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Predicting Online Protest Participation of Social Media Users
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add messages interacting with the user (drift term n_i) to his own status history (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.636 -> 0.702 | Helped |
| add interaction-network regularizer (w_reg 0 -> 0.1) (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.681 -> 0.702 | Helped |
| increase w_reg to 0.5 (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.702 -> 0.716 | Helped |
| increase w_reg to 2 or above (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.716 -> 0.689 | Mixed |
| increase training data from 10% to 40% (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.674 -> 0.704 | Helped |
| increase training data from 50% to 90% (ablation) | Our Method · Nigerian election protest Twitter | Accuracy: 0.697 -> 0.656 | Hurt |
| use proposed framework instead of strongest baseline (Time Topic) | next-post protest prediction · Nigerian election protest Twitter | F1: 0.715 -> 0.817 | Helped |
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