Public Health Policy Monitoring through Public Perceptions: A Case of COVID-19 Tweet Analysis

Abstract

Since the start of the COVID-19 pandemic, government authorities have responded by issuing new public health policies, many of which were intended to contain its spread but ended up limiting economic and social activities. The citizen responses to these policies are diverse, ranging from goodwill to fear and anger. It is challenging to determine whether or not these public health policies achieved the intended impact. This requires systematic data collection and scientific studies, which can be very time-consuming. To overcome such challenges, in this paper, we provide an alternative approach to continuously monitor and dynamically make sense of how public health policies impact citizens. Our approach is to continuously collect Twitter posts related to COVID-19 policies and to analyze the public reactions. We have developed a web-based system that collects tweets daily and generates timelines and geographical displays of citizens’ “concern levels”. Tracking the public reactions towards different policies can help government officials assess the policy impacts in a more dynamic and real-time manner. For this paper, we collected and analyzed over 16 million tweets related to ten policies over a 10-month period. We obtained several findings; for example, the “COVID-19 (General)” and ”Ventilators” policies engendered the highest concern levels, while the “Face Coverings” policy caused the lowest. Nine out of ten policies exhibited significant changes in concern levels during the observation period.

A New Social Media Analytics Method for Identifying Factors Contributing to COVID-19 Discussion Topics

“…A global perspective in COVID-19 tweet analysis is pivotal for recognizing international trends and disparities [9][10][11][12][13][14][15][16][17][18][19][20][21]. It enables us to identify common global concerns and regional variations, aiding policymakers in tailoring responses to specific contexts and populations.…”

“…Sentiment analysis delves into the emotional undercurrents of COVID-19 tweets, shedding light on public sentiment towards the pandemic, government responses, and vaccination efforts. This knowledge is invaluable for gauging public support and addressing concerns, ultimately contributing to more effective public health communication [9][10][11][12][13][14][15][16][17][18][19][20][21][35][36][37][38][39][40][41][42][43][44].…”

“…The survey also found that age, race, political party, and news source are some of the factors that influence the level of belief in COVID-19 misinformation. To eliminate these misconceptions and also to make strategic policy decisions on controlling COVID-19 crises, researchers and policymakers have been using Twitter analytics with artificial intelligence (AI) and natural language processing (NLP) (as shown in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]). However, none of these tweet-mining technics in the area of COVID-19 (i.e., [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]) or other topics (e.g., [26][27][28][29][30][31][32][33]), have demonstrated a methodology to identify the factors correlated to Twitter discourse topics.…”

Influence of variants mutations on the progression and outcome of COVID-19 in Pakistan

“…By closing schools and businesses, physically separating people, and using face masks, non-pharmaceutical measures aim to reduce contact. Furthermore, isolation and quarantine prevent further transmission, testing and screening programs were the additional measures (7). However, difficulties persist because of the public's resistance and the emergence of novel variations.…”

Real-time human activity recognition from smart phone using linear support vector machines

“…Most inquiries regarding real-time recognition of human activity need to be a swered inside sure transition intervals. However, there may be some inquiries that do not impose any strict timing necessities; recall, for example, analytical inquiries associated with city making plans [5]. For this reason, this work ambition to deal with and answer the subsequent questions: what devices can procedure very huge amounts of saved, decided on, and processed huge facts?…”