Name
Abstract | ||
|---|---|---|
Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using models built from a corpus of texts from multiple cultures in order to learn about each represented people group separately. To do this, we employ a topic modeling approach to quantify open-ended writing responses describing personal values and everyday behaviors in two distinct cultures. We show that some topics are more prominent in one culture compared to the other, while other topics are mentioned to similar degrees. Furthermore, our results indicate that culture influences how value-behavior relationships are exhibited. While some relationships exist in both cultural groups, in most cases we see that the observed relations are dependent on the cultural background of the data set under examination. |
Year | Venue | Field |
|---|---|---|
2016 | AAAI Spring Symposia | Cultural group selection,Computer science,Pattern language,Artificial intelligence,Topic model,Linguistics,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Steven R. Wilson | 1 | 12 | 7.21 |
| Rada Mihalcea | 2 | 6460 | 445.54 |
| Ryan L. Boyd | 3 | 8 | 2.69 |
| James W. Pennebaker | 4 | 256 | 21.87 |