A Comparative Study of Social Media Prediction Potential in the 2012 U.S. Republican Presidential Preelections

Pimenta, Fernanda; Obradović, Darko; Dengel, Andreas

doi:10.1109/cgc.2013.43

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Such "participation advantage" improved upon models that used only the partisan vote index and incumbency as predictors. Pimenta, Obradovic, and Dengel predicted candidates' vote shares in 2012 Republican primaries and opinion polls using the number of incoming links to blog posts, number of likes/reposts a Facebook post received, number of retweets a tweet post received, the number of comments and likes/dislikes a YouTube video received, and so on [53]. However, simplistic interaction-based approaches have largely been criticized because they fail robustness checks [8,54].…”

Section: Diversity In Methodsmentioning

confidence: 99%

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

2020

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In recent years, many studies have used social media data to make estimates of electoral outcomes and public opinion. This paper reports the findings from a meta-analysis examining the predictive power of social media data by focusing on various sources of data and different methods of prediction; i.e., (1) sentiment analysis, and (2) analysis of structural features. Our results, based on the data from 74 published studies, show significant variance in the accuracy of predictions, which were on average behind the established benchmarks in traditional survey research. In terms of the approaches used, the study shows that machine learning-based estimates are generally superior to those derived from pre-existing lexica, and that a combination of structural features and sentiment analyses provides the most accurate predictions. Furthermore, our study shows some differences in the predictive power of social media data across different levels of political democracy and different electoral systems. We also note that since the accuracy of election and public opinion forecasts varies depending on which statistical estimates are used, the scientific community should aim to adopt a more standardized approach to analyzing and reporting social media data-derived predictions in the future.

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Section: Diversity In Methodsmentioning

confidence: 99%

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

2020

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“…Looking across various data collection time windows, only a single group (right-leaning Republicans) averages well above 50%, but even then stands at 67.5% averaged accuracy. The 2012 Republican primaries were also considered by [156] who examined the difference between traditional raw volume analyses and sentiment analyses which take into account the popularity of a post. They correlate predictions from blogs, Facebook, Twitter, and Youtube against traditional Gallup polls and present two major findings.…”

Section: Election Outcomesmentioning

confidence: 99%

Using Social Media to Predict the Future: A Systematic Literature Review

Phillips¹,

Dowling²,

Shaffer³

et al. 2017

Preprint

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Social media (SM) data provides a vast record of humanity's everyday thoughts, feelings, and actions at a resolution previously unimaginable. Because user behavior on SM is a reflection of events in the real world, researchers have realized they can use SM in order to forecast, making predictions about the future. The advantage of SM data is its relative ease of acquisition, large quantity, and ability to capture socially relevant information, which may be difficult to gather from other data sources. Promising results exist across a wide variety of domains, but one will find little consensus regarding best practices in either methodology or evaluation. In this systematic review, we examine relevant literature over the past decade, tabulate mixed results across a number of scientific disciplines, and identify common pitfalls and best practices. We find that SM forecasting is limited by data biases, noisy data, lack of generalizable results, a lack of domain-specific theory, and underlying complexity in many prediction tasks. But despite these shortcomings, recurring findings and promising results continue to galvanize researchers and demand continued investigation. Based on the existing literature, we identify research practices which lead to success, citing specific examples in each case and making recommendations for best practices. These recommendations will help researchers take advantage of the exciting possibilities offered by SM platforms.

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“…It is no surprise, then, that election forecasting has become a big business, for polling firms, news organizations, and betting markets as well as for research scholars. Research scholar uses different techniques and methods for forecasting elections, some uses economics parameters [2] while other included social media [3] as a mere dimension for predictions with some computational techniques like classification regression and Mean Absolute error [4]. The era of predictions markets becomes more momentous when Mr. Barack Obama strategically used Social media chiefly twitter in his elections process [5].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Power of Social Media in Election Predictions

Koli¹,

Ahmed²

2019

IJRTE

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The forecasting of election’s outcome remained prevailed in prominence from pre-historic times and is still a delightful topic of the current era. The predictions of election results have been started from traditional methods to economic indicators and now is being swung by social media especially sentimental analysis. The present studies discuss the election forecasting methods carried out in diverse nations by the number of researchers till now. Furthermore, different number of approaches for electoral prediction using social media and economic dimensions has been investigated based on previous literature work. The main focus of this work is to study and examine various techniques, methods and parameters used for election predictions in distinct areas. Finally, we suggest some intelligent techniques which will be based upon some parameters such as the development agenda, party type and religionism etc for further modification in election prediction system, so as to enhance the accuracy of political forecasting globally

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A Comparative Study of Social Media Prediction Potential in the 2012 U.S. Republican Presidential Preelections

Cited by 5 publications

References 6 publications

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis

Using Social Media to Predict the Future: A Systematic Literature Review

Exploring the Power of Social Media in Election Predictions

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