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Neologisms refer to newly coined words or phrases adopted by a language, and it is a slow but ongoing process that occurs in all languages. Sometimes, rarely used or obsolete words are also considered neologisms. Certain events, such as wars, the emergence of new diseases, or advancements like computers and the internet, can trigger the creation of new words or neologisms. The COVID-19 pandemic is one such event that has rapidly led to an explosion of neologisms in the context of the disease and several other social contexts. Even the term COVID-19 itself is a newly coined term. Studying such adaptation or change and quantifying it is essential from a linguistic perspective. However, identifying newly coined terms or extracting neologisms computationally is a challenging task. The standard tools and techniques for finding newly coined terms in English-like languages may not be suitable for Bengali and other Indic languages. This study aims to use a semi-automated approach to investigate the emergence or modification of new words in the Bengali language amidst the COVID-19 pandemic. To conduct this study, a Bengali web corpus was compiled consisting of COVID-19 related articles sourced from various web sources in Bengali. The current experiment focuses solely on COVID-19-related neologisms, but the method can be adapted for general purposes and extended to other languages as well.
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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.
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BACKGROUNDS: With the rapid spread of COVID-19, strict home confinement has been implemented in most parts of Chinese regions. Millions of people were not allowed to leave their homes except for special reasons. Home confinement plays an essential role in curbing pandemic and promoting preventive behaviors, but it may affect individuals' mental health as well. OBJECTS: The objective of this study was to explore the psychological impacts of home confinement. MATERIALS AND METHODS: We collected more than 150,360 Weibo messages from 5,370 Chinese active users, and then extracted psycho-linguistic features from these messages. Psycho-linguistic analysis was carried out using the 2 (confinement vs. non-confinement) × 2 (before vs. after confinement) repeated measure analysis of variance (RM ANOVA). RESULTS: The results showed that the frequency of positive emotion words was remarkably decreased during home confinement [F (1,5368) = 7.926, p = 0.005, η2 = 0.001]. In high-endemic subgroup, home confinement also reduced the frequency of exclusion words [F (1,3445) = 4.518, p = 0.034, η2 = 0.001] and inhibition words [F (1,3445) = 10.154, p = 0.001, η2 = 0.003]. CONCLUSION: Home confinement caused a decline in the use of positive emotion words. This indicates that home confinement can increase the frequency of negative emotions. The changes of exclusion words and inhibition words in high-endemic areas may be related to the high epidemic threat and the urgent need for social distancing in these areas.
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During the COVID-19 pandemic, countries all over the world have tried to prevent the spread of the virus with measures like social distancing, movement limitation, closure of premises and shops, voluntary isolation, lockdown, and curfew. Likely, these limitations have influenced the way people moved within urban spaces. In this study, we use Twitter as a passive sensor to understand how these measures affected human mobility. We focus on the city of Milan, one of the most international and active cities in Italy, but also one of the cities most affected by the spread of the virus. We analyzed more than one million of GPS geo-tagged tweets, posted from 2019 to 2022, and results show that the pandemic has affected human mobility (in 2022, less mobility during work hours and more mobility during the evening hours), and show that social and fashion-related activities are the main reasons people move within the city. This study shows the benefits of using Twitter as a passive sensor to measure human mobility: real-time analysis (not possible with interviews and/or questionnaire) and insights of the reasons behind human mobility (not possible to get with the sole use of telephone operators data). © 2023 IEEE.
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With the widespread use of social media, online social platforms like Twitter have become a place of rapid dissemination of information―both accurate and inaccurate. After the COVID-19 outbreak, the overabundance of fake information and rumours on online social platforms about the COVID-19 pandemic has spread over society as quickly as the virus itself. As a result, fake news poses a significant threat to effective virus response by negatively affecting people's willingness to follow the proper public health guidelines and protocols, which makes it important to identify fake information from online platforms for the public interest. In this research, we introduce an approach to detect fake news using deep learning techniques, which outperform traditional machine learning techniques with a 93.1% accuracy. We then investigate the content differences between real and fake news by applying topic modeling and linguistic analysis. Our results show that topics on Politics and Government services are most common in fake news. In addition, we found that fake news has lower analytic and authenticity scores than real news. With the findings, we discuss important academic and practical implications of the study. © 2022,Asia Pacific Journal of Information Systems.All Rights Reserved.
