Tactics and Tallies: A Study of the 2016 U.S. Presidential Campaign Using Twitter 'Likes'
April 06, 2017 Β· Declared Dead Β· π Proceedings of the International AAAI Conference on Web and Social Media
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yu Wang, Xiyang Zhang, Jiebo Luo
arXiv ID
1704.02042
Category
cs.SI: Social & Info Networks
Citations
3
Venue
Proceedings of the International AAAI Conference on Web and Social Media
Last Checked
5 months ago
Abstract
We propose a framework to measure, evaluate, and rank campaign effectiveness in the ongoing 2016 U.S. presidential election. Using Twitter data collected from Sept. 2015 to Jan. 2016, we first uncover the tweeting tactics of the candidates and second, using negative binomial regression and exploiting the variations in 'likes,' we evaluate the effectiveness of these tactics. Thirdly, we rank the candidates' campaign tactics by calculating the conditional expectation of their generated 'likes.' We show that while Ted Cruz and Marco Rubio put much weight on President Obama, this tactic is not being well received by their supporters. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates and that the women issue is equally emphasized in Sanders' campaign as in Clinton's. Finally, we suggest two ways that politicians can use the feedback mechanism in social media to improve their campaign: (1) use feedback from social media to improve campaign tactics within social media; (2) prototype policies and test the public response from the social media.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Social & Info Networks
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Fake News Detection on Social Media: A Data Mining Perspective
R.I.P.
π»
Ghosted
Natural Scales in Geographical Patterns
R.I.P.
π»
Ghosted
Representation Learning on Graphs: Methods and Applications
R.I.P.
π»
Ghosted
The COVID-19 Social Media Infodemic
R.I.P.
π»
Ghosted
OSMnx: New Methods for Acquiring, Constructing, Analyzing, and Visualizing Complex Street Networks
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted