Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia

[Abstract] The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations.Taiwan. Ministry of Science and Technology; 108-2511-H-224-007-MY

Detecting the Presence of COVID-19 Vaccination Hesitancy from South African Twitter Data Using Machine Learning

Very few social media studies have been done on South African user-generated content during the COVID-19 pandemic and even fewer using hand-labelling over automated methods. Vaccination is a major tool in the fight against the pandemic, but vaccine hesitancy jeopardizes any public health effort. In this study, sentiment analysis on South African tweets related to vaccine hesitancy was performed, with the aim of training AI-mediated classification models and assessing their reliability in categorizing UGC. A dataset of 30000 tweets from South Africa were extracted and hand-labelled into one of three sentiment classes: positive, negative, neutral. The machine learning models used were LSTM, bi-LSTM, SVM, BERT-base-cased and the RoBERTa-base models, whereby their hyperparameters were carefully chosen and tuned using the WandB platform. We used two different approaches when we pre-processed our data for comparison: one was semantics-based, while the other was corpus-based. The pre-processing of the tweets in our dataset was performed using both methods, respectively. All models were found to have low F1-scores within a range of 45$\%$-55$\%$, except for BERT and RoBERTa which both achieved significantly better measures with overall F1-scores of 60$\%$ and 61$\%$, respectively. Topic modelling using an LDA was performed on the miss-classified tweets of the RoBERTa model to gain insight on how to further improve model accuracy

Parenting, Vaccines, and COVID-19: A Machine-Learning Approach

COVID-19 is currently at the forefront of both out-of-school time program providers’ and parents’ minds, with additional policies and procedures added existing operating standards to protect the health of participants, staff, and parents (Environmental Health & Engineering, 2020). A failure to adequately prepare and react to different parenting styles may have both operational and financial implications for out-of-school time programs. These implications are only further exacerbated in the additional context of a global pandemic. While the COVID-19 vaccine is a hope to many that the end of the pandemic is near, parental vaccine hesitancy or refusal may pose a significant hurdle to the safe operation of out-of-school time programs. By exploring the topics of vaccine hesitancy, children, and parents in an online environment, this study offers a closer look into a digital leisure space. In order to better explore the conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19, web-scraping technologies were employed to aid in a more robust data collection. Due to the nature of web-scraped data as large in size and unruly, a machine learning method was used to analyze the data: Latent Dirichlet Allocation (i.e., LDA), a specific form of topic modelling. After establishing model parameters for the LDA, 25 latent topics were identified from the cleaned dataset (N = 31,925). These 25 topics were subsequently sorted into seven categories: Government, Feelings, School, Public Health, Christmas, Risk & Safety, and Parents & Families. Interpretation of the 25 latent topics was aided by a visualization of the top words most relevant to individual topics, in context to the overall dataset. Representative tweets from each category further identified the range of conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19. Challenges with research at the cusp of innovation for leisure sciences, as well as implications of practice for out-of-school-time professionals, are also discussed

Leveraging social media data using latent dirichlet allocation and naïve bayes for mental health sentiment analytics on Covid-19 pandemic

In Malaysia, during the early stages of the COVID-19 pandemic, the negative impact on mental health became noticeable. The public's psychological and behavioral responses have risen as the COVID-19 outbreak progresses. A high impression of severity, vulnerability, impact, and fear was the element that influenced higher anxiety. Social media data can be used to track Malaysian sentiments in the COVID-19 era. However, it is often found on the internet in text format with no labels, and manually decoding this data is usually complicated. Furthermore, traditional data-gathering approaches, such as filling out a survey form, may not completely capture the sentiments. This study uses a text mining technique called Latent Dirichlet Allocation (LDA) on social media to discover mental health topics during the COVID-19 pandemic. Then, a model is developed using a hybrid approach, combining both lexicon-based and Naïve Bayes classifier. The accuracy, precision, recall, and F-measures are used to evaluate the sentiment classification. The result shows that the best lexicon-based technique is VADER with 72% accuracy compared to TextBlob with 70% accuracy. These sentiments results allow for a better understanding and handling of the pandemic. The top three topics are identified and further classified into positive and negative comments. In conclusion, the developed model can assist healthcare workers and policymakers in making the right decisions in the upcoming pandemic outbreaks

