A growing share of human interactions now occurs online, where the expression and perception of emotions are often amplified and distorted. Yet, the interplay between different emotions and the extent to which they are driven by external stimuli or social feedback remains poorly understood. We calibrate a multivariate Hawkes self-exciting point process to model the temporal expression of six basic emotions in YouTube Live chats. This framework captures both temporal and cross-emotional dependencies while allowing us to disentangle the influence of video content (exogenous) from peer interactions (endogenous). We find that emotional expressions are up to four times more strongly driven by peer interaction than by video content. Positivity is more contagious, spreading three times more readily, whereas negativity is more memorable, lingering nearly twice as long. Moreover, we observe asymmetric cross-excitation, with negative emotions frequently triggering positive ones, a pattern consistent with trolling dynamics, but not the reverse. These findings highlight the central role of social interaction in shaping emotional dynamics online and the risks of emotional manipulation as human-chatbot interactions become increasingly realistic.

翻译：如今，越来越多的人际互动发生在线上环境中，其中情感的表达与感知常被放大和扭曲。然而，不同情感之间的相互作用，以及它们在多大程度上受外部刺激或社交反馈驱动，目前仍知之甚少。本研究采用多元霍克斯自激励点过程进行校准，以建模YouTube直播聊天中六种基本情感的时间表达。该框架同时捕捉了时间依赖性与跨情感依赖性，并允许我们区分视频内容（外生性）与同伴互动（内生性）的影响。我们发现，情感表达受同伴互动驱动的强度最高可达视频内容驱动的四倍。积极情感的传染性更强，其传播速度是消极情感的三倍；而消极情感的记忆性更持久，其持续时间近乎积极情感的两倍。此外，我们观察到非对称的交叉激励现象：消极情感频繁触发积极情感，这一模式与网络挑衅行为动态一致，但反向触发并不显著。这些发现凸显了社交互动在塑造线上情感动态中的核心作用，以及随着人机聊天交互日益逼真所带来的情感操纵风险。