Donald Trump’s administration has seen several “rogue” Twitter accounts claiming to be from staffers in the White House and other US governmental agencies. The accounts purport to provide snippets of insider information to the public; its “revelations” extend from exampled staff behaviour and overheard speech from inside the West Wing to the actions of Trump and other senior White House figures.
One such account is @RoguePOTUSStaff, which claims to be maintained by “the unofficial resistance team within the White House”, currently with 865,000 Twitter followers. There is much speculation over the account’s legitimacy, and the person or people behind the account refuse to divulge their identity; of course, any actions that the “rogue” staffers could take to “prove” themselves might entail potentially serious personal and legal consequences.
But without providing direct evidence, whoever is behind the account is using other means to establish credibility. In particular, we can look at their use of language to legitimise themselves and their cause.
The best tools for doing this date back to Aristotle’s Rhetoric, in which he identified three key ways in which discourse (especially in the public sphere) can be persuasive and effective: “pathos”, an appeal to the emotions of the audience; “logos”, an appeal to reason and rational argument; and “ethos”, the work of establishing and reinforcing the reliability of the speaker or author.
Linguists use these concepts to analyse how political language works. Some of the most persuasive and masterful language uses all three to create a particular effect on the audience. But far as the tweets by the “rogue” White House staffers are concerned, ethos (credibility) is clearly the crucial issue, and their tweets try to establish it in three main ways.
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Most crucially, the account depends on creating the impression of close proximity to the action. Many of its tweets make a point of quoting Trump and others directly, and more to the point implicitly claim to be doing so in something close to real time:

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