Design and analysis of tweet-based election models for the 2021 Mexican legislative election
Abstract Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social be...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-07-01
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| Series: | EPJ Data Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1140/epjds/s13688-023-00401-w |
| _version_ | 1797784712387756032 |
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| author | Alejandro Vigna-Gómez Javier Murillo Manelik Ramirez Alberto Borbolla Ian Márquez Prasun K. Ray |
| author_facet | Alejandro Vigna-Gómez Javier Murillo Manelik Ramirez Alberto Borbolla Ian Márquez Prasun K. Ray |
| author_sort | Alejandro Vigna-Gómez |
| collection | DOAJ |
| description | Abstract Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour. |
| first_indexed | 2024-03-13T00:44:47Z |
| format | Article |
| id | doaj.art-a1be7ab55c18456aab4eaa6cd1a5e890 |
| institution | Directory Open Access Journal |
| issn | 2193-1127 |
| language | English |
| last_indexed | 2024-03-13T00:44:47Z |
| publishDate | 2023-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj.art-a1be7ab55c18456aab4eaa6cd1a5e8902023-07-09T11:08:49ZengSpringerOpenEPJ Data Science2193-11272023-07-0112111710.1140/epjds/s13688-023-00401-wDesign and analysis of tweet-based election models for the 2021 Mexican legislative electionAlejandro Vigna-Gómez0Javier Murillo1Manelik Ramirez2Alberto Borbolla3Ian Márquez4Prasun K. Ray5Niels Bohr International Academy, Niels Bohr InstituteThe Aspen Institute MéxicoFacultad de Ciencias, Universidad Nacional Autónoma de MéxicoCiencia de Datos & TecnologíaFacultad de Negocios, Universidad La Salle MéxicoDepartment of Mathematics, Imperial College LondonAbstract Modelling and forecasting real-life human behaviour using online social media is an active endeavour of interest in politics, government, academia, and industry. Since its creation in 2006, Twitter has been proposed as a potential laboratory that could be used to gauge and predict social behaviour. During the last decade, the user base of Twitter has been growing and becoming more representative of the general population. Here we analyse this user base in the context of the 2021 Mexican Legislative Election. To do so, we use a dataset of 15 million election-related tweets in the six months preceding election day. We explore different election models that assign political preference to either the ruling parties or the opposition. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods. These results demonstrate that analysis of public online data can outperform conventional polling methods, and that political analysis and general forecasting would likely benefit from incorporating such data in the immediate future. Moreover, the same Twitter dataset with geographical attributes is positively correlated with results from official census data on population and internet usage in Mexico. These findings suggest that we have reached a period in time when online activity, appropriately curated, can provide an accurate representation of offline behaviour.https://doi.org/10.1140/epjds/s13688-023-00401-wSocial mediaElectionsPollingTwitter |
| spellingShingle | Alejandro Vigna-Gómez Javier Murillo Manelik Ramirez Alberto Borbolla Ian Márquez Prasun K. Ray Design and analysis of tweet-based election models for the 2021 Mexican legislative election EPJ Data Science Social media Elections Polling |
| title | Design and analysis of tweet-based election models for the 2021 Mexican legislative election |
| title_full | Design and analysis of tweet-based election models for the 2021 Mexican legislative election |
| title_fullStr | Design and analysis of tweet-based election models for the 2021 Mexican legislative election |
| title_full_unstemmed | Design and analysis of tweet-based election models for the 2021 Mexican legislative election |
| title_short | Design and analysis of tweet-based election models for the 2021 Mexican legislative election |
| title_sort | design and analysis of tweet based election models for the 2021 mexican legislative election |
| topic | Social media Elections Polling |
| url | https://doi.org/10.1140/epjds/s13688-023-00401-w |
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