Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases

• URL: http://arxiv.org/abs/2110.00031v1
• Date: Thu, 30 Sep 2021 18:03:10 GMT
• Title: Variance of Twitter Embeddings and Temporal Trends of COVID-19 cases
• Authors: Khushbu Pahwa, Ambika Sadhu, Mayank Sethi, Sargun Nagpal, Tavpritesh Sethi
• Abstract summary: This paper proposes a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs) Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0.80 and 0.62 respectively.
• Score: 0.9449650062296824
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The severity of the coronavirus pandemic necessitates the need of effective administrative decisions. Over 4 lakh people in India succumbed to COVID-19, with over 3 crore confirmed cases, and still counting. The threat of a plausible third wave continues to haunt millions. In this ever changing dynamic of the virus, predictive modeling methods can serve as an integral tool. The pandemic has further triggered an unprecedented usage of social media. This paper aims to propose a method for harnessing social media, specifically Twitter, to predict the upcoming scenarios related to COVID-19 cases. In this study, we seek to understand how the surges in COVID-19 related tweets can indicate rise in the cases. This prospective analysis can be utilised to aid administrators about timely resource allocation to lessen the severity of the damage. Using word embeddings to capture the semantic meaning of tweets, we identify Significant Dimensions (SDs).Our methodology predicts the rise in cases with a lead time of 15 days and 30 days with R2 scores of 0.80 and 0.62 respectively. Finally, we explain the thematic utility of the SDs.

Related papers

• Event Detection from Social Media for Epidemic Prediction [76.90779562626541]
  We develop a framework to extract and analyze epidemic-related events from social media posts. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics. We show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
  arXiv  Detail & Related papers  (2024-04-02T06:31:17Z)
• Human Behavior in the Time of COVID-19: Learning from Big Data [71.26355067309193]
  Since March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths. The pandemic has impacted and even changed human behavior in almost every aspect. Researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning.
  arXiv  Detail & Related papers  (2023-03-23T17:19:26Z)
• Twitter conversations predict the daily confirmed COVID-19 cases [0.2320417845168326]
  Pandemic-specific discourse has remained on-trend on microblogging platforms such as Twitter and Weibo. We propose a sentiment-involved topic-based methodology for designing multiple time series from publicly available COVID-19 related Twitter conversations. We show that the inclusion of social media variables for modeling introduces 48.83--51.38% improvements on RMSE over the baseline models.
  arXiv  Detail & Related papers  (2022-06-21T15:31:06Z)
• Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic [0.45593531937154413]
  We collect a moderate-sized representative corpus of tweets (200,000 approx.) pertaining to Covid-19 vaccination over a period of seven months (September 2020 - March 2021) Following a Transfer Learning approach, we utilize the pre-trained Transformer-based XLNet model to classify tweets as Misleading or Non-Misleading. We build on this to study and contrast the characteristics of tweets in the corpus that are misleading in nature against non-misleading ones. Several ML models are employed for prediction, with up to 90% accuracy, and the importance of each feature is explained using SHAP Explainable AI (X
  arXiv  Detail & Related papers  (2021-08-16T17:02:18Z)
• Global Tweet Mentions of COVID-19 [3.3043776328952226]
  We present an open-source dataset of 1.92 million keyword-selected Twitter posts, updated weekly from January 2020 to present. The dashboard presents 100% of the geotagged tweets that contain keywords or hashtags related COVID-19. With emerging COVID variants but ongoing vaccine hesitancy and resistance, this dataset could be used by researchers to study numerous aspects of COVID-19.
  arXiv  Detail & Related papers  (2021-08-13T20:21:29Z)
• COVID-19 Tweets Analysis through Transformer Language Models [0.0]
  In this study, we perform an in-depth, fine-grained sentiment analysis of tweets in COVID-19. A trained transformer model is able to correctly predict, with high accuracy, the tone of a tweet. We then leverage this model for predicting tones for 200,000 tweets on COVID-19.
  arXiv  Detail & Related papers  (2021-02-27T12:06:33Z)
• 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)
• Effectiveness and Compliance to Social Distancing During COVID-19 [72.94965109944707]
  We use a detailed set of mobility data to evaluate the impact that stay-at-home orders had on the spread of COVID-19 in the US. We show that there is a unidirectional Granger causality, from the median percentage of time spent daily at home to the daily number of COVID-19-related deaths with a lag of 2 weeks.
  arXiv  Detail & Related papers  (2020-06-23T03:36:19Z)
• Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment [90.12602012910465]
  We train on Italy's early COVID-19 outbreak through Twitter and transfer to several other countries. Our experiments show strong results with up to 0.85 Spearman correlation in cross-country predictions.
  arXiv  Detail & Related papers  (2020-06-05T02:04:25Z)
• Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions [1.6571886312953874]
  An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. This paper presents a large-scale study based on data mined from Twitter.
  arXiv  Detail & Related papers  (2020-05-18T15:53:13Z)
• A Deep Q-learning/genetic Algorithms Based Novel Methodology For Optimizing Covid-19 Pandemic Government Actions [63.669642197519934]
  We use the SEIR epidemiological model to represent the evolution of the virus COVID-19 over time in the population. The sequences of actions (confinement, self-isolation, two-meter distance or not taking restrictions) are evaluated according to a reward system. We prove that our methodology is a valid tool to discover actions governments can take to reduce the negative effects of a pandemic in both senses.
  arXiv  Detail & Related papers  (2020-05-15T17:17:45Z)

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.