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A Bi-level Assessment of Twitter Data for Election Prediction: Delhi Assembly Elections 2020

Published: 16 August 2022 Publication History

Abstract

Elections are the backbone of any democratic country, where voters elect the candidates as their representatives. The emergence of social networking sites has provided a platform for political parties and their candidates to connect with voters in order to spread their political ideas. Our study aims to use Twitter in assessing the outcome of the Delhi Assembly elections held in 2020, using a bi-level approach, i.e., concerning political parties and their candidates. We analyze the correlation of election results with the activities of different candidates and parties on Twitter, and the response of voters on them, especially the mentions and sentiment of voters towards a party over time. The Twitter profiles of the candidates are compared both at the party level as well as the candidate level to evaluate their association with the outcome of the election. We observe that the number of followers and the replies to candidates’ tweets are good indicators for predicting actual election outcomes. However, we observe that the number of tweets mentioning a party and the temporal analysis of voters’ sentiment towards the party shown in tweets are not aligned with the election result. Moreover, the variations in the activeness of candidates and political parties on Twitter with time also not very helpful in identifying the winner. Thus, merely using temporal data from Twitter is not sufficient to make accurate predictions, especially for countries like India.

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  • (2024)Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global ElectionsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652003(1126-1129)Online publication date: 13-May-2024
  • (2023)Report on the 12th Temporal Web Analytics Workshop (TempWeb 2022) at WWW 2022ACM SIGIR Forum10.1145/3582900.358290956:2(1-6)Online publication date: 31-Jan-2023

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  1. A Bi-level Assessment of Twitter Data for Election Prediction: Delhi Assembly Elections 2020

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      cover image ACM Conferences
      WWW '22: Companion Proceedings of the Web Conference 2022
      April 2022
      1338 pages
      ISBN:9781450391306
      DOI:10.1145/3487553
      © 2022 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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      Published: 16 August 2022

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      Author Tags

      1. Indian Election
      2. Social Media
      3. Temporal Analysis.

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      April 25 - 29, 2022
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      • (2024)Digital Democracy at Crossroads: A Meta-Analysis of Web and AI Influence on Global ElectionsCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3652003(1126-1129)Online publication date: 13-May-2024
      • (2023)Report on the 12th Temporal Web Analytics Workshop (TempWeb 2022) at WWW 2022ACM SIGIR Forum10.1145/3582900.358290956:2(1-6)Online publication date: 31-Jan-2023

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