ABSTRACT

Does Twitter trigger negative tones in politicians' digital communication? On social media direct feedback mechanisms such as retweets or likes signal to politicians which message and tone are popular. Current research suggests that negative language increases the number of retweets a single tweet receives, indicating preferences for negativity in the audience on Twitter. However, it remains unclear whether politicians adapt to the logic of Twitter or simply follow the rules determined by the broader political context, namely the state of their electoral race. We use sentiment analysis to measure the tone used by 342 candidates in 97,909 tweets in their Twitter campaign in the 2018 midterm elections for the US House of Representatives and map the ideological composition of each politician's Twitter network. We show that the feedback candidates receive creates an incentive to use negativity. The size and direction of the tonal incentive is connected to the ideological composition of the candidate's follower network. Unexpectedly, the platform-specific incentive does not affect the tone used by candidates in their Twitter campaigns. Instead we find that the tone is mainly related to characteristics of the electoral race. We show that our findings are not dependent on our sentiment measurement by validating our results using hand coding and machine learning.

摘要

Twitter 是否会在政客的数字交流中引发负面情绪？在社交媒体上，转发或点赞等直接反馈机制会向政客发出哪些信息和语气受欢迎的信号。目前的研究表明，负面语言会增加单条推文收到的转发数量，这表明 Twitter 上的观众更喜欢负面情绪。然而，目前尚不清楚政客们是适应 Twitter 的逻辑，还是仅仅遵循由更广泛的政治背景决定的规则，即他们的选举状态。我们使用情绪分析来衡量 342 名候选人在 2018 年美国众议院中期选举的 Twitter 竞选活动中使用的 97,909 条推文的语气，并绘制出每个政治家 Twitter 网络的意识形态构成图。我们表明，候选人收到的反馈会激发他们使用消极情绪。调性激励的大小和方向与候选人的追随者网络的意识形态构成有关。出乎意料的是，特定于平台的激励措施并没有影响候选人在 Twitter 竞选活动中使用的语气。相反，我们发现语气主要与竞选的特点有关。我们通过使用手动编码和机器学习验证我们的结果，表明我们的发现不依赖于我们的情绪测量。特定于平台的激励措施不会影响候选人在其 Twitter 活动中使用的语气。相反，我们发现语气主要与竞选的特点有关。我们通过使用手动编码和机器学习验证我们的结果，表明我们的发现不依赖于我们的情绪测量。特定于平台的激励措施不会影响候选人在其 Twitter 活动中使用的语气。相反，我们发现语气主要与竞选的特点有关。我们通过使用手动编码和机器学习验证我们的结果，表明我们的发现不依赖于我们的情绪测量。