Depression in times of a pandemic – the impact of COVID-19 on the lay discourses of e-mental health communities

The article aims at exploring the impact of the COVID-19 pandemic on the lay discourses of depression emerging in online mental health forums. The narrative framing of depression plays a central role not only because it affects the instrumental strategies of depressed people (e.g., preferred therapy), but also because it is a constitutive element of the identity of depressed people, thus affects the process of recovery itself. COVID-19 had a serious impact on people living with mental disorders (especially depression and anxiety), thus our research aimed at mapping the consequences of these transformations on a discursive level. A textual dataset of English language online health forums was collected (n = 339,550 publicly available entries posted between 15 February 2016 and 31 December 2020). Structural topic modelling was used to explore the various discursive patterns characterizing the pre-pandemic and pandemic era. Our results show that the pandemic did not take over the discursive space of depression forums, yet it transformed many aspects of it: a new horizon of critique opened up;the biomedical authority was reinforced;the ego-centric perspectives were refined;the previously unquestionable discursive frames become fragmented;and the horizon of emergency overshadowed the previous risk perspective. [ FROM AUTHOR] Copyright of Information, Communication & Society is the property of Routledge 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 Temperature-Modified Dynamic Embedded Topic Model

Topic models are natural language processing models that can parse large collections of documents and automatically discover their main topics. However, conventional topic models fail to capture how such topics change as the collections evolve. To amend this, various researchers have proposed dynamic versions which are able to extract sequences of topics from timestamped document collections. Moreover, a recently-proposed model, the dynamic embedded topic model (DETM), joins such a dynamic analysis with the representational power of word and topic embeddings. In this paper, we propose modifying its word probabilities with a temperature parameter that controls the smoothness/sharpness trade-off of the distributions in an attempt to increase the coherence of the extracted topics. Experimental results over a selection of the COVID-19 Open Research Dataset (CORD-19), the United Nations General Debate Corpus, and the ACL Title and dataset show that the proposed model - nicknamed DETM-tau after the temperature parameter - has been able to improve the model's perplexity and topic coherence for all datasets.

Changes in public travel willingness in the post-COVID-19 era: Evidence from social network data

Amid the impact of COVID-19, the public's willingness to travel has changed, which has had a fundamental impact on the ridership of urban public transport. Usually, travel willingness is mainly analyzed by questionnaire survey, but it needs to reflect the accurate psychological perception of the public entirely. Based on Weibo text data, this paper used natural language processing technology to quantify the public's willingness to travel in the post-COVID-19 era. First, web crawler technology was used to collect microblog text data, which will discuss COVID-19 and travel at the same time. Then, based on the Naive Bayes classification algorithm, travel sentiment analysis was carried out on the data, and the relationship between public travel willingness and urban public transport ridership was analyzed by Spearman correlation analysis. Finally, the LDA topic model was used to conduct content topic research on microblog text data during and after COVID-19. The results showed that the mean values of compelling travel emotion were-0.8197 and-0.0640 during and after COVID-19, respectively. The willingness of the public to travel directly affects the ridership of urban public transport. Compared with the COVID-19 period, the public's fear of travel infection in the post-COVID-19 era has significantly improved, but it still exists. The public pays more attention to the level of COVID-19 prevention and control and the length of travel time on public transport.

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.

Pseudo-document simulation for comparing LDA, GSDMM and GPM topic models on short and sparse text using Twitter data.

Topic models are a useful and popular method to find latent topics of documents. However, the short and sparse texts in social media micro-blogs such as Twitter are challenging for the most commonly used Latent Dirichlet Allocation (LDA) topic model. We compare the performance of the standard LDA topic model with the Gibbs Sampler Dirichlet Multinomial Model (GSDMM) and the Gamma Poisson Mixture Model (GPM), which are specifically designed for sparse data. To compare the performance of the three models, we propose the simulation of pseudo-documents as a novel evaluation method. In a case study with short and sparse text, the models are evaluated on tweets filtered by keywords relating to the Covid-19 pandemic. We find that standard coherence scores that are often used for the evaluation of topic models perform poorly as an evaluation metric. The results of our simulation-based approach suggest that the GSDMM and GPM topic models may generate better topics than the standard LDA model.

