Predicting the winner of the US 2024 elections using trust analytics

• URL: http://arxiv.org/abs/2411.10457v1
• Date: Fri, 01 Nov 2024 15:16:47 GMT
• Title: Predicting the winner of the US 2024 elections using trust analytics
• Authors: Budzynska Katarzyna, Gajewska Ewelina,
• Abstract summary: We employ a computational social science approach, utilising public reactions in social media to real-life events that involve presidential candidates. We name the tool we developed: Trust Analytics (TrustAn) We observe a tight race between Harris and Trump with week to week changes in the level of trust and distrust towards the two candidates. Using the ratio between the level of trust and distrust towards them and changes of this metric in time, we predict Donald Trump as the winner of the US 2024 elections.
• Score: 0.0
• License:
• Abstract: A number of models and techniques has been proposed for predicting the outcomes of presidential elections. Some of them use information on the socio-economical status of a country, others focus on candidates' popularity measures in news media. We employ a computational social science approach, utilising public reactions in social media to real-life events that involve presidential candidates. Contrary to the popular approach, we do not analyse public emotions but ethotic references to the character of politicians which allows us to analyse how much they are (dis-)trusted by the general public, hence the name of the tool we developed: Trust Analytics (TrustAn). Similarly to major news media's polls, we observe a tight race between Harris and Trump with week to week changes in the level of trust and distrust towards the two candidates. Using the ratio between the level of trust and distrust towards them and changes of this metric in time, we predict Donald Trump as the winner of the US 2024 elections.

Related papers

• Analyzing and Estimating Support for U.S. Presidential Candidates in Twitter Polls [1.71952017922628]
  We examine nearly two thousand Twitter polls gauging support for U.S. presidential candidates during the 2016 and 2020 election campaigns. Our findings reveal that Twitter polls are biased in various ways, starting from the position of the presidential candidates. The 2016 and 2020 polls were predominantly crafted by older males and manifested a pronounced bias favoring candidate Donald Trump.
  arXiv  Detail & Related papers  (2024-06-05T14:57:29Z)
• Election Polls on Social Media: Prevalence, Biases, and Voter Fraud Beliefs [5.772751069162341]
  This study focuses on the 2020 presidential elections in the U.S. We find that Twitter polls are disproportionately authored by older males and exhibit a large bias towards candidate Donald Trump. We also find that Twitter accounts participating in election polls are more likely to be bots, and election poll outcomes tend to be more biased, before the election day than after.
  arXiv  Detail & Related papers  (2024-05-18T02:29:35Z)
• Prediction of the 2023 Turkish Presidential Election Results Using Social Media Data [0.5156484100374059]
  We aim to predict the vote shares of parties participating in the 2023 elections in Turkey by combining social media data with traditional polling data. Our approach is a volume-based approach that considers the number of social media interactions rather than content.
  arXiv  Detail & Related papers  (2023-05-28T13:17:51Z)
• The Chance of Winning Election Impacts on Social Media Strategy [7.528982057686348]
  We analyze candidates' tweets in terms of users, topics, and sentiment of replies. As their chances of winning increase, candidates narrow the targets they communicate with.
  arXiv  Detail & Related papers  (2023-01-18T03:10:02Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• Novelty in news search: a longitudinal study of the 2020 US elections [62.997667081978825]
  We analyze novelty, a measurement of new items that emerge in the top news search results. We find more new items emerging for election related queries compared to topical or stable queries. We argue that such imbalances affect the visibility of political candidates in news searches during electoral periods.
  arXiv  Detail & Related papers  (2022-11-09T08:42:37Z)
• Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections [92.33485580547801]
  We analyze the change of polarization between the 2016 and 2020 U.S. presidential elections. Most of the top influencers were affiliated with media organizations during both elections. 75% of the top influencers in 2020 were not present in 2016, demonstrating that such status is difficult to retain.
  arXiv  Detail & Related papers  (2021-11-03T20:08:54Z)
• Reaching the bubble may not be enough: news media role in online political polarization [58.720142291102135]
  A way of reducing polarization would be by distributing cross-partisan news among individuals with distinct political orientations. This study investigates whether this holds in the context of nationwide elections in Brazil and Canada.
  arXiv  Detail & Related papers  (2021-09-18T11:34:04Z)
• Predicting the 2020 US Presidential Election with Twitter [0.0]
  Electoral prediction utilizing social media data potentially would significantly affect campaign strategies. This paper explores past successful methods from research for analysis and prediction of the 2020 US Presidential Election using Twitter data. It is inconclusive whether this is an accurate method for predicting elections due to scarcity of data.
  arXiv  Detail & Related papers  (2021-07-19T14:59:25Z)
• Mundus vult decipi, ergo decipiatur: Visual Communication of Uncertainty in Election Polls [56.8172499765118]
  We discuss potential sources of bias in nowcasting and forecasting. Concepts are presented to attenuate the issue of falsely perceived accuracy. One key idea is the use of Probabilities of Events instead of party shares.
  arXiv  Detail & Related papers  (2021-04-28T07:02:24Z)
• Political audience diversity and news reliability in algorithmic ranking [54.23273310155137]
  We propose using the political diversity of a website's audience as a quality signal. Using news source reliability ratings from domain experts and web browsing data from a diverse sample of 6,890 U.S. citizens, we first show that websites with more extreme and less politically diverse audiences have lower journalistic standards.
  arXiv  Detail & Related papers  (2020-07-16T02:13:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.