Correcting public opinion trends through Bayesian data assimilation

• URL: http://arxiv.org/abs/2105.14276v1
• Date: Sat, 29 May 2021 11:39:56 GMT
• Title: Correcting public opinion trends through Bayesian data assimilation
• Authors: Robin Hendrickx, Rossella Arcucci, Julio Amador D{\i}az Lopez, Yi-Ke Guo, and Mark Kennedy
• Abstract summary: Measuring public opinion is a key focus during democratic elections. Traditional survey polling remains the most popular estimation technique. Twitter opinion mining has attempted to combat these issues.
• Score: 8.406968279478347
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Measuring public opinion is a key focus during democratic elections, enabling candidates to gauge their popularity and alter their campaign strategies accordingly. Traditional survey polling remains the most popular estimation technique, despite its cost and time intensity, measurement errors, lack of real-time capabilities and lagged representation of public opinion. In recent years, Twitter opinion mining has attempted to combat these issues. Despite achieving promising results, it experiences its own set of shortcomings such as an unrepresentative sample population and a lack of long term stability. This paper aims to merge data from both these techniques using Bayesian data assimilation to arrive at a more accurate estimate of true public opinion for the Brexit referendum. This paper demonstrates the effectiveness of the proposed approach using Twitter opinion data and survey data from trusted pollsters. Firstly, the possible existence of a time gap of 16 days between the two data sets is identified. This gap is subsequently incorporated into a proposed assimilation architecture. This method was found to adequately incorporate information from both sources and measure a strong upward trend in Leave support leading up to the Brexit referendum. The proposed technique provides useful estimates of true opinion, which is essential to future opinion measurement and forecasting research.

Related papers

• On the Use of Proxies in Political Ad Targeting [49.61009579554272]
  We show that major political advertisers circumvented mitigations by targeting proxy attributes. Our findings have crucial implications for the ongoing discussion on the regulation of political advertising.
  arXiv  Detail & Related papers  (2024-10-18T17:15:13Z)
• 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)
• 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)
• Leveraging Administrative Data for Bias Audits: Assessing Disparate Coverage with Mobility Data for COVID-19 Policy [61.60099467888073]
  We show how linking administrative data can enable auditing mobility data for bias. We show that older and non-white voters are less likely to be captured by mobility data. We show that allocating public health resources based on such mobility data could disproportionately harm high-risk elderly and minority groups.
  arXiv  Detail & Related papers  (2020-11-14T02:04:14Z)
• Using Online Implicit Association Tests in Opinion Polling [2.102846336724103]
  We analyse the phenomenon of socially desirable responding (shy voters) which has emerged as one of the reasons for modern day inaccurate polling. We argue for inclusion of IATs in traditional polling and point to the fact that these can be conducted accurately online.
  arXiv  Detail & Related papers  (2020-07-08T15:16:15Z)
• Predicting Livelihood Indicators from Community-Generated Street-Level Imagery [70.5081240396352]
  We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
  arXiv  Detail & Related papers  (2020-06-15T18:12:12Z)
• Electoral Forecasting Using a Novel Temporal Attenuation Model: Predicting the US Presidential Elections [91.3755431537592]
  We develop a novel macro-scale temporal attenuation (TA) model, which uses pre-election poll data to improve forecasting accuracy. Our hypothesis is that the timing of publicizing opinion polls plays a significant role in how opinion oscillates, especially right before elections. We present two different implementations of the TA model, which accumulate an average forecasting error of 2.8-3.28 points over the 48-year period.
  arXiv  Detail & Related papers  (2020-04-30T09:21:52Z)
• Measuring relative opinion from location-based social media: A case study of the 2016 U.S. presidential election [0.0]
  Social media has become an emerging alternative to opinion polls for public opinion collection. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls.
  arXiv  Detail & Related papers  (2020-02-03T16:01:48Z)

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.