short-paper

Understanding Demographic Bias and Representation in Social Media Health Data

Authors:

Christan Grant,

Elaine O. NsoesieAuthors Info & Claims

WebSci '19: Companion Publication of the 10th ACM Conference on Web Science

Pages 7 - 9

https://doi.org/10.1145/3328413.3328415

Published: 26 June 2019 Publication History

Abstract

Text, images, geotags and other data from social media sites lend researchers a unique window into population health trends and disease spread. While these data provide the opportunity to track and measure health outcomes across geographic regions, over extended periods of time, and through complex social networks, they also present challenges. Most notably, these data carry significant biases due to demographic differences in who chooses to use each platform, and what they choose to share. While several publications have discussed the limitations of leveraging social media data for public health research, the amount of literature systematically investigating their demographic bias and exploring mitigation strategies is limited and ripe for interdisciplinary contributions. In this discussion paper, we highlight that understanding the strengths and limitations of these data sources would enable a rigorous assessment of their usefulness for public health research and provide a means for quantifying uncertainty in research findings.

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Cited By

Lin BZou LZhao BHuang XCai HYang MZhou B(2024)Sensing the pulse of the pandemic: unveiling the geographical and demographic disparities of public sentiment toward COVID-19 through social mediaCartography and Geographic Information Science10.1080/15230406.2024.232348951:3(366-384)Online publication date: 21-Mar-2024
https://doi.org/10.1080/15230406.2024.2323489
Gambini MTardelli STesconi M(2024)The anatomy of conspiracy theorists: Unveiling traits using a comprehensive Twitter datasetComputer Communications10.1016/j.comcom.2024.01.027217(25-40)Online publication date: Mar-2024
https://doi.org/10.1016/j.comcom.2024.01.027
Ankita Garg R(2024)Some Observations on Social Media Mining tools for Health ApplicationsData Science and Applications10.1007/978-981-99-7817-5_8(97-109)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_8
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Index Terms

Understanding Demographic Bias and Representation in Social Media Health Data
1. Social and professional topics
  1. User characteristics

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cover image ACM Conferences

WebSci '19: Companion Publication of the 10th ACM Conference on Web Science

June 2019

34 pages

ISBN:9781450361743

DOI:10.1145/3328413

General Chairs:
Paolo Boldi
Università degli Studi, Milano, Italy
,
Brooke Foucault Welles
Northeastern University, Boston, USA
,
Katharina Kinder-Kurlanda
GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany
,
Christo Wilson
Northeastern University, Boston, USA
,
Program Chairs:
Isabella Peters
ZBW Leibniz Information Center for Economics & Kiel University, Kiel, Germany
,
Wagner Meira
Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Copyright © 2019 ACM.

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Published: 26 June 2019

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WebSci '19: 11th ACM Conference on Web Science

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Cited By

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https://doi.org/10.1080/15230406.2024.2323489
Gambini MTardelli STesconi M(2024)The anatomy of conspiracy theorists: Unveiling traits using a comprehensive Twitter datasetComputer Communications10.1016/j.comcom.2024.01.027217(25-40)Online publication date: Mar-2024
https://doi.org/10.1016/j.comcom.2024.01.027
Ankita Garg R(2024)Some Observations on Social Media Mining tools for Health ApplicationsData Science and Applications10.1007/978-981-99-7817-5_8(97-109)Online publication date: 18-Jan-2024
https://doi.org/10.1007/978-981-99-7817-5_8
Morita PZakir Hussain IKaur JLotto MButt Z(2023)Tweeting for Health Using Real-time Mining and Artificial Intelligence–Based Analytics: Design and Development of a Big Data Ecosystem for Detecting and Analyzing Misinformation on TwitterJournal of Medical Internet Research10.2196/4435625(e44356)Online publication date: 9-Jun-2023
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Engel TDotan ESynett YHeld RSoffer SBen‐Horin SKopylov U(2023)Self‐reported treatment effectiveness for Crohn's disease using a novel crowdsourcing web‐based platformUnited European Gastroenterology Journal10.1002/ueg2.12424Online publication date: 27-Jun-2023
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Dirkson Aden Hollander DVerberne SDesar IHusson Ovan der Graaf WOosten AReyners ASteeghs Nvan Loon Wvan Oortmerssen GGelderblom HKraaij W(2022)Sample Bias in Web-Based Patient-Generated Health Data of Dutch Patients With Gastrointestinal Stromal Tumor: Survey StudyJMIR Formative Research10.2196/367556:12(e36755)Online publication date: 15-Dec-2022
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Flores LYoung S(2022)Ethical Considerations in the Application of Artificial Intelligence to Monitor Social Media for COVID-19 DataMinds and Machines10.1007/s11023-022-09610-032:4(759-768)Online publication date: 25-Aug-2022
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