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


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:
Divisions: College of Business and Social Sciences > 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
ISBN: 9788490486894
Last Modified: 08 Dec 2023 12:55
Date Deposited: 28 Mar 2019 08:46
Full Text Link:
Related URLs: http://ocs.edit ... wFile/8337/4286 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2018-09-07
Accepted Date: 2018-07-01
Authors: Pekar, Viktor (ORCID Profile 0000-0002-9664-1675)

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