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Worldwide, an increase in cases and severity of domestic violence (DV) has been reported as a result of social distancing measures implemented to decrease the spreading of the Coronavirus Disease (COVID-19). As one's language can provide insight in one's mental health, this pre-registered study analyzed word use in a DV online support group, aiming to investigate the impact of the COVID-19 pandemic on DV victims in an ex post facto research design. Words reflecting social support and leisure activities were investigated as protective factors against linguistic indicators of depression in 5,856 posts from the r/domesticviolence subreddit and two neutral comparison subreddits (r/changemyview & r/femalefashionadvice). In the DV support group, the average number of daily posts increased significantly by 22% from pre- to mid-pandemic. Confirmatory analysis was conducted following a registered pre-analysis plan. DV victims used significantly more linguistic indicators of depression than individuals in the comparison groups. This did not change with the onset of COVID-19. The use of negative emotion words was negatively related to the use of social support words (Spearman's rho correlation coefficient [rho] = -0.110) and words referring to leisure activities (rho = -0.137). Pre-occupation with COVID-19 was associated with the use of negative emotion words (rho = 0.148). We conclude that language of DV victims is characterized by indicators of depression and this characteristic is stable over time. Concerns with COVID-19 could contribute to negative emotions, whereas social support and leisure activities could function to some degree as protective factors. A potential weakness of this study is its cross-sectional design and the lack of experimental control. Future studies could make use of natural language processing and other advanced methods of linguistic analysis to learn about the mental health of DV victims.
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Mobile learning (ML) is extremely relevant to distance teaching. Although much is known about ML usage in teacher education, less is known about crucial points in teachers' ML adoption process under constraints such as the COVID-19 pandemic. The aim of this exploratory case study was to gain insight into the ML adoption process, including its critical points, by examining teachers' emotion-related language. This study investigated the emotional response of 32 inservice teachers to Mobile Learning (ML) adoption while attending ML training during the COVID-19 pandemic. The data were collected using semi-structured interviews (10), focus groups (3), and participants' reflections (96) at five time points. The data underwent multilevel analysis (content and linguistic analyses), revealing two critical stages during the ML adoption process and indicating several factors that may affect the quality of emotional response, thereby promoting or impeding this process. The study highlights the critical sages and their related features that must be addressed to promote optimal ML adoption in teacher education in both routine and emergency conditions.
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This paper introduces a multi-lingual database containing translated texts of COVID-19 mythbusters. The database has translations into 115 languages as well as the original English texts, of which the original texts are published by World Health Organization (WHO). This paper then presents preliminary analyses on latin-alphabet-based texts to see the potential of the database as a resource for multilingual linguistic analyses. The analyses on latin-alphabet-based texts gave interesting insights into the resource. While the amount of translated texts in each language was small, character bi-grams with normalization (lowercasing and removal of diacritics) turned out to be an effective proxy for measuring the similarity of the languages, and the affinity ranking of language pairs could be obtained. Additionally, the hierarchical clustering analysis is performed using the character bigram overlap ratio of every possible pair of languages. The result shows the cluster of Germanic languages, Romance languages, and Southern Bantu languages. In sum, the multilingual database not only offers fixed set of materials in numerous languages, but also serves as a preliminary tool to identify the language family using text-based similarity measure of bigram overlap ratio. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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The coronavirus disease 2019 (COVID-19) spread several months ago from China and it is now a global pandemic. The experience of lockdown has been an undesirable condition for people with mental health problems, including eating disorders. The present study has the aim of understanding the impact of the first wave of the COVID-19 pandemic on people with self-reported disordered eating behaviours. A linguistic analysis was carried out with regard to the online posts and comments published by 1971 individuals (86% women) in a Facebook online community focusing on EDs during the lockdown. A total of 244 posts and 3603 comments were collected during the 56 days of lockdown (from the 10th of March until the 4th of May 2020) in Italy and were analysed by Linguistic Inquiry and Word Count (LIWC) software. The results showed that words related to peer support decreased in posts over time, and that anxiety and anger increased in the published comments. Moreover, greater feelings of negativity and anxiety were found in posts and comments throughout the COVID-19 pandemic, as well as lesser use of words related to positive emotions. Thematic qualitative analysis revealed eight themes that described the main subjective components of ED symptomatology and distress during the first COVID-19 lockdown. The current findings can help in delivering tailored treatments to people with eating disorders during the COVID-19 pandemic. