monitoring and assessment of web-based discussions could provide useful data for health care organizations to understand how their primary caregivers are affected by external pressures and internal managerial decisions and design more effective responses and planning during crises.

Analysis of Public Emotion and Disseminator-Audience Framework during Outbreak of Public Crisis Events: A case study of Microblog Account Xiake Island

This study crawled the cross-sectional data of the contents and comments from Microblog Account Xiake Island during the outbreak of coronavirus pneumonia as subjects, to examine the deviation and resonance association among affective fluctuations of the Chinese public, media framework, and audiences' cognitive framework. Using SnowNLP to conduct sentiment analysis of text comments, we found that during the outbreak of coronavirus pneumonia, the public spent most of the time in low-intensity negative affectivity, and the average affective propensity in response to individual microblog fluctuated greatly, and the public was easily caught in an emotional frenzy, which reduces the level of trust in government. Through a comparison of public affectivity and related epidemic data, Xiake Island focuses on reporting emotional facts, whose construction of social reality contains obvious emotional trajectories. Clustering analysis of thematic framework by LDA algorithm reveals that in terms of framework, the framework Xiake Island uses resonates to a large degree with the framework users focus on. In terms of the level of concerns over the framework, Xiake Island deviates to a certain extent from the public. This deviation, together with the strategy of focusing on reporting emotional facts, is a discursive strategy adopted by the new mainstream media to seek the reconstruction of cultural leadership. © 2022 Owner/Author.

The Use of Internet Technologies in Education of Medical Students

The article emphasizes the need for the application of Internet technologies in medical schools during the COVID-19 pandemic. The purpose of the article is to highlight the methodology and results of experimental work aimed at the verification of the effectiveness of using of Internet technologies in medical schools. The article is devoted to the questions of organizing online -learning for of medical students and verifying the efficiency of the suggested approach. The results of a survey of would-be doctors and junior medical staff are described. The research was conducted at Vinnytsia national medical university and medical colleges of Vinnytsia region in 2017-2020 years. The educational process in the distance mode encompassed distant classes of using educational blog Blogger, mental maps Mindomo and application LearningApps. At the primary stage of researching an insufficient level of professional knowledge and practical skills of medical students was determined. There were identified shortcomings in the process of professional training of future medical staff in the study of professional disciplines the traditional training and limited use of ICT in the educational process of medical schools. The empirical basis at the formative stage of the study included 247 students and 70 teachers of professional disciplines at Vinnytsia national medical university and medical colleges of Vinnytsia region. At the formative stage of the experiment students of the control group studied according to traditional methods, and their teachers of professional disciplines used traditional teaching technologies. Students of the experimental group studied according to innovative methods, and their teachers used Internet technologies. It was specified that future medical staff of the experimental group received better results, than the students of the control group and they have a higher level of professional knowledge. It has been found out that the use of Internet technologies in the research significantly improves quality and effectiveness of such training, brings the students closer to real clinical scenarios, enables them to enhance their practical skills clearly and qualitatively. The effectiveness the use of blogs, mental maps, and online exercises LearningApps in teaching professional disciplines to future medical staff was checked by conducting comprehensive assessment.The statistical verification also confirmed the effectiveness of the implementation of these modern technologies. The prospects for further research predetermined by the necessity to use the gained experience in organizing isolated distance medical learning at the studying of natural, socio-economic, and humanitarian disciplines in the difficult conditions of the war in Ukraine.

Unraveling Students' Attitude of Using Blog for Online Learning in Literature Class

The Covid 19 pandemic has caused problems and progress in learning. Issues related to the online learning process are faced by educators related to technology use. On the one hand, due to the demands of the situation, the online learning process during the pandemic led to progress in learning using technology. One of them is by using a blog. This study aims to find out students' attitudes related to learning literature by using blogs. 79 students in the English Literature study program, Faculty of Art and Education at The University of Teknokrat Indonesia, were the samples in this study. Some of the benefits of blogs were collected and students interpreted blogs positive. Higher response to the use of blogs as a medium to increase critical thought shows positive regarding the purpose of learning literature. The findings also show that there are several problems faced by students related to the use of blogs. However, the students are interested in developing argumentative skills in writing paper on blog and the internet as a medium of self-representation. © 2021 IEEE.

Topic Analysis of Public Welfare Microblogs in the Early Period of the COVID-19 Epidemic Based on LDA Model

Due to the outbreak of COVID-19 in early 2020, a flood of information and rumors about the epidemic have filled the internet, causing panic in people’s lives. During the early period of the epidemic, public welfare information with active energy had played a key role in influencing online public opinion, alleviating public anxiety and mobilizing the entire society to fight against the epidemic. Therefore, analyzing the characteristics of public welfare communication in the early period can help us better develop strategies of public welfare communication in the post-epidemic era. In China, Sina Weibo is a microblog platform based on user relationships, and it is widely used by Chinese people. In this paper, we take the public welfare microblogs released by the Weibo public welfare account “@微公益” (Micro public welfare) in the early period of the epidemic as the research object. Firstly, we collected a total of 1863 blog posts from this account from January to April in 2020, and divided them into four stages by combining the Life Cycle Theory. Then the top 10 keywords from the blog posts of different stages were extracted using word frequency statistics. Finally, the LDA topic model were utilized to find out the topics of each stage whose characteristics of public welfare communication were analyzed in detail. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research on Microblog Sentiment Analysis Based on Multimodal and Multiscale Feature Fusion

The pneumonia epidemic spread by the 2019 new coronavirus(2019-nCoV) has affected people's lives in any aspects, and has aroused widespread concern in global public opinion. In order to better grasp the real public opinion situation on the Internet and ensure the progress of epidemic prevention and public opinion analysis, this paper conducts research on netizen sentiment analysis for epidemic-related topics in the Internet community, and proposes a multimodal feature fusion solution. For the fusion of image and text modalities, Bi-LSTM and Bi-GRU are used to further learn the intrinsic correlation between modalities on the basis of bidirectional transformer feature fusion, and an image-based multi-scale feature fusion method is proposed, which can better solve the problem in this task. Experiments show that the method proposed in this paper is better than the current mainstream multimodal sentiment analysis methods. © 2022 IEEE.