Mining for Signals of Future Consumer Expenditure on Twitter and Google Trends

Pekar, Viktor (2018). Mining for Signals of Future Consumer Expenditure on Twitter and Google Trends. IN: Proceedings of 2nd International Conference on Advanced Reserach Methods and Analytics (Internet and Big Data in Economics and Social Sciences). Valencia, Spain: Editorial Universitat Politècnica de València.

Abstract

Consumer expenditure constitutes the largest component of Gross Domestic Product in developed countries, and forecasts of consumer spending are therefore an important tool that governments and central bank use in their policy-making. In this paper we examine methods to forecast consumer spending from user-generated content, such as search engine queries and social media data, which hold the promise to produce forecasts much more efficiently than traditional surveys. Specifically, the aim of the paper is to study the relative utility of evidence about purchase intentions found in Google Trends versus those found in Twitter posts, for the problem of forecasting consumer expenditure. Our main findings are that, firstly, the Google Trends indicators and indicators extracted from Twitter are both beneficial for the forecasts: adding them as exogenous variables into regression model produces improvements on the pure AR baseline, consistently across all the forecast horizons. Secondly, we find that the Google Trends variables seem to be more useful predictors than the semantic variables extracted from Twitter posts, the differences in performance are significant, but not very large.

Publication DOI: https://doi.org/10.4995/CARMA2018.2018.8337
Divisions: Aston Business School > Operations & information management
Additional Information: This work is licensed under a Creative Commons License CC BY-NC-ND 4.0
Event Title: 2nd International Conference on Advanced Research Methods and Analytics (CARMA 2018)
Event Type: Other
Event Dates: 2018-07-12 - 2018-07-13
Full Text Link:
Related URLs: http://ocs.edit ... wFile/8337/4286 (Publisher URL)
Published Date: 2018-07-13
Authors: Pekar, Viktor ( 0000-0002-9664-1675)

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