Trends in COVID-19 Publications: Streamlining Research Using NLP and LDA.

Background: Research publications related to the novel coronavirus disease COVID-19 are rapidly increasing. However, current online literature hubs, even with artificial intelligence, are limited in identifying the complexity of COVID-19 research topics. We developed a comprehensive Latent Dirichlet Allocation (LDA) model with 25 topics using natural language processing (NLP) techniques on PubMed® research articles about "COVID." We propose a novel methodology to develop and visualise temporal trends, and improve existing online literature hubs. Our results for temporal evolution demonstrate interesting trends, for example, the prominence of "Mental Health" and "Socioeconomic Impact" increased, "Genome Sequence" decreased, and "Epidemiology" remained relatively constant. Applying our methodology to LitCovid, a literature hub from the National Center for Biotechnology Information, we improved the breadth and depth of research topics by subdividing their pre-existing categories. Our topic model demonstrates that research on "masks" and "Personal Protective Equipment (PPE)" is skewed toward clinical applications with a lack of population-based epidemiological research.

Evolution Analysis of Network Public Opinion Theme Based on LDA Model

Online public opinion is a collection of netizens' emotions, attitudes, opinions, opinions and so on. With the development of the Internet, the influence of online public opinion on social stability is increasing day by day. This paper takes the 'COVID-19' event as an example, crawls the relevant news and comment data released by People's Daily, and firstly divides public opinion events into four stages according to the news popularity and life cycle theory: Tf-idf algorithm is used to strengthen the selection of key feature words in the corpus. Finally, LDA theme model is used to identify the topic of public opinion and mine the evolution law of network public opinion, which is helpful to effectively guide and control network public opinion and plays an important role in social stability. © 2022 IEEE.

Public Attitudes Toward Anxiety Disorder on Sina Weibo: Content Analysis.

BACKGROUND: Anxiety disorder has become a major clinical and public health problem, causing a significant economic burden worldwide. Public attitudes toward anxiety can impact the psychological state, help-seeking behavior, and social activities of people with anxiety disorder. OBJECTIVE: The purpose of this study was to explore public attitudes toward anxiety disorders and the changing trends of these attitudes by analyzing the posts related to anxiety disorders on Sina Weibo, a Chinese social media platform that has about 582 million users, as well as the psycholinguistic and topical features in the text content of the posts. METHODS: From April 2018 to March 2022, 325,807 Sina Weibo posts with the keyword "anxiety disorder" were collected and analyzed. First, we analyzed the changing trends in the number and total length of posts every month. Second, a Chinese Linguistic Psychological Text Analysis System (TextMind) was used to analyze the changing trends in the language features of the posts, in which 20 linguistic features were selected and presented. Third, a topic model (biterm topic model) was used for semantic content analysis to identify specific themes in Weibo users' attitudes toward anxiety. RESULTS: The changing trends in the number and the total length of posts indicated that anxiety-related posts significantly increased from April 2018 to March 2022 (R2=0.6512; P<.001 to R2=0.8133; P<.001, respectively) and were greatly impacted by the beginning of a new semester (spring/fall). The analysis of linguistic features showed that the frequency of the cognitive process (R2=0.1782; P=.003), perceptual process (R2=0.1435; P=.008), biological process (R2=0.3225; P<.001), and assent words (R2=0.4412; P<.001) increased significantly over time, while the frequency of the social process words (R2=0.2889; P<.001) decreased significantly, and public anxiety was greatly impacted by the COVID-19 pandemic. Feature correlation analysis showed that the frequencies of words related to work and family are almost negatively correlated with those of other psychological words. Semantic content analysis identified 5 common topical areas: discrimination and stigma, symptoms and physical health, treatment and support, work and social, and family and life. Our results showed that the occurrence probability of the topical area "discrimination and stigma" reached the highest value and averagely accounted for 26.66% in the 4-year period. The occurrence probability of the topical area "family and life" (R2=0.1888; P=.09) decreased over time, while that of the other 4 topical areas increased. CONCLUSIONS: The findings of our study indicate that public discrimination and stigma against anxiety disorder remain high, particularly in the aspects of self-denial and negative emotions. People with anxiety disorders should receive more social support to reduce the impact of discrimination and stigma.

