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.

中文翻译：

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从社交媒体计数数据中过滤公众关注的强度跳跃

在过去十年中，从在线社交媒体数据（如 Twitter）中获得的计数时间序列引起了学术界和市场分析师越来越多的兴趣。将 Web 活动记录转换为计数会产生具有特殊特征的时间序列，包括平滑路径和突然跳跃的共存，以及横截面和时间依赖性。本文使用有关英国和美国国家风险的 Twitter 帖子，提出了一种创新的状态空间模型，用于具有跳跃的多元计数数据。我们使用提议的模型来评估这些国家的公众关注对市场体系的影响。为此，从 Twitter 数据中推断出的公众担忧被分解为特定于国家的持久术语、风险社会放大事件和国家系列的共同运动。