Amidst the COVID-19 vaccination, Twitter is one of the most popular platforms for discussions about the COVID-19 vaccination. These types of discussions most times lead to a compromise of public confidence toward the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis at the provincial, country, or continent level without considering the local communities. The aim of this study is to use clustered geo-tagged Twitter posts to inform city-level variations in sentiments toward COVID-19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). VADER, an NLP pre-trained model was used to label the Twitter posts according to their sentiments with their associated intensity scores. The outputs were validated using NB (0.68), LR (0.75), SVMs (0.70), DT (0.62), and KNN (0.56) machine learning classification algorithms. The number of new COVID-19 cases significantly positively correlated with the number of Tweets in South Africa (Corr = 0.462, P < 0.001). Out of the 10 topics identified from the tweets using the LDA model, two were about the COVID-19 vaccines: uptake and supply, respectively. The intensity of the sentiment score for the two topics was associated with the total number of vaccines administered in South Africa (P < 0.001). Discussions regarding the two topics showed higher intensity scores for the neutral sentiment class (P = 0.015) than for other sentiment classes. Additionally, the intensity of the discussions on the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P < 0.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P = 0.003), (P = 0.002), and (P < 0.001) for positive, negative, and neutral sentiments classes, respectively. The outcome of this research showed that clustered geo-tagged Twitter posts can be used to better analyse the dynamics in sentiments toward community–based infectious diseases-related discussions, such as COVID-19, Malaria, or Monkeypox. This can provide additional city-level information to health policy in planning and decision-making regarding vaccine hesitancy for future outbreaks.
BACKGROUND Global economy has been hardly hit by the COVID-19 pandemic. Many countries are experiencing a severe and destructive recession. Unemployment rate is very important to policy makers as it provide a key indicator of overall labour market and wider economic conditions. Despite its relevance, there is usually a delay in the availability of the indicator as it is traditionally based on a survey of households over several months. The speed at which the economy in most countries decline at the onset of COVID-19 highlights the importance of timely information about the labour market during the onset of a recession. In the coming year, there will be uncertainty about the timing and extent of any improvement in labour market outcomes that will also highlight the value of timely information. OBJECTIVE The main goal of this study is to provide policy- and decision-makers with additional and real-time information about the labor market flow during a prolonged pandemic. The first objective of the study is to find the missing unemployment rates in cases where census measurements are incomplete. The second objective is to estimate the unemployment rate in real-time since it usually takes months for formal unemployment data to be published. In this paper, we use social media data, particularly, Twitter to trace and nowcast the unemployment rate of South Africa during the COVID-19 pandemic. METHODS Unemployment rate in South Africa is estimated quarterly. We first used Google mobility index to interpolate it and find the monthly values. Next, we created a dataset of unemployment related tweets in South Africa using certain keywords such as employed, unemployed, and retrench. Principal Component Regression (PCR) was applied to estimate the unemployment rate using the tweets and their sentiment scores. RESULTS Numerical results indicate that the number of tweets is highly correlated with the unemployment rate during and before the COVID-19 pandemic. In addition, the trend of the normalized sum of the sentiment scores of the tweets is negatively correlated with the unemployment rate of South Africa. Moreover, the estimated unemployment rate using PCR is highly correlated with the actual unemployment rate of South Africa and has a low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). CONCLUSIONS The results of this study show that social media information can be used to reasonably estimate one of the key labor market indicators, especially during disaster events such as a prolonged pandemic. This information can be used to rapidly understand and manage the impacts of the pandemic on the economy and people’s life.
The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.
