Using Twitter data for population estimates
Using Twitter data for population estimates
Twitter is increasingly being used as a source of data for the Social Sciences. However, deriving the demographic characteristics of users and dealing with the non-random non-representative populations from which they are drawn represent challenges for social scientists. This paper has two objectives: first, it compares different methods for estimating demographic information from Twitter data based on the crowd-sourcing platform CrowdFlower and the image-recognition software Face++. Second, it proposes a method for calibrating the non-representative sample of Twitter users with auxiliary information from official statistics, hence allowing to generalize findings based on Twitter to the general population.
1025-1031
Yildiz, Dilek
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Munson, Jo
8f482a63-f0ea-427b-bfd3-e8899177d45b
Vitali, Agnese
56acb6b8-5161-4106-9e73-20712840d675
Tinati, Ramine
4102a244-c312-4d57-88c2-d219d9f8d69a
Holland, Jennifer
53f89965-1900-4972-9d74-8d9c659676bb
June 2017
Yildiz, Dilek
5773b8d6-699c-4491-bee5-c6bea047fc1d
Munson, Jo
8f482a63-f0ea-427b-bfd3-e8899177d45b
Vitali, Agnese
56acb6b8-5161-4106-9e73-20712840d675
Tinati, Ramine
4102a244-c312-4d57-88c2-d219d9f8d69a
Holland, Jennifer
53f89965-1900-4972-9d74-8d9c659676bb
Yildiz, Dilek, Munson, Jo, Vitali, Agnese, Tinati, Ramine and Holland, Jennifer
(2017)
Using Twitter data for population estimates.
In SIS 2017. Statistics and Data Science: new challenges, new generations.
Firenze University Press.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Twitter is increasingly being used as a source of data for the Social Sciences. However, deriving the demographic characteristics of users and dealing with the non-random non-representative populations from which they are drawn represent challenges for social scientists. This paper has two objectives: first, it compares different methods for estimating demographic information from Twitter data based on the crowd-sourcing platform CrowdFlower and the image-recognition software Face++. Second, it proposes a method for calibrating the non-representative sample of Twitter users with auxiliary information from official statistics, hence allowing to generalize findings based on Twitter to the general population.
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More information
Published date: June 2017
Venue - Dates:
Statistics and Data Science: new challenges, new generations, Florence, Florence, Italy, 2017-06-28 - 2017-06-30
Identifiers
Local EPrints ID: 413025
URI: http://eprints.soton.ac.uk/id/eprint/413025
PURE UUID: 20851149-a1b2-4aab-b310-028e95063f1c
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Date deposited: 14 Aug 2017 16:30
Last modified: 15 Mar 2024 15:38
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Contributors
Author:
Dilek Yildiz
Author:
Jo Munson
Author:
Agnese Vitali
Author:
Ramine Tinati
Author:
Jennifer Holland
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