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Twitter Population Sample Bias and its impact on predictive outcomes: a case study on elections

Authors:

Renato Miranda Filho,

Jussara M. Almeida,

Gisele L. PappaAuthors Info & Claims

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

Pages 1254 - 1261

https://doi.org/10.1145/2808797.2809328

Published: 25 August 2015 Publication History

Abstract

In the past years a lot of effort has been spent analyzing online social network data to understand how the world reality is reflected in the "virtual" world. Twitter is by far the network most used in these studies, given its policy of public data availability. However, a big discussion is still on on whether the data available is enough to make user characterization or event outcomes prediction, and what are the pitfalls people do not usually account for. In this direction, we propose a new methodology for drawing representative samples from Twitter data, which is divided into four phases: (i) user filtering, (ii) user demographic characterization, (iii) user sampling, and (iv) event prediction. The methodology is tested into a common scenario in Twitter event outcome prediction: elections. The methodology was tested with municipality elections from six different Brazilian cities, and compared to official election results. Results show it is worth further investigating the topic, but that a very hight number of messages is required to match real data distributions.

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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
Tonglet JJehoul AReusens MReusens MBaesens B(2024)Predicting the demographics of Twitter users with programmatic weak supervisionTOP10.1007/s11750-024-00666-y32:3(354-390)Online publication date: 8-Feb-2024
https://doi.org/10.1007/s11750-024-00666-y
Sathyan SPeedikayil JV RSalkuti S(2023)Two-Layered Machine Learning Approach for Sentiment Analysis of tweets related to Electric Vehicles2023 International Conference on Innovations in Engineering and Technology (ICIET)10.1109/ICIET57285.2023.10220717(1-6)Online publication date: 13-Jul-2023
https://doi.org/10.1109/ICIET57285.2023.10220717
Show More Cited By

Twitter Population Sample Bias and its impact on predictive outcomes: a case study on elections
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

ASONAM '15: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

August 2015

835 pages

ISBN:9781450338547

DOI:10.1145/2808797

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 August 2015

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ASONAM '15

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ASONAM '15: Advances in Social Networks Analysis and Mining 2015

August 25 - 28, 2015

Paris, France

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Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

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https://doi.org/10.1080/15230406.2024.2323489
Tonglet JJehoul AReusens MReusens MBaesens B(2024)Predicting the demographics of Twitter users with programmatic weak supervisionTOP10.1007/s11750-024-00666-y32:3(354-390)Online publication date: 8-Feb-2024
https://doi.org/10.1007/s11750-024-00666-y
Sathyan SPeedikayil JV RSalkuti S(2023)Two-Layered Machine Learning Approach for Sentiment Analysis of tweets related to Electric Vehicles2023 International Conference on Innovations in Engineering and Technology (ICIET)10.1109/ICIET57285.2023.10220717(1-6)Online publication date: 13-Jul-2023
https://doi.org/10.1109/ICIET57285.2023.10220717
Aduragba OYu JCristea A(2023)Incorporating Emotions into Health Mention Classification Task on Social Media2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386330(4834-4842)Online publication date: 15-Dec-2023
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Pueyo-Ros JGarau E(2023)Do I have time to build the ark calmly? Characterizing attitudes towards climate change via sentiment analysis of social mediaJournal of Integrative Environmental Sciences10.1080/1943815X.2023.226438020:1Online publication date: 2-Oct-2023
https://doi.org/10.1080/1943815X.2023.2264380
Hutchings SDixit YAl-Sarayreh MTorrico DRealini CJaeger SReis M(2023)A critical review of social media research in sensory-consumer scienceFood Research International10.1016/j.foodres.2023.112494165(112494)Online publication date: Mar-2023
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Giorgi SEichstaedt JPreotiuc-Pietro DGardner JAndrew Schwartz HUngar L(2023)Filling in the White Space: Spatial Interpolation with Gaussian Processes and Social Media DataCurrent Research in Ecological and Social Psychology10.1016/j.cresp.2023.100159(100159)Online publication date: Oct-2023
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Golder SStevens RO'Connor KJames RGonzalez-Hernandez G(2022)Methods to Establish Race or Ethnicity of Twitter Users: Scoping ReviewJournal of Medical Internet Research10.2196/3578824:4(e35788)Online publication date: 29-Apr-2022
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Brum PCândido Teixeira MVimieiro RAraújo EMeira Jr WLobo Pappa G(2022)Political polarization on Twitter during the COVID-19 pandemic: a case study in BrazilSocial Network Analysis and Mining10.1007/s13278-022-00949-x12:1Online publication date: 23-Sep-2022
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del Gobbo EFontanella LFontanella SSarra A(2021)Geographies of Twitter debatesJournal of Computational Social Science10.1007/s42001-021-00143-75:1(647-663)Online publication date: 12-Sep-2021
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