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TweetFit: Fusing Multiple Social Media and Sensor Data for Wellness Profile Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| fuse all four sources (text, visual, venue, sensor) instead of text alone (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.178 -> 0.246 | Helped |
| add sensor data to text + venue + visual sources (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.163 -> 0.246 | Helped |
| add textual data to sensors + venue + visual (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.221 -> 0.246 | Helped |
| add visual data to sensors + text + venue (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.205 -> 0.246 | Helped |
| add venue semantics & mobility to sensors + text + visual (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.164 -> 0.246 | Helped |
| add visual features to text (ablation) | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.178 -> 0.166 | Hurt |
| TweetFit multi-source multi-task vs MTFL | TweetFit · NUS-SENSE BMI category | Macro-F1: 0.184 -> 0.211 | Helped |
| TweetFit vs MTFL on BMI Trend | TweetFit · NUS-SENSE BMI Trend | Macro-F1: 0.710 -> 0.718 | Helped |
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