Predicting Election Outcomes from Positive and Negative Trait Assessments of Candidate Images
By Kyle Mattes, Michael Spezio, Hackjin Kim, Alexander Todorov, Ralph Adolphs, R. M. Alvarez. 2010
110 citations recorded by the source corpus.
Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/33371d6ce404a4476ea1df30736b3902a0b96e5c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cognitive Neuroscience
- Christopher Y. Olivola
- Ron Dotsch
- Peter Mende-Siedlecki
- Alexander Todorov
- Nikolaas N. Oosterhof
- M. Petersen
- Lasse Laustsen
In Experimental and Cognitive Psychology
- Christopher Y. Olivola
- Ron Dotsch
- Peter Mende-Siedlecki
- Alexander Todorov
- Geng Cui
- Ling Peng
- Yuho Chung
- Wanyi Zheng
In Sociology and Political Science
Citations in the corpus
- Social attributions from faces: determinants, consequences, accuracy, and functional significance. (2015, background): “…outcomes are predicted by how dominant (Chen et al. 2014, Chiao et al. 2008, Little et al. 2007), sociable (Castelli et al. 2009), threatening (Mattes et al. 2010, Spezio et al. 2008), stereotypically Republican (Olivola et al. 2012), and even stereotypically politician-like (Olivola et al.…”
- The Faces of Success: Beauty and Ugliness Premiums in e-Commerce Platforms (2020, background): “First, like candidates in political campaigns who often enhance their images (Mattes et al. 2010), aspiring entrepreneurs in social selling and C2C e-commerce should be mindful of their self-presentation; attractive appearances help create a favorable impression and gain the trust of shoppers.”