  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.
