Ipsos Social Media Analysis: Trump Statements in Helsinki

Overwhelmingly Negative Response, Across the Political Spectrum, to Trump’s Statements after Summit with Putin

The author(s)
  • Clifford Young President, US, Public Affairs
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An analysis of the first 600,000 tweets from the United States mentioning U.S. President Donald J. Trump and Russian President Vladimir V. Putin during and after their joint press conference in Helsinki shows overwhelmingly negative reactions. Net sentiment toward Mr. Trump is negative in every single one of the 50 states, ranging from -0.53 to -0.71 on a scale of +1.0 to -1.0. 

The main themes of Twitter conversations about the U.S. President’s performance at the Helsinki summit press conference are, in order:
-    Disapproval of Donald Trump (21%)
-    Treason (9%)
-    General coverage of the press conference (6%)
-    Trump and Russian intelligence agencies (6%)
-    Trump meeting with Putin (5%)
-    Disapproval of Republicans (5%)
-    Hillary Clinton’s response (3%)
-    Collusion and election interference (3%)
-    John McCain’s response (3%)
-    Trump is a Russian asset (3%)
-    U.S. intelligence (2%)
-    Liz Cheney’s response (2%)
-    Robert Mueller (2%) 

An analysis of the first 10,000 Reddit comments on five major political subreddits spanning the political spectrum shows strongly negative net sentiment in each one of them – not only on left-leaning r/progressive (-0.63), r/politics (-0.53), and r/liberal (-0.5), but also on right-leaning r/conservative (-0.51) and even on r/The_Donald (-0.32).

The research was conducted on July 16, 2018, utilizing Ipsos social intelligence analytics, including proprietary machine learning algorithms applied to social media. TFIDF (term frequency-inverse document frequency) was utilized to vectorize social media mentions in text analytics and topic modeling. It is a numerical statistic, which reflects the weight (importance) of words to a document. In order to identify groups of users with similar interests, Ipsos Public Affairs utilized the clustering algorithm non-negative matrix factorization (NMF). When applied to text, this technique identifies terms and phrases that pattern together across texts. 

Contact: Clifford Young, President, Ipsos Public Affairs, [email protected], 312.375.3328

The author(s)
  • Clifford Young President, US, Public Affairs