Name
Title | ||
|---|---|---|
Predicting Delay Discounting from Social Media Likes with Unsupervised Feature Learning. |
Abstract | ||
|---|---|---|
Delay discounting, a behavioral measure of impulsivity, is often used to quantify the human tendency to choose a smaller, sooner reward (e.g., $1 today) over a larger, later reward ($2 tomorrow). Delay discounting and its relation to human decision making is a hot topic in economics and behavior science since pitting the demands of long-term goals against short-term desires is among the most difficult tasks in human decision making. Previously, small-scale studies based on questionnaires were used to analyze an individual's delay discounting rate (DDR) and its relation to his/her real-world behavior such as substance abuse, pathological gambling and poor academic performance. In this research, we employ large-scale social media analytics to study DDR and its relation to people's social media behavior (e.g., their Likes on Facebook). We also build computational models to automatically infer DDR from Social Media Likes. Since the predicting feature space is very large and the size of the delay discounting ground truth dataset is relatively small, we focus on studying the impact of different unsupervised feature learning methods on predicting performance. Our results demonstrate the significant role unsupervised feature learning plays in this task.
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Year | DOI | Venue |
|---|---|---|
2018 | 10.5555/3382225.3382279 | ASONAM '18: International Conference on Advances in Social Networks Analysis and Mining
Barcelona
Spain
August, 2018 |
Keywords | Field | DocType |
unsupervised feature learning,human decision making,behavior science,DDR,real-world behavior,large-scale social media analytics,feature space,social media likes,behavioral measure,delay discounting rate,social media behavior | Feature vector,Social media analytics,Social media,Discounting,Impulsivity,Computer science,Computational model,Ground truth,Artificial intelligence,Machine learning,Feature learning | Conference |
ISSN | ISBN | Citations |
2473-9928 | 978-1-5386-6051-5 | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Ding | 1 | 15 | 8.48 |
| Warren K Bickel | 2 | 12 | 3.51 |
| Shimei Pan | 3 | 684 | 64.41 |