Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time

• URL: http://arxiv.org/abs/2103.08952v1
• Date: Tue, 16 Mar 2021 10:22:39 GMT
• Title: Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time
• Authors: Philipp Wicke and Marianna M. Bolognesi
• Abstract summary: We show how the topics associated with the development of the pandemic changed through time, using topic modeling. We show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The words we use to talk about the current epidemiological crisis on social media can inform us on how we are conceptualizing the pandemic and how we are reacting to its development. This paper provides an extensive explorative analysis of how the discourse about Covid-19 reported on Twitter changes through time, focusing on the first wave of this pandemic. Based on an extensive corpus of tweets (produced between 20th March and 1st July 2020) first we show how the topics associated with the development of the pandemic changed through time, using topic modeling. Second, we show how the sentiment polarity of the language used in the tweets changed from a relatively positive valence during the first lockdown, toward a more negative valence in correspondence with the reopening. Third we show how the average subjectivity of the tweets increased linearly and fourth, how the popular and frequently used figurative frame of WAR changed when real riots and fights entered the discourse.

Related papers

• "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)
• Extracting Major Topics of COVID-19 Related Tweets [2.867517731896504]
  We use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We additionally analyze temporal trends of the topics for the whole world and four countries.
  arXiv  Detail & Related papers  (2021-10-05T08:40:51Z)
• When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
  We present CovidEmo, 1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises.
  arXiv  Detail & Related papers  (2021-07-23T04:07:14Z)
• Sentiment Analysis of Covid-19 Tweets using Evolutionary Classification-Based LSTM Model [0.6445605125467573]
  This paper represents the sentiment analysis on collected large number of tweets on Coronavirus or Covid-19. We analyze the trend of public sentiment on the topics related to Covid-19 epidemic using an evolutionary classification followed by the n-gram analysis. We trained the long-short term network using two types of rated tweets to predict sentiment on Covid-19 data and obtained an overall accuracy of 84.46%.
  arXiv  Detail & Related papers  (2021-06-13T04:27:21Z)
• Leveraging Natural Language Processing to Mine Issues on Twitter During the COVID-19 Pandemic [0.3674863913115431]
  The recent global outbreak of the coronavirus disease (COVID-19) has spread to all corners of the globe. To understand the public concerns and responses to the pandemic, a system that can leverage machine learning techniques to filter out irrelevant tweets is needed. In this study, we constructed a system to identify the relevant tweets related to the COVID-19 pandemic throughout January 1st, 2020 to April 30th, 2020.
  arXiv  Detail & Related papers  (2020-10-31T22:26:26Z)
• Understanding the Hoarding Behaviors during the COVID-19 Pandemic using Large Scale Social Media Data [77.34726150561087]
  We analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020. We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population. The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean.
  arXiv  Detail & Related papers  (2020-10-15T16:02:25Z)
• 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)
• "Notic My Speech" -- Blending Speech Patterns With Multimedia [65.91370924641862]
  We propose a view-temporal attention mechanism to model both the view dependence and the visemic importance in speech recognition and understanding. Our proposed method outperformed the existing work by 4.99% in terms of the viseme error rate. We show that there is a strong correlation between our model's understanding of multi-view speech and the human perception.
  arXiv  Detail & Related papers  (2020-06-12T06:51:55Z)
• 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)
• The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic [66.80677233314002]
  Pandemic of the novel Coronavirus Disease 2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. This paper aims to discover the social implications of this unprecedented disruption in our interactive society by mining people's opinions on social media.
  arXiv  Detail & Related papers  (2020-04-21T13:02:38Z)
• Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter [0.0]
  War-related terminology is commonly used to frame the discourse around epidemics and diseases. We present an analysis of the discourse around #Covid-19 based on a corpus of 200k tweets posted on Twitter during March and April 2020.
  arXiv  Detail & Related papers  (2020-04-15T10:14:15Z)

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.