Social media sentiment and consumer confidence

Piet J.H. Daas and Marco J.H. Puts

No 5, Statistics Paper Series from European Central Bank

Abstract: Changes in the sentiment of Dutch public social media messages were compared with changes in monthly consumer confidence over a period of three-and-a-half years, revealing that both were highly correlated (up to r = 0.9) and that both series cointegrated. This phenomenon is predominantly affected by changes in the sentiment of all Dutch public Facebook messages. The inclusion of various selections of public Twitter messages improved this association and the response to changes in sentiment. Granger causality studies revealed that it is more likely that changes in consumer confidence precede those in social media sentiment than vice-versa. A comparison of the development of various seven-day sentiment aggregates with the monthly consumer confidence series confirmed this finding and revealed that the social media sentiment lag is most likely in the order of seven days. This indicates that, because of the ease at which social media sentiment-based data are available and can be processed, they can be published before the official consumer confidence publication and certainly at a higher frequency. All research findings are consistent with the notion that changes in consumer confidence and social media sentiment are affected by an identical underlying phenomenon. An explanation for this phenomenon can be found in the Appraisal-Tendency Framework (Han et al. 2007), which is concerned with consumer decision-making. In this framework, it is claimed that a consumer decision is influenced by two kinds of emotions, namely the incidental and the integral. In this framework, the integral emotion is relevant for the decision at stake, whereas the incidental emotion is not. Based on this theory, consumer confidence is likely to be influenced mainly by the incidental emotion, as consumer confidence is also not measured in relation to an actual decision to buy something. This suggests that the sentiment in social media messages might reflect the incidental emotion in that part of the population that is active on social media. Because of the general nature of the latter, one could denote this the JEL Classification: C55

Keywords: big data; methodology; sentiment; social media; statistics (search for similar items in EconPapers)
Date: 2014-09
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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