MODELING OF COVID-19 TOPICS ON PUBLIC HEALTH MESSAGE COMMUNICATION PATTERNS ON RADAR BANYUMAS SOCIAL MEDIA

The global outbreak of Covid-19 has emerged as one of the most devastating and challenging threats to all peoples of the world. The purpose of this study is to identify the theme and topic of Covid-19 on the pattern of public health message communication on social media Radar Banyumas. The spread of Covid-19 disease was found to correlate with social media activity as a tool to promote Covid-19 News. Topic modeling revealed from time to time in the Radar Banyumas mass media can help understand the impact of the outbreak on the emotions, beliefs, and thoughts of the affected communities. This can open up great opportunities for proper education and dissemination of information on public health recommendations. This study shows that data from Banyumas Radar mass media is useful for infodemiology studies. This topic modeling consistently categorizes public health messages, risk factors, pandemic situations, the impact of Covid-19, measures to slow the spread of Covid-19, preventive measures, health authorities and government policies, negative psychological reactions, social stigma related to Covid-19, Covid-19 cases, Covid-19 in Banyumas, and Covid-19 cases in Indonesia

Gaining a better understanding of online polarization by approaching it as a dynamic process

Polarization is often a clich{\'e}, its conceptualization remains approximate and no consensus has been reached so far. Often simply seen as an inevitable result of the use of social networks, polarization nevertheless remains a complex social phenomenon that must be placed in a wider context. To contribute to a better understanding of polarization, we approach it as an evolving process, drawing on a dual expertise in political and data sciences. We compare the polarization process between one mature debate (COVID-19 vaccine) and one emerging debate (Ukraine conflict) at the time of data collection. Both debates are studied on Twitter users, a highly politicized population, and on the French population to provide key elements beyond the traditional US context. This unprecedented analysis confirms that polarization varies over time, through a succession of specific periods, whose existence and duration depend on the maturity of the debate. Importantly, we highlight that polarization is paced by context-related events. Bearing this in mind, we pave the way for a new generation of personalized depolarization strategies, adapted to the context and maturity of debates

AI approaches to understand human deceptions, perceptions, and perspectives in social media

Social media platforms have created virtual space for sharing user generated information, connecting, and interacting among users. However, there are research and societal challenges: 1) The users are generating and sharing the disinformation 2) It is difficult to understand citizens\u27 perceptions or opinions expressed on wide variety of topics; and 3) There are overloaded information and echo chamber problems without overall understanding of the different perspectives taken by different people or groups. This dissertation addresses these three research challenges with advanced AI and Machine Learning approaches. To address the fake news, as deceptions on the facts, this dissertation presents Machine Learning approaches for fake news detection models, and a hybrid method for topic identification, whether they are fake or real. To understand the user\u27s perceptions or attitude toward some topics, this study analyzes the sentiments expressed in social media text. The sentiment analysis of posts can be used as an indicator to measure how topics are perceived by the users and how their perceptions as a whole can affect decision makers in government and industry, especially during the COVID-19 pandemic. It is difficult to measure the public perception of government policies issued during the pandemic. The citizen responses to the government policies are diverse, ranging from security or goodwill to confusion, fear, or anger. This dissertation provides a near real-time approach to track and monitor public reactions toward government policies by continuously collecting and analyzing Twitter posts about the COVID-19 pandemic. To address the social media\u27s overwhelming number of posts, content echo-chamber, and information isolation issue, this dissertation provides a multiple view-based summarization framework where the same contents can be summarized according to different perspectives. This framework includes components of choosing the perspectives, and advanced text summarization approaches. The proposed approaches in this dissertation are demonstrated with a prototype system to continuously collect Twitter data about COVID-19 government health policies and provide analysis of citizen concerns toward the policies, and the data is analyzed for fake news detection and for generating multiple-view summaries

