Browsing by Author "Chung, Yu-Lin"

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Does Who Matter? Studying the Impact of Relationship Characteristics on Receptivity to Mobile IM Messages
(2019-01-01)
Lee, Hao-Ping
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Chen, Kuan-Yin
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Lin, Chih-Heng
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Chen, Chia-Yu
;
Chung, Yu-Lin
This study examines the characteristics of mobile instant-messaging users' relationships with their social contacts and the effects of both relationship and interruption context on four measures of receptivity: Attentiveness, Responsiveness, Interruptibility, and Opportuneness. Overall, interruption context overshadows relationship characteristics as predictors of all four of these facets of receptivity; this overshadowing was most acute for Interruptibility and Opportuneness, but existed for all factors. In addition, while Mobile Maintenance Expectation and Activity Engagement were negatively correlated with all receptivity measures, each such measure had its own set of predictors, highlighting the conceptual differences among the measures. Finally, delving more deeply into potential relationship effects, we found that a single, simple closeness question was as effective at predicting receptivity as the 12-item Unidimensional Relationship Closeness Scale.
Predicting Smartphone Users' General Responsiveness to IM Contacts Based on IM Behavior
(2019-01-01)
Lee, Hao-Ping
;
Chung, Yu-Lin
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Dingier, Tilman
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Chen, Chia-Yu
;
Lin, Chih-Heng
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.