Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
- URL: http://arxiv.org/abs/2407.03190v2
- Date: Fri, 26 Jul 2024 21:51:19 GMT
- Title: Cutting through the noise to motivate people: A comprehensive analysis of COVID-19 social media posts de/motivating vaccination
- Authors: Ashiqur Rahman, Ehsan Mohammadi, Hamed Alhoori,
- Abstract summary: The COVID-19 pandemic exposed significant weaknesses in the healthcare information system.
The overwhelming volume of misinformation on social media created challenges to motivate people to take proper precautions and get vaccinated.
This study addresses scientific communication and public motivation in social media.
- Score: 1.1606619391009658
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: The COVID-19 pandemic exposed significant weaknesses in the healthcare information system. The overwhelming volume of misinformation on social media and other socioeconomic factors created extraordinary challenges to motivate people to take proper precautions and get vaccinated. In this context, our work explored a novel direction by analyzing an extensive dataset collected over two years, identifying the topics de/motivating the public about COVID-19 vaccination. We analyzed these topics based on time, geographic location, and political orientation. We noticed that while the motivating topics remain the same over time and geographic location, the demotivating topics change rapidly. We also identified that intrinsic motivation, rather than external mandate, is more advantageous to inspire the public. This study addresses scientific communication and public motivation in social media. It can help public health officials, policymakers, and social media platforms develop more effective messaging strategies to cut through the noise of misinformation and educate the public about scientific findings.
Related papers
- Large language models for sentiment analysis of newspaper articles during COVID-19: The Guardian [0.16777183511743468]
This study provides a sentiment analysis of The Guardian newspaper during various stages of COVID-19.
During the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy.
Results indicate that during the early pandemic stages, public sentiment prioritised urgent crisis response, later shifting focus to addressing the impact on health and the economy.
arXiv Detail & Related papers (2024-05-20T07:10:52Z) - Exploring a Hybrid Deep Learning Framework to Automatically Discover
Topic and Sentiment in COVID-19 Tweets [2.3940819037450987]
COVID-19 has created a major public health problem worldwide and other problems such as economic crisis, unemployment, mental distress, etc.
The pandemic is deadly in the world and involves many people not only with infection but also with problems, stress, wonder, fear, resentment, and hatred.
Twitter is a highly influential social media platform and a significant source of health-related information, news, opinion and public sentiment.
arXiv Detail & Related papers (2023-12-02T16:58:17Z) - Visualizing Relation Between (De)Motivating Topics and Public Stance
toward COVID-19 Vaccine [0.0]
In this study, we proposed an interactive visualization tool to inspect and analyze the topics that resonated among Twitter-sphere during the COVID-19 pandemic.
This tool can easily be generalized for any scenario for visual analysis and to increase the transparency of social media data for researchers and the general population alike.
arXiv Detail & Related papers (2023-06-21T09:01:53Z) - Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy
among Healthcare Workers [64.1526243118151]
We find that doctors are overall more positive toward the COVID-19 vaccines.
Doctors are more concerned with the effectiveness of the vaccines over newer variants.
Nurses pay more attention to the potential side effects on children.
arXiv Detail & Related papers (2022-09-11T14:22:16Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - Adherence to Misinformation on Social Media Through Socio-Cognitive and
Group-Based Processes [79.79659145328856]
We argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation.
We make the case that polarization and misinformation adherence are closely tied.
arXiv Detail & Related papers (2022-06-30T12:34:24Z) - "COVID-19 was a FIFA conspiracy #curropt": An Investigation into the
Viral Spread of COVID-19 Misinformation [60.268682953952506]
We estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models.
We provide a strategy to combat social media posts that are likely to cause widespread harm.
arXiv Detail & Related papers (2022-06-12T19:41:01Z) - Know it to Defeat it: Exploring Health Rumor Characteristics and
Debunking Efforts on Chinese Social Media during COVID-19 Crisis [65.74516068984232]
We conduct a comprehensive analysis of four months of rumor-related online discussion during COVID-19 on Weibo, a Chinese microblogging site.
Results suggest that the dread (cause fear) type of health rumors provoked significantly more discussions and lasted longer than the wish (raise hope) type.
We show the efficacy of debunking in suppressing rumor discussions, which is time-sensitive and varies across rumor types and debunkers.
arXiv Detail & Related papers (2021-09-25T14:02:29Z) - Health, Psychosocial, and Social issues emanating from COVID-19 pandemic
based on Social Media Comments using Natural Language Processing [8.150081210763567]
The COVID-19 pandemic has caused a global health crisis that affects many aspects of human lives.
Social media data can reveal public perceptions toward how governments and health agencies are handling the pandemic.
This paper aims to investigate the impact of the COVID-19 pandemic on people globally using social media data.
arXiv Detail & Related papers (2020-07-23T17:19:50Z) - Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using
Social Media [14.662523926129117]
We analyze the topic and sentiment dynamics due to COVID-19 from the massive social media posts.
Some topics like stay safe home" are dominated with positive sentiment.
The others such as people death" are consistently showing negative sentiment.
arXiv Detail & Related papers (2020-07-05T12:05:30Z) - Analyzing COVID-19 on Online Social Media: Trends, Sentiments and
Emotions [44.92240076313168]
We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.
By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures.
Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
arXiv Detail & Related papers (2020-05-29T09:24:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.