We investigate feedback mechanisms in political communication by testing whether politicians adapt the sentiment of their messages in response to public engagement. Using over 1.5 million tweets from Members of Parliament in the United Kingdom, Spain, and Greece during 2021, we identify sentiment dynamics through a simple yet interpretable linear model. The analysis reveals a closed-loop behavior: engagement with positive and negative messages influences the sentiment of subsequent posts. Moreover, the learned coefficients highlight systematic differences across political roles: opposition members are more reactive to negative engagement, whereas government officials respond more to positive signals. These results provide a quantitative, control-oriented view of behavioral adaptation in online politics, showing how feedback principles can explain the self-reinforcing dynamics that emerge in social media discourse.
翻译:本研究通过检验政治家是否根据公众参与度调整其信息的情感倾向,探讨了政治传播中的反馈机制。我们利用2021年英国、西班牙和希腊议会议员发布的超过150万条推文,通过一个简单且可解释的线性模型识别情感动态。分析揭示了一种闭环行为:对积极和消极信息的参与度会影响后续帖子的情感倾向。此外,学习得到的系数突显了政治角色间的系统性差异:反对派成员对消极参与度更为敏感,而政府官员则对积极信号反应更强。这些结果为在线政治中的行为适应提供了定量化、控制导向的视角,展示了反馈原理如何解释社交媒体话语中出现的自我强化动态。