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

• Main Authors: Marko M. Skoric, Jing Liu, Kokil Jaidka
• Format: Article
• Language: English
• Published: MDPI AG 2020-03-01
• Series: Information
• Subjects: social media; public opinion; computational methods; meta-analysis
• Online Access: https://www.mdpi.com/2078-2489/11/4/187

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.