Exploring the Influencing Factors of COVID-19 Vaccination Willingness among Young Adults in China.

Vaccine uptake is considered as one of the most effective methods of defending against COVID-19 (coronavirus disease 2019). However, many young adults are hesitant regarding COVID-19 vaccines, and they actually play an important role in virus transmission. Based on a multi-theory model, this study aims to explore the influencing factors related to COVID-19 vaccine willingness among young adults in China. Using semi-structured interviews, this study explored the factors that would motivate young adults with vaccine hesitancy to get the COVID-19 vaccine. Thematic analysis was used to analyze the interview data with topic modeling as a complementarity method. After comparing the differences and similarities of results generated by thematic analysis and topic modeling, this study ultimately identified ten key factors related to COVID-19 vaccination intention, including the effectiveness and safety of vaccines, application range of vaccine, etc. This study combined thematic analysis with machine learning and provided a comprehensive and nuanced picture of facilitating factors for COVID-19 vaccine uptake among Chinese young adults. Results may be taken as potential themes for authorities and public health workers in vaccination campaigns.

Web-Based Conversations Regarding Fathers Before and During the COVID-19 Pandemic: Qualitative Content Analysis.

BACKGROUND: Studies of new and expecting parents largely focus on the mother, leaving a gap in knowledge about fathers. OBJECTIVE: This study aimed to understand web-based conversations regarding new and expecting fathers on social media and to explore whether the COVID-19 pandemic has changed the web-based conversation. METHODS: A social media analysis was conducted. Brandwatch (Cision) captured social posts related to new and expecting fathers between February 1, 2019, and February 12, 2021. Overall, 2 periods were studied: 1 year before and 1 year during the pandemic. SAS Text Miner analyzed the data and produced 47% (9/19) of the topics in the first period and 53% (10/19) of the topics in the second period. The 19 topics were organized into 6 broad themes. RESULTS: Overall, 26% (5/19) of the topics obtained during each period were the same, showing consistency in conversation. In total, 6 broad themes were created: fatherhood thoughts, fatherhood celebrations, advice seeking, fatherhood announcements, external parties targeting fathers, and miscellaneous. CONCLUSIONS: Fathers use social media to make announcements, celebrate fatherhood, seek advice, and interact with other fathers. Others used social media to advertise baby products and promote baby-related resources for fathers. Overall, the arrival of the COVID-19 pandemic appeared to have little impact on the excitement and resiliency of new fathers as they transition to parenthood. Altogether, these findings provide insight and guidance on the ways in which public health professionals can rapidly gather information about special populations-such as new and expecting fathers via the web-to monitor their beliefs, attitudes, emotional reactions, and unique lived experiences in context (ie, throughout a global pandemic).

Identifying informative tweets during a pandemic via a topic-aware neural language model.

Every epidemic affects the real lives of many people around the world and leads to terrible consequences. Recently, many tweets about the COVID-19 pandemic have been shared publicly on social media platforms. The analysis of these tweets is helpful for emergency response organizations to prioritize their tasks and make better decisions. However, most of these tweets are non-informative, which is a challenge for establishing an automated system to detect useful information in social media. Furthermore, existing methods ignore unlabeled data and topic background knowledge, which can provide additional semantic information. In this paper, we propose a novel Topic-Aware BERT (TABERT) model to solve the above challenges. TABERT first leverages a topic model to extract the latent topics of tweets. Secondly, a flexible framework is used to combine topic information with the output of BERT. Finally, we adopt adversarial training to achieve semi-supervised learning, and a large amount of unlabeled data can be used to improve inner representations of the model. Experimental results on the dataset of COVID-19 English tweets show that our model outperforms classic and state-of-the-art baselines.

