Addressing supporters and enemies: Socio-semantic networks of tweets by Donald Trump

Iina Hellsten, Rens Vliegenthart


Twitter has become a tool for directly addressing the publics by political leaders, directly addressing a number of controversial issues, such as immigration. How political leaders use Twitter, and other social media to address societal issues is gaining interest in academic research into social media, in general, and online communication networks, in particular. The US President, Donald Trump, is notoriously active Twitter user, who addressed immigration as one of his campaign themes prior to his election, and as a main policy issue after he became the President of the US in January 2017. Earlier studies have pointed out that Trump’s style of tweeting has not changed after his presidency(e.g. Clark & Grieve, 2019), and that his tweets with factually correct information contain more hashtags and @usernames than tweets containing false information (van der Zee, Poppe, Havrileck & Baillon, 2019). We ask how Trump tweeted about immigration prior and after he became the President, and how the socio-semantic networks of addressing other actors (using the @username construction) and using hashtags (#hashtag construction) have changed over time. We collected tweets sent from the Twitter account @RealDonaldTrump that address immigration from the publicly available Trump Twitter Archive ( Total of 392 tweets sent in between 1/1/2015 (starting date of the data archive) and 19/1/2017 (day before inauguration), and 827 tweets sent in between 20/1/2017 (inauguration date) to 30/07/2019 (date of our data collection). For the automated analysis we used the publicly available tweet.exe routine to extract the @usernames, RT@usernames and #hashtags in the tweets following the procedure outlined in Hellsten, Wonneberger & Jacobs (2019) and Hellsten & Leydesdorff (2020). The resulting document/word (@usernames, #hashtags and RT@usernames) co-occurrence matrices were analyzed in Pajek, and the resulting networks were exported to VosViewer for clustering and lay-out. Our results show a notable difference in who is addressed and in connection to which issue-related hashtags prior to and after he became the President The earlier tweets focus on addressing mainly conservative media, supporting his presidential campaign, such as FoxNews and BreitbartNews, Frank Caliendo, a comedian on FoxNews, and Ann Coultier, but also liberal media, such as @CNN. After his presidency, the main addressed actors are governmental organizations, such as US Customs and Border Protection (@customsborder), Homeland security (@DHSGov), US citizenship and immigration services (@USCIS), Judicial Watch (@judicialwatch), a conservative activist group investigating misconduct by governmental officials, and re-tweets of the Vice President Pence (RT@VP and RT@mikepence. Whereas there were only two re-tweets in our immigration data prior to his presidency, there were 43 re-tweets after his presidency started – most of them (28) re-tweets of his own tweets. Notably, the socio-semantic network results show a change from addressing media outletsd to addressing governmental departments, as ‘enemies’. We also compare the immigration-related words in the tweets to scrutinize whether the hashtags and addressed users, indeed, spread more factual information than tweets not containing those affordances (van der Zee, et al, 2019).

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