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Natural Language Use of Candidates and Vote Count During the Midterm Election

April 28, 2020

Abstract: In the modern age of politics, political candidates use Twitter to express their ideas and connect with voters. In 2018, Twitter was used by nearly every candidate for the U.S. House of Representatives throughout their political campaign. To analyze the language used on Twitter, we used Linguistic Inquiry and Word Count (LIWC) to analyze a text file (for each candidate) of all tweets from July 1, 2018 to November 6, 2018 to produce a descriptive output of language use in the months preceding the midterm elections. Consistent with past studies, it was predicted that candidates would use words characterized by negative affect on Twitter in order to connect with voters on an emotional level and to gain votes. In-depth analysis relating linguistic variables to vote count provided insight into how politicians used language on Twitter to improve their popularity. As theorized, candidates who used more words consisting of negative emotion obtained a greater number of votes than that of their counterparts. These findings provided support for the hypothesis that words of negative affect are deemed more impactful than neutral or positive words in politics, and that such language is highly correlated, regardless of party affiliation, with vote count. These findings provide a greater understanding of linguistics in the modern age of politics and provide insight into how increasingly prevalent social media platforms are factoring into politics.

Keywords: Political candidates, natural language, vote count, midterm election


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