Balanced reporting and boomerang effect: an analysis of Croatian online news sites vaccination coverage and user comments during the COVID-19 pandemic

IN ENGLISH: The purpose of this paper was to explore online media coverage of COVID-19 vaccination and user reactions to the different types of coverage. The authors aimed to investigate possible boomerang effects that arise when COVID-19 media coverage is assertive and confident, and to determine the effects of balanced reporting. A two-stage random sample comprised a total of 300 articles published in three Croatian online news sites during a period from 1 February 2020, through 15 January 2022. The data were categorized using human coding content analysis, while reliability of coding was checked by using two coders and calculating reliability coefficients. The data were analyzed by means of negative binomial regression analysis. The results revealed that COVID-19 reporting was mainly consensual, i.e., it provided largely affirmative information about vaccines. However, user comments were highly polarized and mostly negative, with the majority of anti-vaccination tropes linked to the “corrupt elites”. Based on the user comments, the negative influence of balanced reporting on COVID-19 vaccines and the existence of boomerang effect in cases of the overtly persuasive affirmative reporting was also established. The boomerang effect did not depend on the context, i.e., on the type of reporting. This study extends previous research on balanced reporting and boomerang effects by analyzing online comments as a potentially good parallelism of the offline discursive strategies of the pro-vaccination and anti-vaccination communication. The results of the study can be used for the adjustment of strategic communication targeting the vaccine hesitant audience. Based on the study results, it is recommended that relativization and politicization of science should be prevented by not equating scientific consensus with absolute epistemological certainty and by addressing legitimate concerns of vaccine hesitant persons without putting explicit blame on them. --------------- IN CROATIAN: Svrha ovog rada bila je istražiti izvještavanje internetskih medija o cijepljenju protiv COVID-19 bolesti i reakcije korisnika na različite vrste izvještavanja. Autori su imali za cilj istražiti mogući bumerang učinak koji nastaju kada je medijsko izvještavanje o COVID-19 bolesti asertivno i samopouzdano te utvrditi učinke uravnoteženog izvještavanja. Slučajni uzorak obuhvatio je ukupno 300 članaka objavljenih na trima hrvatskim internetskim portalima u razdoblju od 1. veljače 2020. do 15. siječnja 2022. godine. Podaci su kategorizirani analizom sadržaja, dok je pouzdanost kodiranja provjerena korištenjem dva kodera i izračunavanjem koeficijenata pouzdanosti. Podaci su analizirani negativnom binomnom regresijom. Rezultati su otkrili da je izvještavanje o COVID-u 19 bilo uglavnom konsenzualno, tj. pružalo je uglavnom afirmativne informacije o cjepivima. Međutim, komentari korisnika bili su vrlo polarizirani i većinom negativni, a većina antivakserskih tropa povezana je s "korumpiranim elitama". Na temelju komentara korisnika također je utvrđen negativan utjecaj uravnoteženog izvještavanja o cjepivima protiv COVID-19 te postojanje efekta bumeranga u slučajevima otvorenog uvjerljivog afirmativnog izvještavanja. Efekt bumeranga nije ovisio o kontekstu, odnosno o vrsti izvještavanja. Rezultati studije mogu se koristiti za prilagodbu strateške komunikacije usmjerene na publiku koja oklijeva s cijepljenjem

The Potential of Social Media Intelligence to Improve Peoples Lives: Social Media Data for Good

In this report, developed with support from Facebook, we focus on an approach to extract public value from social media data that we believe holds the greatest potential: data collaboratives. Data collaboratives are an emerging form of public-private partnership in which actors from different sectors exchange information to create new public value. Such collaborative arrangements, for example between social media companies and humanitarian organizations or civil society actors, can be seen as possible templates for leveraging privately held data towards the attainment of public goals