The Political Campaign as Interaction of Candidates and Citizens

• In Event: Communicating Politics on Twitter

Abstract

Televised campaign debates are now a standard part of presidential election campaigns. They focus the attention of the electorate as they bring candidates together facilitating comparative judgments. The research about debates is substantial. It has focused primarily on the knowledge about issues acquired by viewers, the perceptions of the character of candidates, and the extent to which candidate preferences are altered or not.

The focus of this research is on the campaign as interaction of candidates and citizens. For candidates the campaign is communication designed to convince citizens to vote for the candidate. Until the advent of social media the response of citizens was muted because there was no infrastructure to carry that response other than survey research which can only handle a modest response. Social media becomes a much fuller response by citizens as they express their views of what they have seen and heard. Given the responses of citizens candidates may refocus their communication. This research examines the extent of feedback producing interaction in the campaign -- candidate to voters, voters to candidates, candidates to voters in a feedback pattern of interaction.

The succession of one candidate debate after another and the availability of social media produces a communication environment in which this interaction becomes possible. It is also a communication environment in which the potential for interaction can be studied.

The research will examine communication in connection with the last six campaign debates of the Democrats beginning with December 2019 and through April 2020. The transcript of the debates are readily available, and we will collect twitter messages about each of the candidates before and after the debate. The collection of tweets will continue daily to assess temporality of the response to the debates.

Data collection: At the end of each day NodeXL is used to make a collection for each of the candidates eligible for participation in the debate. This yields between 50K an 80K tweets per day per candidate. There is some variation from day to day depending on what happened that day. Over a three and a half month period that will produce a substantial collection for each candidate.

Analysis: The basic scheme is comparing themes in the communication before the debate and after the debate. For example, in December one change was a substantial increase in the tweets about Warren mentioning corruption. There were few before the debate and substantially more after the debate. There are comparable comparisons for each candidate in the debate. For December it was 7 candidates, and for January it is 6 candidates. Given the rules for eligibility the number of candidates will continue to decrease over time. The results of this analysis permits examining the interaction between candidates and citizens as the campaign progresses. NodeXL is used for collection because it is designed for analysis of networks of communication and specifically for looking at sub-networks that may appear in the tweets. When the sub-networks are distinct we can determine how they differed in their response to the candidate.

The debates gives a specific point of attention to the campaign. However, candidates do not stop campaigning between debates and citizens do not ignore the campaigns between debates. Our daily collection of tweets will show us when there are changes in how citizens are responding to the campaigns. After the four week December to January break the debates are close enough together that candidate adjustments should show up in the next debate. These changes can be factored into the analysis of the interaction in the campaign.

Obviously this research is not nearing completion since five of the debates have not yet occurred. But the last debate in the sequence is in April. That will give ample time to complete the analysis before presentation of our results at the meeting in September.

Authors

• George Robert Boynton, University of Iowa

• Glenn W. Richardson Jr., Kutztown University of Pennsylvania