ABSTRACT
As most governments in the world currently face the pandemic, various policies and initiatives have been put in place in order to help control the spread of the COVID-19 outbreak. While these initiatives and interventions are taking place, a pandemic still creates a reality of risk and uncertainty. In these kinds of situations, public trust is greatly important to properly mitigate health and societal impacts of the pandemic. Social media platforms could be utilized as sources of information to gain insight on public sentiment, especially with the rise of social media utilization during the quarantine [13]. Given this, the study attempts to analyze social media sentiments particularly found in Twitter in order to not only look into the polarity of public sentiment on the government, its processes, and its policies, but particularly, to detect trust between the governed and the ones governing. Furthermore, it seeks to examine and analyze the trust narratives present in the Philippines currently. In this study, a supervised machine learning model was created using Linear SVC, utilizing TF-IDF and n-grams for feature extraction and selection in order to detect the respective trust category of a given sentiment and predict the trust category of new data points. While the results are overall negative, examining the trust categories individually demonstrates different narratives that dictate, affect, and express citizen trust towards different aspects of the government. The behavioral trust group provided narratives on certain political figures involved in a string of anomalies for the negative category, while the positive category lauded the VP for her continued service amidst the pandemic. On the other hand, narratives in the institutional trust group revolved around national and local institutions, where talks about national institutions being more prominent in the negative category, while local institutions, such as local government units, are found in the positive category. Lastly, narratives on the operational trust group focused on certain pandemic policies (lockdowns, mass testing, contact tracing) for the negative side, while vaccines and vaccinations were the focus for the positive side. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
As of January 02, 2022, the Philippines is combating another surge in COVID-19 cases. With vaccinations still ongoing, the country remains vigilant and the government continues to promote compliance to minimum health standards as preventive measures to minimize the spread. Disinformation remains a challenge especially if compliance to minimum health standards and adoption of health interventions are necessary to curb the spread of COVID-19. Incorrect and unverified information about the virus increased as well which continues to run rampant in social media and with minimal models to detect disinformation in a Philippine context. The study aimed to understand the features of disinformation of COVID-19 in a Philippine context with the goal of creating a text classification model to detect disinformation of COVID-19 in social media to promote vaccine usage in the country. The usage of social network analysis was performed to understand the narratives present regarding COVID-19 disinformation. Words related to vaccines, government corruption, and government mismanagement were prevalent under the disinformation categories of “False” and “Mostly False” while words related to health information such as cases or vaccine counts were prevalent under the “Mostly True” and “True” category. Linear SVM text classification model performed the best through accuracy, precision, and recall in detecting disinformation by using TF-IDF as a feature compared to using both TF-IDF and n-grams. Disinformation narratives revolved around the idea of COVID-19 cases/vaccines, government mismanagement, and regulations. Results showed that disinformation caused distrust of the government’s management over the pandemic. Moreover, the spread of disinformation was contained to the user itself and spread to at least one other user. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Social media can be used to understand how the public is responding to the ongoing nationwide COVID-19 vaccination campaign, allowing policymakers to respond effectively through informed decisions. However, conducting social media analysis in the Philippine-context presents a challenge because natural informal conversations make use of a combination of English and local language. This study addresses this challenge by including part-of-speech tags, frequency of code switching and language dominance features to represent bilingualism in training machine learning models with COVID-19 vaccination-related Tweets for sentiment and emotion analysis. Results showed that the English-Tagalog Logistic Regression sentiment classification model performed better than Textblob, VADER and Polyglot with an accuracy of 70.36%. Similarly, the English-Tagalog SVM emotion classification model performed better than Text2emotion, NRC Affect Intensity Lexicon and EmoTFIDF with an average mean-squared error of 0.049. The added bilingual features only improved these performance metrics by a small margin. Nevertheless, SHAP analysis still revealed that sentiment and emotion classes exhibit varying levels of these bilingual features, which shows the potential in exploring similar linguistic features to distinguish between classes better during text classification for future studies. Finally, Tweets from September 2021 to January 2022 shows negative, mainly anger and sadness, perceptions towards COVID-19 vaccinations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Purpose: This paper aimed to validate the sustainable competitive advantage (SCA) measures in Malaysian electronics manufacturing organizations' context after the post coronavirus disease 2019 (COVID-19) outbreak. Post pandemic sustainability in competitive advantages have become a buzzword for Malaysian electronics industries in facilitating the value chain generation to consumers, besides enhancing profitability to the organization. SCAs are important when the manufacturer can provide something valuable to the market, and it differs from the competitors, especially during the crisis. Design/methodology/approach: This study adopted the quantitative research approach in validating the SCA variables. The questionnaires were self-administered and randomly distributed among the electronics manufacturing organizations across Malaysia, with a total of 207 responses. Concisely, this research theoretically conceptualized SCAs as a multidimensional construct containing two dimensions: lower cost advantage (LCA) and differentiation advantage (DFA). Findings: Besides, the findings ascertained the strategies to sustain the competitive advantages within an organization, which is underpinned by resource-based theory. In short, findings of this research would be an imperative implication for academicians and organization's policymakers to move forward towards advanced economy and Industrial Revolution 4.0 in the current global competitive environment. Originality/value: There is no prescription of attaining competitive advantages that suits all conditions in Malaysian electronics industries;this implies that the literature gaps existed, and further research shall be conducted on SCAs. © 2021, Emerald Publishing Limited.
ABSTRACT
The federal government of Malaysia recently implemented a nationwide Movement Control Order (MCO) to control the COVID-19 outbreak. However, the MCO has had a negative impact on people's mental well-being. Interventions that can improve people's mental health when their movement is restricted are therefore urgently needed. The present study investigated the impact of an ultra-brief online mindfulness-based intervention on mental health using a two-arm randomized controlled trial design. A total of 161 participants self-reported their distress, anxiety, psychological insecurity, and well-being at baseline and post-treatment, while 61 of them answered the same set of measures and the fear of COVID-19 scale in a follow-up study two weeks later. A multivariate analysis of covariance found the intervention reduced psychological insecurity levels measured during post-treatment. Moreover, gender, the experience of practicing mindfulness, and participants' experiences of undergoing quarantine were found to play a role in post-treatment measures. No significant difference was found between the baseline and follow-up treatment. However, hierarchical multiple regression found that psychological insecurity measured at baseline positively predicted the level of fear after controlling for demographic variables. Overall, the findings suggest that an online mindfulness intervention is a potentially useful tool for alleviating people's mental health difficulties.