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Dataset Open Access

Crowd4SDG-VisualCit COVID-19 behavioral indicators

Barbara Pernici

This dataset contains the VisualCit  data for social distance and face masks derived from social media image analysis.

From Twitter crawls with COVID-10 keywords, images are filtered ewith ML classifiers in order to retrieve images of people in public places which are photos. With crowdsourcing additional information is added about COVID-19 related behavioral aspects, with the goal of deriving indicators for decision makers to assess the ongoing situation. In this analysis we focus on the percentages of people wearing masks and maintaining social distances.

 

Reference paper:

V. Negri, D. Scuratti, S. Agresti, D. Rooein, G. Scalia, J.L. Fernandez Marquez, A. Ravi Shankar, M. Carman and B. Pernici, Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter, ICSE - Track Software Engineering in Society, May 2021 https://arxiv.org/abs/2010.03021

Abstract 

Social Media provides a trove of information that, if aggregated and analysed appropriately can provide important statistical indicators to policy makers. In some situations these indicators are not available through other mechanisms. For example, given the ongoing COVID-19 outbreak, it is essential for governments to have access to reliable data on policy-adherence with regards to mask wearing, social distancing, and other hard-to-measure quantities. In this paper we investigate whether it is possible to obtain such data by aggregating information from images posted to social media. The paper presents VisualCit, a pipeline for image-based social sensing combining recent advances in image recognition technology with geocoding and crowdsourcing techniques. Our aim is to discover in which countries, and to what extent, people are following COVID-19 related policy directives.
We compared the results with the indicators produced within the CovidDataHub behavior tracker initiative. Preliminary results shows that social media images can produce reliable indicators for policy makers.

The metadata for the files in this dataset are described in the file crowd4SDG-VisualCit-COVID-19-metadata.docx in the dataset below.
Files (42.8 MB)
Name Size
106-info_3.csv
md5:5ae3821c4bfe9f76adbd7e1b5b8b292f
3.7 kB Download
106-info_4_0.csv
md5:78e10f284f4516cb7e6a235edee41a22
4.8 kB Download
106_CrawledTweets_25k.csv
md5:6919c4dc566c7c9cb32890419f5549e2
2.9 MB Download
106_social_distancing_and_masks_result.csv
md5:6d42230b71c2d8e278975a8dc93029aa
251.3 kB Download
106_social_distancing_and_masks_results_geoloc_ok.csv
md5:76aa4673630143eb4872c4b8b48e1272
1.3 MB Download
106_social_distancing_and_masks_task.csv
md5:a7630c6534a33e20c7c501b7ba0abc4b
9.2 MB Download
106_social_distancing_and_masks_task_run.csv
md5:08c2fc93bc37afa795b9e450bdec6491
3.1 MB Download
152_distancing_and_masks_2_result.csv
md5:8e0c7fb374a35aa1054ff1ab8ab2e613
389.8 kB Download
152_distancing_and_masks_2_results_geoloc_ok.csv
md5:b20b826fcd6ab75c6a25b9fab8a09eb4
2.7 MB Download
152_distancing_and_masks_2_task.csv
md5:fbb4e5fc3e2a93ab9cfd69118142d5eb
2.1 MB Download
152_distancing_and_masks_2_task_run.csv
md5:6cc11e4e59e96310a6e1d49d17047bc0
2.6 MB Download
152_info_3.csv
md5:60bb1d84fe9de3f4bc6331a3b59b68c8
1.7 kB Download
152_info_4_0.csv
md5:5359836d8824c7baf83847a3ef777e54
2.6 kB Download
162_info_3.csv
md5:16daf2ed8ae26c8757b05cd7e3f755ba
2.5 kB Download
162_info_4_0.csv
md5:8d77b9d442b66c07eef86ecaca3cfb47
3.8 kB Download
162_social_distancing_week_34_result.csv
md5:2bd44a437b8ca89c923968c872eb1e58
427.6 kB Download
162_social_distancing_week_34_results_geoloc_ok.csv
md5:c2390dfb878c93e3a44ecf7be3282e0e
1.9 MB Download
162_social_distancing_week_34_task.csv
md5:df64b8e9e7145aa91da70cb6a103c902
11.6 MB Download
162_social_distancing_week_34_task_run.csv
md5:0942b8b8f5f790b5ba0b165b5c4102f1
4.0 MB Download
crowd4SDG-VisualCit-COVID-19-metadata.docx
md5:79043dab14f4d4bcc48c318ece20782c
16.2 kB Download
crowd4SDG-VisualCit-COVID-19_social_distancing_and_masks-crowd-question_structure.xlsx
md5:703cfa86673f4577b0a93606c07c5623
34.5 kB Download
Worn face mask 17Aug-23Aug_alpha3.csv
md5:764621bc35d1ee69d3608ecd7480c8b7
575 Bytes Download
Worn face mask 27Jul-02Aug_alpha3.csv
md5:d3aa1a0730f5ee81170fc22ccb89b487
218.0 kB Download
Worn face mask May11-17-alpha3.csv
md5:867aff52ba0c5f9c9849051f8918183a
697 Bytes Download
  • V. Negri, D. Scuratti, S. Agresti, D. Rooein, G. Scalia, J.L. Fernandez-Marquez, A. Ravi Shankar, M. Carman and B.Pernici, Image-based Social Sensing: Combining AI and the Crowd to Mine Policy-Adherence Indicators from Twitter, ICSE - Track Software Engineering in Society, May 2021 http://hdl.handle.net/11311/1161146

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