Impressive Words: Linguistic Predictors of Public Approval of the U.S. Congress
نویسندگان
چکیده
What type of language makes the most positive impression within a professional setting? Is competent/agentic language or warm/communal language more effective at eliciting social approval? We examined this basic social cognitive question in a real world context using a "big data" approach-the recent record-low levels of public approval of the U.S. Congress. Using Linguistic Inquiry and Word Count (LIWC), we text analyzed all 123+ million words spoken by members of the U.S. House of Representatives during floor debates between 1996 and 2014 and compared their usage of various classes of words to their public approval ratings over the same time period. We found that neither agentic nor communal language positively predicted public approval. However, this may be because communion combines two disparate social motives (belonging and helping). A follow-up analysis found that the helping form of communion positively predicted public approval, and did so more strongly than did agentic language. Next, we conducted an exploratory analysis, examining which of the 63 standard LIWC categories predict public approval. We found that the public approval of Congress was highest when politicians used tentative language, expressed both positive emotion and anxiety, and used human words, numbers, prepositions, numbers, and avoided conjunctions and the use of second-person pronouns. These results highlight the widespread primacy of warmth over competence as the primary dimensions of social cognition.
منابع مشابه
A decline in prosocial language helps explain public disapproval of the US Congress.
Talking about helping others makes a person seem warm and leads to social approval. This work examines the real world consequences of this basic, social-cognitive phenomenon by examining whether record-low levels of public approval of the US Congress may, in part, be a product of declining use of prosocial language during Congressional debates. A text analysis of all 124 million words spoken in...
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