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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Given that the world is still affected by the COVID-19 pandemic and new words related to it are still penetrating the Macedonian language, this qualitative study examines these novel words from a linguistic point of view, stressing their meaning, formation, the word group they belong to and the emerging implications for all language levels. The sample consists of plenty of corona and COVID-19 related terms excerpted from an immense number of electronic journalistic articles published in 2021 and 2022. Processing data and reaching conclusions rests upon the method of interpretative analysis. The results indicate that the pandemic as a global phenomenon has had a huge impact on the Macedonian language as newborn words, mainly nouns and semi-compound nouns, have been introduced. Consequently, multifold implications have arisen on all language levels. In addition, the existing classification of neologisms has been enriched with new categories of neologisms due to the emergence of these recently formed words. Finally, the new corona and COVID-19 related words in the Macedonian language challenge Macedonian linguists particularly with respect to the interpretation of their meaning as well as their potential inclusion in the upcoming editions of the Macedonian monolingual dictionary. © 2022 Revista Mexicana de Ciencias Forestales. All rights reserved.
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Language is changing over time, and it is a common phenomenon for all languages. Generally, it is a slow but continuous process. The new words come or adopt in the languages, and some existing words become less frequent use or becoming obsolete in written or verbal communication. The term neologism is implying a newly coined word or expression or a phrase that is entering for common use. But sometimes or some special events like War, New disease, Computer, Internet, etc. make the change rapidly, and the COVID-19 pandemic is one such latest event. Note that the infectious disease caused by a newly discovered coronavirus is termed COVID-19, and it is now the official name of coronavirus disease. It has led to an explosion of neologism in the context of disease and several other social contexts. During this period, many new words were coined in the languages and many of these terminologies are rapidly becoming a part of our daily life. For example, some established terms like “lockdown”, “quarantine”, “isolation”, “pandemic”, etc. increased quickly the use in our daily terminology. From the linguistic point of view, the study of such change or adaptation and its quantization is very much important. This study attempted a corpus-based computational approach to explore the adaptation or creation of new words during the outbreak of COVID-19 in the Bengali language. The main components of this work are the creation of the corpus related to the COVID-19 and an algorithm to find out the neologism. For this study, a news corpus has been used. The corpus is created from the news article related to the COVID-19 from January 2020 to February 2021. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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In this paper we focus on the difficulty in judging what is called covert hate speech. We emphasize the need for a multidimensional framework when analysing covert hate speech in situ, and the need to consider the multifaceted dimension of such speech act to assess its performativity. To explain such need, we apply the test of the Rabat Plan of Action and adopt a pragmatic perspective to analyse a specific covert hate speech act, considering such speech act as both an expressive and potentially performative act. We focus on the prosecution of hate speech against a woman holding a poster during an anti-safe pass demonstration. Her poster inferred a link between conspiracy theory, the government strategy addressing the Covid pandemic and many other collectives, primarily the Jewish community. Our analysis of the sign adopts a radically context-dependent methodology combining a pragmatic approach and the Rabat Plan of Action test. We then contrast our analysis with the legal and media perspective on the issue. We conclude by suggesting the benefit of integrating pragmatic analysis with application of normative set of rules such as the Rabat Plan of Action test, even though the fluidity of meaning always poses a challenge to any authority tasked with judging such communicative events. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
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The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for “Social Distancing” and “Physical Distancing” as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms’ use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies “social distancing” and “physical distancing”. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The coronavirus disease 2019 (COVID-19) spread several months ago from China and it is now a global pandemic. The experience of lockdown has been an undesirable condition for people with mental health problems, including eating disorders. The present study has the aim of understanding the impact of the first wave of the COVID-19 pandemic on people with selfreported disordered eating behaviours. A linguistic analysis was carried out with regard to the online posts and comments published by 1971 individuals (86% women) in a Facebook online community focusing on EDs during the lockdown. A total of 244 posts and 3603 comments were collected during the 56 days of lockdown (from the 10th of March until the 4th of May 2020) in Italy and were analysed by Linguistic Inquiry and Word Count (LIWC) software. The results showed that words related to peer support decreased in posts over time, and that anxiety and anger increased in the published comments. Moreover, greater feelings of negativity and anxiety were found in posts and comments throughout the COVID-19 pandemic, as well as lesser use of words related to positive emotions. Thematic qualitative analysis revealed eight themes that described the main subjective components of ED symptomatology and distress during the first COVID-19 lockdown. The current findings can help in delivering tailored treatments to people with eating disorders during the COVID-19 pandemic.
