Electronic Theses and Dissertations

Identifier

2483

Date

2015

Date of Award

10-5-2015

Document Type

Thesis

Degree Name

Master of Science

Major

Psychology

Concentration

General Psychology

Committee Chair

Xiangen Hu

Committee Member

Stephanie Huette

Committee Member

Benjamin Daniel Nye

Abstract

The challenge of predicting meme success has gained attention from researchers, largely due to the increased availability of social media data. Many models focus on structural features of online social networks as predictors of meme success. The current work takes a different approach, predicting meme success from linguistic features.We propose predictive power is gained by grounding memes in theories of working memory, emotion, memory, and psycholinguistics. The linguistic content of several memes were analyzed with linguistic analysis tools. These features were then trained with a multilayer supervised backpropagation network. A set of new memes was used to test the generalization of the network. Results indicated the network was able to generalize the linguistic features in order to predict success at greater than chance levels (80% accuracy). Linguistic features appear to be enough to predict meme transmission success without any information about social network structure.

Comments

Data is provided by the student.

Library Comment

dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

Share

COinS