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Article

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

1
Department of Media and Communication, City University of Hong Kong, Hong Kong 999077, China
2
HKUST Business School, Hong Kong University of Science and Technology, Hong Kong 999077, China
3
Department of Communications and New Media, National University of Singapore, Singapore 117416, Singapore
*
Author to whom correspondence should be addressed.
Information 2020, 11(4), 187; https://doi.org/10.3390/info11040187
Received: 7 February 2020 / Revised: 19 March 2020 / Accepted: 27 March 2020 / Published: 31 March 2020
(This article belongs to the Special Issue Digital Citizenship and Participation 2018)
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. View Full-Text
Keywords: social media; public opinion; computational methods; meta-analysis social media; public opinion; computational methods; meta-analysis
MDPI and ACS Style

Skoric, M.M.; Liu, J.; Jaidka, K. Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis. Information 2020, 11, 187. https://doi.org/10.3390/info11040187

AMA Style

Skoric MM, Liu J, Jaidka K. Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis. Information. 2020; 11(4):187. https://doi.org/10.3390/info11040187

Chicago/Turabian Style

Skoric, Marko M., Jing Liu, and Kokil Jaidka. 2020. "Electoral and Public Opinion Forecasts with Social Media Data: A Meta-Analysis" Information 11, no. 4: 187. https://doi.org/10.3390/info11040187

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