Social media posts are used to examine what people experience in their everyday
lives. A new method is developed for assessing the situational characteristics of social
media posts based on the words used in these posts. To accomplish this, machine learning
models are built that accurately approximate the judgments of human raters. This new
method of situational assessment is applied on two of the most popular social media sites:
Twitter and Facebook. Millions of Tweets and Facebook statuses are analyzed. Temporal
patterns of situational experiences are found. Geographic and gender differences in
experience are examined. Relationships between personality and situation experience
were also assessed. Implications of these finding and future applications of this new
method of situational assessment are discussed. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection
| Identifer | oai:union.ndltd.org:fau.edu/oai:fau.digital.flvc.org:fau_33491 |
| Contributors | Serfass, David G. (author), Sherman, Ryne A. (Thesis advisor), Nowak, Andrzej (Thesis advisor), Florida Atlantic University (Degree grantor), Charles E. Schmidt College of Science, Department of Psychology |
| Publisher | Florida Atlantic University |
| Source Sets | Florida Atlantic University |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation, Text |
| Format | 113 p., application/pdf |
| Rights | Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder., http://rightsstatements.org/vocab/InC/1.0/ |
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