Background The global Mpox (formerly, Monkeypox) outbreak is disproportionately affecting the gay and bisexual men having sex with men community. Objective The aim of this study is to use social media to study country-level variations in topics and sentiments toward Mpox and Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual (2SLGBTQIAP+)–related topics. Previous infectious outbreaks have shown that stigma intensifies an outbreak. This work helps health officials control fear and stop discrimination. Methods In total, 125,424 Twitter and Facebook posts related to Mpox and the 2SLGBTQIAP+ community were extracted from May 1 to December 25, 2022, using Twitter application programming interface academic accounts and Facebook-scraper tools. The tweets’ main topics were discovered using Latent Dirichlet Allocation in the sklearn library. The pysentimiento package was used to find the sentiments of English and Spanish posts, and the CamemBERT package was used to recognize the sentiments of French posts. The tweets’ and Facebook posts’ languages were understood using the Twitter application programming interface platform and pycld3 library, respectively. Using ArcGis Online, the hot spots of the geotagged tweets were identified. Mann-Whitney U, ANOVA, and Dunn tests were used to compare the sentiment polarity of different topics and countries. Results The number of Mpox posts and the number of posts with Mpox and 2SLGBTQIAP+ keywords were 85% correlated (P<.001). Interestingly, the number of posts with Mpox and 2SLGBTQIAP+ keywords had a higher correlation with the number of Mpox cases (correlation=0.36, P<.001) than the number of posts on Mpox (correlation=0.24, P<.001). Of the 10 topics, 8 were aimed at stigmatizing the 2SLGBTQIAP+ community, 3 of which had a significantly lower sentiment score than other topics (ANOVA P<.001). The Mann-Whitney U test shows that negative sentiments have a lower intensity than neutral and positive sentiments (P<.001) and neutral sentiments have a lower intensity than positive sentiments (P<.001). In addition, English sentiments have a higher negative and lower neutral and positive intensities than Spanish and French sentiments (P<.001), and Spanish sentiments have a higher negative and lower positive intensities than French sentiments (P<.001). The hot spots of the tweets with Mpox and 2SLGBTQIAP+ keywords were recognized as the United States, the United Kingdom, Canada, Spain, Portugal, India, Ireland, and Italy. Canada was identified as having more tweets with negative polarity and a lower sentiment score (P<.04). Conclusions The 2SLGBTQIAP+ community is being widely stigmatized for spreading the Mpox virus on social media. This turns the community into a highly vulnerable population, widens the disparities, increases discrimination, and accelerates the spread of the virus. By identifying the hot spots and key topics of the related tweets, this work helps decision makers and health officials inform more targeted policies.
BACKGROUND While vaccination against the coronavirus (COVID–19) lasts, Twitter has become one of the social media platforms used to generate discussions about the COVID–19 vaccination. These types of discussions most times lead to a compromise of public confidence towards the vaccine. The text-based data generated by these discussions are used by researchers to extract topics and perform sentiment analysis on provincial, country, or continent level without considering the local communities. OBJECTIVE The aim of this study is to use geoclustering of Twitter posts to inform city-level variations in sentiments toward COVID–19 vaccine-related topics in the three largest South African cities (Cape Town, Durban, and Johannesburg). METHODS We generated a dataset and processed (n=25,000) COVID–19 vaccine-related tweets in South Africa from January 2021 to August 2021 using the academic researcher Twitter Application Programming Interface (API) with keywords like vaccine, vaccination, AstraZeneca, Oxford-AstraZeneca, VaccineToSaveSouthAfrica, JohnsonJohnson, and pfizer. Tweets were mapped with their geolocation. The Latent Dirichlet Allocation was used to identify frequently discussed topics across the cities. Senti- ments (negative, neutral, and positive) scores were assigned using the Valence Aware Dictionary with Support Vector Machine classification algorithm. RESULTS The number of new COVID–19 cases significantly positively correlated with the number of Tweets in South Africa (Corr=0.462, P<.001). Out of the 10 topics identified from the tweets, 2 were about the COVID–19 vaccines: uptake and supply, respectively. The intensity of the sentiments score for the two topics was associated with the total number of vaccines administered in South Africa (P<.001). Discussions regarding the two topics showed higher intensity scores for neutral sentiment class (P=.015) than for other sentiment classes. Additionally, the intensity of the discussions for the two topics was associated with the total number of vaccines administered, new cases, deaths, and recoveries across the three cities (P<.001). The sentiment score for the most discussed topic, vaccine uptake, differed across the three cities, with (P=.003), (P=.002), and (P<.001) for positive, negative, and neutral sentiments classes, respectively. CONCLUSIONS The outcome of this research showed that geolocation clustering of Twitter posts can be used to better analyze the sentiments towards COVID–19 vaccines at the local level. This can provide additional city–level information to health policy and decision-making regarding COVID–19 vaccine hesitancy.
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