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Filtering the Intensity of Public Concern from Social Media Count Data with Jumps

Author

Listed:
  • Matteo Iacopini

    (QMUL - Queen Mary University of London, VU - Vrije Universiteit Amsterdam [Amsterdam])

  • Carlo Romano Marcello Alessandro Santagiustina

    (médialab - médialab (Sciences Po) - Sciences Po - Sciences Po, University of Ca’ Foscari [Venice, Italy])

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.

Suggested Citation

  • Matteo Iacopini & Carlo Romano Marcello Alessandro Santagiustina, 2021. "Filtering the Intensity of Public Concern from Social Media Count Data with Jumps," Sciences Po Economics Publications (main) hal-04494229, HAL.
    • Handle: RePEc:hal:spmain:hal-04494229
    DOI: 10.1111/rssa.12704
    Note: View the original document on HAL open archive server: https://hal.science/hal-04494229v1
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    2. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.
    3. Xiao‐Li Meng, 2021. "Enhancing (publications on) data quality: Deeper data minding and fuller data confession," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1161-1175, October.

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