Name
Title | ||
|---|---|---|
Predicting Smartphone Users' General Responsiveness To Im Contacts Based On Im Behavior |
Abstract | ||
|---|---|---|
History of conversations through instant messaging (IM) contains abundant information about the communication patterns of the dyad, including conversation partners' mutual responsiveness to messages. We have, however, not seen many examinations of using such information in modeling mobile users' responsiveness in IM communication. In this paper, we present an in-the-wild study, in which we leverage participants' IM messaging logs to build models predicting their general responsiveness. Our models based on data from 33 IM user achieved an accuracy of up to 71% (AUROC). In particular, we show that 90-day IM-communication patterns, in general, outperformed their 14-day equivalent in our prediction models, indicating better coherence between long-term IM patterns with their general communication experience. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3338286.3344387 | PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON HUMAN-COMPUTER INTERACTION WITH MOBILE DEVICES AND SERVICES (MOBILEHCI'19) |
Keywords | Field | DocType |
Mobile notifications, mobile receptivity, ESM, machine learning | Computer science,Human–computer interaction | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao-Ping Lee | 1 | 4 | 4.44 |
| Tilman Dingler | 2 | 281 | 37.00 |
| Chih-Heng Lin | 3 | 24 | 4.69 |
| Kuan-Yin Chen | 4 | 27 | 2.94 |
| Yu-Lin Chung | 5 | 0 | 0.68 |
| Chia-Yu Chen | 6 | 6 | 2.91 |
| Yung-Ju Chang | 7 | 58 | 20.93 |