Filtering the intensity of public concern from social media count data with jumps

TL;DR

This paper introduces a novel state space model for multivariate count time series with jumps, derived from social media data, to analyze public concern impacts on financial market volatility.

Contribution

It proposes an innovative model capturing jumps and dependence in count data from social media, linking public concern dynamics to market risk spillovers.

Findings

Identified persistent and jump components in Twitter-based count data.

Detected significant country-risk spillovers affecting financial market volatility.

Uncovered social amplification effects influencing market responses.

Abstract

Count time series obtained from online social media data, such as Twitter, have drawn increasing interest among academics and market analysts over the past decade. Transforming Web activity records into counts yields time series with peculiar features, including the coexistence of smooth paths and sudden jumps, as well as cross-sectional and temporal dependence. Using Twitter posts about country risks for the United Kingdom and the United States, this paper proposes an innovative state space model for multivariate count data with jumps. We use the proposed model to assess the impact of public concerns in these countries on market systems. To do so, public concerns inferred from Twitter data are unpacked into country-specific persistent terms, risk social amplification events, and co-movements of the country series. The identified components are then used to investigate the existence and magnitude of country-risk spillovers and social amplification effects on the volatility of financial markets.