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The current study aimed to explore the linguistic analysis of neologism related to Coronavirus (COVID-19). Recently, a new coronavirus disease COVID-19 has emerged as a respiratory infection with significant concern for global public health hazards. However, with each passing day, more and more confirmed cases are being reported worldwide which has alarmed the global authorities including the World Health Organization (WHO). In this study, the researcher uses the term neologism which means the coinage of new words. Neologism played a significant role throughout the history of epidemic and pandemic. The focus of this study is on the phenomenon of neologism to explore the creation of new words during the outbreak of COVID-19. The theoretical framework of this study is based on three components of neologism, i.e. word formation, borrowing, and lexical deviation. The researcher used the model of neologism as a research tool which is presented by Krishnamurthy in 2010. The study is also compared with the theory of onomasiology by Pavol Stekauer (1998). The secondary data have been used in this study. The data were collected from articles, books, Oxford Corpus, social media, and five different websites and retrieved from January 2020 to April 2020. The findings of this study revealed that with the outbreak of COVID-19, the majority of the people on social media and state briefings, the word-formation is utilized in the form of nouns, adjectives, and verbs. The abbreviations and acronyms are also used which are related to the current situation of COVID-19. No doubt, neologisms present colorful portrayals of various social and cultural practices of respective societies the rationale behind them all remains the same.
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BACKGROUND: The WHO has raised concerns about the psychological consequences of the current COVID-19 pandemic, negatively affecting health across societies, cultures and age-groups. METHODS: This online survey study investigated mental health, subjective experience, and behaviour (health, learning/teaching) among university students studying in Egypt or Germany shortly after the first pandemic lockdown in May 2020. Psychological assessment included stable personality traits, self-concept and state-like psychological variables related to (a) mental health (depression, anxiety), (b) pandemic threat perception (feelings during the pandemic, perceived difficulties in describing, identifying, expressing emotions), (c) health (e.g., worries about health, bodily symptoms) and behaviour including perceived difficulties in learning. Assessment methods comprised self-report questions, standardized psychological scales, psychological questionnaires, and linguistic self-report measures. Data analysis comprised descriptive analysis of mental health, linguistic analysis of self-concept, personality and feelings, as well as correlational analysis and machine learning. N = 220 (107 women, 112 men, 1 = other) studying in Egypt or Germany provided answers to all psychological questionnaires and survey items. RESULTS: Mean state and trait anxiety scores were significantly above the cut off scores that distinguish between high versus low anxious subjects. Depressive symptoms were reported by 51.82% of the student sample, the mean score was significantly above the screening cut off score for risk of depression. Worries about health (mental and physical health) and perceived difficulties in identifying feelings, and difficulties in learning behaviour relative to before the pandemic were also significant. No negative self-concept was found in the linguistic descriptions of the participants, whereas linguistic descriptions of feelings during the pandemic revealed a negativity bias in emotion perception. Machine learning (exploratory) predicted personality from the self-report data suggesting relations between personality and subjective experience that were not captured by descriptive or correlative data analytics alone. CONCLUSION: Despite small sample sizes, this multimethod survey provides important insight into mental health of university students studying in Egypt or Germany and how they perceived the first COVID-19 pandemic lockdown in May 2020. The results should be continued with larger samples to help develop psychological interventions that support university students across countries and cultures to stay psychologically resilient during the pandemic.