Topic Modeling on Covid-19 Vaccination in Indonesia Using LDA Model

According to data from covid19.go.id, there is a lot of hoax news about Covid-19 vaccinations spread across various social media in Indonesia. Meanwhile, the ability to monitor and track misinformation and trends regarding Covid-19 and its spread is an important part of the response process by the media and government to dealing with fake news about Covid-19. Twitter is a social media that is actively used in spreading issues. Twitter users in Indonesia reached 18.45 million users as of January 2022. To find out useful information on Twitter social media comments regarding the Covid-19 Vaccination, a method, namely Topic Modeling, can be used. This study aims to obtain the distribution of the Covid-19 Vaccination topic on Twitter Data in Indonesia to assist the government in knowing the trend of topics related to Covid-19 vaccination and the trend of changing the topic. The dataset on Twitter used is 10,140 pieces about Covid-19 vaccinations in Indonesia in the period August 2021 to April 2022. Based on the Latent Dirichlet Association (LDA), the 5 most popular topics were obtained for each model, and spread in the fields of health, religion, society. Based on the alpha and beta hyperparameter tuning, it was found that the topic with K=5, alpha=asymmetric, and beta=0.61 was a relevant LDA topic model for the research dataset because good in topic diversity and have coherence value=0.590. © 2022 IEEE.

Covering the Crisis: Evolution of Key Topics and Actors in COVID-19 News Coverage in Switzerland

Objectives: The goal of this study is to map the share of COVID-related news articles over time, to investigate key subtopics and their evolution throughout the pandemic, and to identify key actors and their relationship with different aspects of the discourse around the pandemic. Methods: This study uses a large-scale automated content analysis to conduct a within-country comparison of news articles (N = 1,171,114) from two language regions of Switzerland during the first 18 months of the pandemic. Results: News media coverage of the pandemic largely mirrors key epidemiological developments in terms of the volume and content of coverage. Key actors in COVID-related reporting tend to be included in news articles that relate to their respective area of expertise. Conclusion: Balanced news coverage of the pandemic facilitates effective dissemination of pandemic-related information by health authorities. Copyright © 2023 Ort, Rohrbach, Diviani and Rubinelli.

Research on online reviews of popular restaurants based on Latent Dirichlet Allocation Theme Model

Online popular restaurants are those that are widely concerned by the society and sought after by the public through we media platform or internet marketing. Online comment is the product of the information age. The daily life of Internet users is to exchange information, express views and communicate with others through major Internet platforms. The outbreak of COVID-19 in 2020 has hit the catering industry in China. According to the statistics of the existing literature, it is found that there are few studies on online popular restaurants, and the research methods are relatively simple and traditional. The research on online comments of online popular restaurants can explore the emotional tendency of consumers, find the problems existing in online popular restaurants and put forward corresponding development suggestions. This paper uses Python technology to obtain the comments of 30 popular restaurants in Dalian on the public comment website, and puts forward corresponding opinions and suggestions on the operation of online popular restaurants through data mining. It is concluded that consumers care about the following aspects in the consumption process: taste, service, decoration style, waiting time in line. In this regard, we put forward the following suggestions: improve the taste of food, constantly push through the old and bring forth the new, and the primary task for the sustainable development of the restaurant is to ensure the taste;Improve service quality and create a high-quality service culture;Create a unique decoration style and resolutely resist and crack down on piracy;Reduce waiting time or provide better service during waiting time. © 2022 SPIE.

Media Reports on COVID-19 Vaccinations: A Study of Topic Modeling in South Korea.

Early successes in controlling the COVID-19 pandemic have prevented Republic of Korea from implementing a prompt, large-scale vaccine rollout to the public. The influence of traditional media on public opinion remains critical and substantial in Republic of Korea, and there have been heated debates about vaccination in traditional media reports in Korea. Effective and efficient public health communication is integral in managing public health challenges. This study explored media reports on the COVID-19 vaccines during the pandemic in Republic of Korea. 12,399 media news reports from May 2020 to September 2021 were collected. An LDA topic model was applied in order to analyze and compare the topics drawn from each study phase using words from the unstructured text data. Although media reports from before the national vaccination implementation focused on the development and rollout of COVID-19 vaccines, diverse topics were reported without any overlap. After the vaccination rollout, the biggest concern was the side effects of the COVID-19 vaccine. In sum, Republic of Korea's major media outlets reported on diverse topics rather than generating a common discourse about topics related to COVID-19 vaccination.

