Twitter gained new levels of political prominence with Donald J. Trump’s use of the platform. Although previous work has been done studying the content of Trump’s tweets, there remains a dearth of research exploring who opinion leaders were in the early days of his presidency and what they were tweeting about. Therefore, this study retroactively investigates opinion leaders on Twitter during Trump’s 1st month in office and explores what those influencers tweeted about. We uniquely used a historical data set of 3 million tweets that contained the word “trump” and used Latent Dirichlet Allocation, a probabilistic algorithmic model, to extract topics from both general Twitter users and opinion leaders. Opinion leaders were identified by measuring eigenvector centrality and removing users with fewer than 10,000 followers. The top 1% users with the highest score in eigencentrality (N = 303) were sampled, and their attributes were manually coded. We found that most Twitter-based opinion leaders are either media outlets/journalists with a left-center bias or social bots. Immigration was found to be a key topic during our study period. Our empirical evidence underscores the influence of bots on social media even after the 2016 U.S. presidential election, providing further context to ongoing revelations and disclosures about influence operations during that election. Furthermore, our results provide evidence of the continued relevance of established, “traditional” media sources on Twitter as opinion leaders.

由于唐纳德·J·特朗普（Donald J. Trump）使用该平台，Twitter在政治上的地位得到了新的提高。尽管先前的工作已经完成了对特朗普推文内容的研究，但仍缺乏研究来探讨特朗普担任总统期间的意见领袖是谁以及他们的推文。因此，本研究回顾了特朗普上任第一个月期间推特上的舆论领袖，并探讨了那些影响者发表的推文。我们独特地使用了包含300万条推文的历史数据集，其中包含“王牌”一词，并使用了概率算法模型Latent Dirichlet Allocation，从Twitter的一般用户和意见领袖中提取主题。通过测量特征向量中心度并删除关注者少于10,000个的用户来确定意见领袖。N = 303），对其属性进行了手动编码。我们发现，大多数基于Twitter的意见领袖都是偏左偏见的媒体/新闻工作者或社交机器人。在我们的研究期间，移民是一个关键主题。我们的经验证据强调，即使在2016年美国总统大选之后，机器人程序也会对社交媒体产生影响，为此次选举期间正在进行的有关影响力运作的启示和披露提供了进一步的背景信息。此外，我们的结果提供了证据，证明Twitter上已建立的“传统”媒体资源作为舆论领袖的持续意义。