<p> 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.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:10172674 |
| Date | 14 October 2016 |
| Creators | Serfass, David G. |
| Publisher | Florida Atlantic University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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