Forecasting executive approval with social media data: opportunities, challenges and limitations

Juan S. Gómez Cruces ()
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Juan S. Gómez Cruces: Georgia State University

Journal of Computational Social Science, 2024, vol. 7, issue 2, No 33, 2029-2065

Abstract: Abstract In recent years, several studies have explored the potential of social media data in forecasting electoral outcomes and public opinion trends, with mixed results. This paper presents a novel approach to forecasting modeling, employing data from daily approval polls for 21 executives across Asia, Europe, North America, and Latin America, as well as social media data from their respective Twitter accounts. Machine learning models are trained using these data to predict future executive approval ratings. Through extensive testing of different models, the findings reveal that a combination of previous approval ratings and social media data yields superior performance in predicting future approval. Additionally, models using exclusively social media data exhibit slightly lower performance; however, it remains acceptable for political contexts where executives are highly active on these platforms. This study offers valuable insights into the effective use of social media data for executive approval forecasting, presenting a comparative analysis across diverse political contexts.

Keywords: Forecasting; Executive approval; Social media; Public opinion (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s42001-024-00299-y

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