A comprehensive analysis of forecasting elections using social media text.
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| Title: | A comprehensive analysis of forecasting elections using social media text. |
|---|---|
| Authors: | Gaur, Abhishek1 (AUTHOR) phishek@gmail.com, Yadav, Dharmendra Kumar1 (AUTHOR) dky@mnnit.ac.in |
| Source: | Multimedia Tools & Applications. Sep2025, Vol. 84 Issue 29, p35693-35725. 33p. |
| Subjects: | Election forecasting, Sentiment analysis, Political campaigns, User-generated content, Data mining, Data analysis |
| Abstract: | Modern social media's rise to prominence has altered the ways in which candidates reach out to voters and conduct campaigns. Researchers often dwell upon the uses of social media platforms as a plethora of information for various tasks, such as election prediction, since they contain a large volume of people's ideas about politics and leaders. Modern political campaigns and party propaganda make extensive use of social media. It is common practise for political parties and candidates to utilise Twitter and other social media during election season for coverage and promotion. This study analyses and provides estimates for the reliability of several volumetric social media techniques to predict election outcomes from social media activity. Incredibly large datasets made available by social media sites may be mined for insights into societal problems and predictions about the future. However, this is difficult because of the skewed and noisy nature of the data. This literature review aims to enlighten readers about the researchers' input towards the process of forecasting election outcomes using social media content by outlining an assessment of sentiment analysis and its methodologies. The study also discusses research that aims to foretell upcoming elections in several nations by analysing user textual data on social media sites. In addition, this paper has pointed out some of the research gaps that exist in the area of election outcome forecasting and some of the challenging questions in the domain of sentiment analysis. In addition, this paper makes recommendations for the future of election prediction based on material gleaned from social media. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187863823 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A comprehensive analysis of forecasting elections using social media text. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gaur%2C+Abhishek%22">Gaur, Abhishek</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> phishek@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Yadav%2C+Dharmendra+Kumar%22">Yadav, Dharmendra Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> dky@mnnit.ac.in</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Sep2025, Vol. 84 Issue 29, p35693-35725. 33p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Election+forecasting%22">Election forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Political+campaigns%22">Political campaigns</searchLink><br /><searchLink fieldCode="DE" term="%22User-generated+content%22">User-generated content</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Modern social media's rise to prominence has altered the ways in which candidates reach out to voters and conduct campaigns. Researchers often dwell upon the uses of social media platforms as a plethora of information for various tasks, such as election prediction, since they contain a large volume of people's ideas about politics and leaders. Modern political campaigns and party propaganda make extensive use of social media. It is common practise for political parties and candidates to utilise Twitter and other social media during election season for coverage and promotion. This study analyses and provides estimates for the reliability of several volumetric social media techniques to predict election outcomes from social media activity. Incredibly large datasets made available by social media sites may be mined for insights into societal problems and predictions about the future. However, this is difficult because of the skewed and noisy nature of the data. This literature review aims to enlighten readers about the researchers' input towards the process of forecasting election outcomes using social media content by outlining an assessment of sentiment analysis and its methodologies. The study also discusses research that aims to foretell upcoming elections in several nations by analysing user textual data on social media sites. In addition, this paper has pointed out some of the research gaps that exist in the area of election outcome forecasting and some of the challenging questions in the domain of sentiment analysis. In addition, this paper makes recommendations for the future of election prediction based on material gleaned from social media. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-20528-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 33 StartPage: 35693 Subjects: – SubjectFull: Election forecasting Type: general – SubjectFull: Sentiment analysis Type: general – SubjectFull: Political campaigns Type: general – SubjectFull: User-generated content Type: general – SubjectFull: Data mining Type: general – SubjectFull: Data analysis Type: general Titles: – TitleFull: A comprehensive analysis of forecasting elections using social media text. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gaur, Abhishek – PersonEntity: Name: NameFull: Yadav, Dharmendra Kumar IsPartOfRelationships: – BibEntity: Dates: – D: 11 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 29 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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