Twitter, human mobility, and COVID-19

• URL: http://arxiv.org/abs/2007.01100v1
• Date: Wed, 24 Jun 2020 23:21:03 GMT
• Title: Twitter, human mobility, and COVID-19
• Authors: Xiao Huang, Zhenlong Li, Yuqin Jiang, Xiaoming Li, Dwayne Porter
• Abstract summary: We analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected. To quantify the responsiveness in certain geographical regions, we propose a mobility-based responsive index (MRI) The results suggest that mobility patterns obtained from Twitter data are amendable to quantitatively reflect the mobility dynamics.
• Score: 11.143921916292726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The outbreak of COVID-19 highlights the need for a more harmonized, less privacy-concerning, easily accessible approach to monitoring the human mobility that has been proved to be associated with the viral transmission. In this study, we analyzed 587 million tweets worldwide to see how global collaborative efforts in reducing human mobility are reflected from the user-generated information at the global, country, and the U.S. state scale. Considering the multifaceted nature of mobility, we propose two types of distance: the single-day distance and the cross-day distance. To quantify the responsiveness in certain geographical regions, we further propose a mobility-based responsive index (MRI) that captures the overall degree of mobility changes within a time window. The results suggest that mobility patterns obtained from Twitter data are amendable to quantitatively reflect the mobility dynamics. Globally, the proposed two distances had greatly deviated from their baselines after March 11, 2020, when WHO declared COVID-19 as a pandemic. The considerably less periodicity after the declaration suggests that the protection measures have obviously affected people's travel routines. The country scale comparisons reveal the discrepancies in responsiveness, evidenced by the contrasting mobility patterns in different epidemic phases. We find that the triggers of mobility changes correspond well with the national announcements of mitigation measures. In the U.S., the influence of the COVID-19 pandemic on mobility is distinct. However, the impacts varied substantially among states. The strong mobility recovering momentum is further fueled by the Black Lives Matter protests, potentially fostering the second wave of infections in the U.S.

Related papers

• Countrywide natural experiment reveals impact of built environment on physical activity [55.93314719065985]
  More walkable built environments have the potential to increase activity across the population. Increases in walkability are associated with significant increases in physical activity after relocation. Moderate-to-vigorous physical activity (MVPA) is linked to an array of associated health benefits.
  arXiv  Detail & Related papers  (2024-06-07T00:11:17Z)
• Priority-Centric Human Motion Generation in Discrete Latent Space [59.401128190423535]
  We introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM) for text-to-motion generation. M2DM incorporates a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token.
  arXiv  Detail & Related papers  (2023-08-28T10:40:16Z)
• Human MotionFormer: Transferring Human Motions with Vision Transformers [73.48118882676276]
  Human motion transfer aims to transfer motions from a target dynamic person to a source static one for motion synthesis. We propose Human MotionFormer, a hierarchical ViT framework that leverages global and local perceptions to capture large and subtle motion matching. Experiments show that our Human MotionFormer sets the new state-of-the-art performance both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2023-02-22T11:42:44Z)
• "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)
• Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data [36.99572681309649]
  The constrained outbreak of COVID-19 in Mainland China has recently been regarded as a successful example of fighting this virus. We use the huge amount of human mobility data collected from Baidu Maps to look into the detail reaction of the people there during the pandemic.
  arXiv  Detail & Related papers  (2022-02-16T10:32:39Z)
• Influence of Mobility Restrictions on Transmission of COVID-19 in the state of Maryland -- the USA [0.0]
  The novel coronavirus, COVID-19, was first detected in the United States in January 2020. To curb the spread of the disease in mid-March, different states issued mandatory stay-at-home (SAH) orders. We studied the impact of restrictions on mobility on reducing COVID-19 transmission.
  arXiv  Detail & Related papers  (2021-09-24T22:15:40Z)
• Change of human mobility during COVID-19: A United States case study [0.0]
  COVID-19 and the resulting shelter in place guidelines combined with remote working practices has dramatically impacted human mobility in 2020. We study mobility change in the US through a five-step process using mobility footprint data. We find that our analysis provides a comprehensive understanding of mobility change in response to the COVID-19 pandemic.
  arXiv  Detail & Related papers  (2021-09-18T22:09:39Z)
• 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)
• COVID-19 Mobility Data Collection of Seoul, South Korea [0.46180371154032895]
  This paper presents two categories of mobility datasets that concern nearly 10 million citizens' movements during COVID-19 in the capital city of South Korea, Seoul. We curate hourly data of subway ridership, traffic volume and population present count at selected points of interests.
  arXiv  Detail & Related papers  (2020-06-11T07:04:31Z)
• 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)
• Mobility Changes in Response to COVID-19 [0.0]
  We use anonymized and/or de-identified mobile device locations to measure mobility. Results indicate that a large reduction in mobility has taken place, both in the US and globally.
  arXiv  Detail & Related papers  (2020-03-31T14:03:30Z)

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.