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.

Research on the promotion of new energy vehicles based on multi-source heterogeneous data: consumer and manufacturer perspectives.

Under the background of green development, new energy vehicles, as an important strategic emerging industry, play a crucial role in energy conservation and emission reduction. In the post-epidemic era, steadily promoting the promotion of new energy vehicles will be a hot topic. Based on multi-source heterogeneous data, combined with the latent Dirichlet allocation topic model, social network analysis, and econometric methods, this paper explores whether individual purchase decisions and company-level cooperative research and development will promote the promotion of new energy vehicles. The results show that whether it is battery electric vehicles, hybrid electric vehicles or plug-in hybrid electric vehicles, users are more concerned about space dimension, power performance, and design style. Patent collaboration network analysis indicates that new energy vehicle enterprises are establishing close partnerships, which will urge the promotion of new energy vehicles. An interesting test result found that for short-term innovation, new energy vehicles enterprises should invest more patent research and development in battery electric vehicles and hybrid electric vehicles models to better accelerate the promotion of new energy vehicles.

Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study.

BACKGROUND: The COVID-19 pandemic and its corresponding preventive and control measures have increased the mental burden on the public. Understanding and tracking changes in public mental status can facilitate optimizing public mental health intervention and control strategies. OBJECTIVE: This study aimed to build a social media-based pipeline that tracks public mental changes and use it to understand public mental health status regarding the pandemic. METHODS: This study used COVID-19-related tweets posted from February 2020 to April 2022. The tweets were downloaded using unique identifiers through the Twitter application programming interface. We created a lexicon of 4 mental health problems (depression, anxiety, insomnia, and addiction) to identify mental health-related tweets and developed a dictionary for identifying health care workers. We analyzed temporal and geographic distributions of public mental health status during the pandemic and further compared distributions among health care workers versus the general public, supplemented by topic modeling on their underlying foci. Finally, we used interrupted time series analysis to examine the statewide impact of a lockdown policy on public mental health in 12 states. RESULTS: We extracted 4,213,005 tweets related to mental health and COVID-19 from 2,316,817 users. Of these tweets, 2,161,357 (51.3%) were related to "depression," whereas 1,923,635 (45.66%), 225,205 (5.35%), and 150,006 (3.56%) were related to "anxiety," "insomnia," and "addiction," respectively. Compared to the general public, health care workers had higher risks of all 4 types of problems (all P<.001), and they were more concerned about clinical topics than everyday issues (eg, "students' pressure," "panic buying," and "fuel problems") than the general public. Finally, the lockdown policy had significant associations with public mental health in 4 out of the 12 states we studied, among which Pennsylvania showed a positive association, whereas Michigan, North Carolina, and Ohio showed the opposite (all P<.05). CONCLUSIONS: The impact of COVID-19 and the corresponding control measures on the public's mental status is dynamic and shows variability among different cohorts regarding disease types, occupations, and regional groups. Health agencies and policy makers should primarily focus on depression (reported by 51.3% of the tweets) and insomnia (which has had an ever-increasing trend since the beginning of the pandemic), especially among health care workers. Our pipeline timely tracks and analyzes public mental health changes, especially when primary studies and large-scale surveys are difficult to conduct.

Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model.

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Topic Modeling with Transformers for Sentence-Level Using Coronavirus Corpus

A Topic Model is a class of generative probabilistic models which has gained widespread use in computer science in recent years, especially in the field of text mining and information retrieval. Since it was first proposed, it has received a large amount of attention and general interest among scientists in many research areas. It allows us to discover the mix of hidden or "latent" subjects that differs from one document to another in a given corpus. But since topic modeling usually requires the prior definition of some parameters - above all the number of topics k to be discovered -, model evaluation is decisive to identify an "optimal" set of parameters for the specific data. Latent Dirichlet allocation (LDA) and Bidirectional Encoder Representations from Transformers Topic (BerTopic) are the two most popular topic modeling techniques. LDA uses a probabilistic approach whereas BerTopic uses transformers (BERT embeddings) and class-based TF-IDF to create dense clusters © 2022,International Journal of Interactive Mobile Technologies.All Rights Reserved