Use of Chinese Short Messages

Ma, Dafei; Ichikawa, Fumiko; Liu, Ying; Jiang, Li

doi:10.1007/978-3-540-73354-6_64

Cited by 4 publications

(7 citation statements)

References 1 publication

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“…When we calculated the predicted speed while the predictive feature is on as Equation 15 shows, we directly used for T the average time (3059 ms) calculated based on all characters of the corpus. However, the characters that were entered by the predictive feature should have been removed from this number.…”

Section: Discussionmentioning

confidence: 99%

“…By May of 2009, there were 687 million mobile phone users in mainland China [27], many of whom also use short message services (SMS). SMS use has seen a tremendous increase in China since its release in 2000 [15]. In May of 2009, Chinese users exchanged more than 64 billion short messages, with over 3 messages per user per day [27].…”

Section: Introductionmentioning

confidence: 99%

“…Equations 14 (S pre_off ) and 15 (S pre_on ) show how we calculate the predicted speeds when the predictive feature is off and on. In Equation15, P pre is the overall percentage of characters entered by the predictive feature only. The predicted speeds are presented inTable 6.S pre_on = 60 / {[T×(1 − P pre ) + T pre ×P pre ] / 1000}(15) To be comparable with text entry rates of other languages, we used WPM to present results on text entry rate.…”

mentioning

confidence: 99%

“…In Equation15, P pre is the overall percentage of characters entered by the predictive feature only. The predicted speeds are presented inTable 6.S pre_on = 60 / {[T×(1 − P pre ) + T pre ×P pre ] / 1000}(15) To be comparable with text entry rates of other languages, we used WPM to present results on text entry rate. Time of all extra key presses, for example, for making and clearing errors were all removed from the task completion time to make sure the calculated text entry rates are comparable with the predicted ones.Table 6.…”

mentioning

confidence: 99%

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Predicting Chinese text entry speeds on mobile phones

Liu

Räihä

2010

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Chinese text entry on mobile phones is critical considering the large number of Chinese speakers worldwide and as a key task in many core applications. But there is still a lack of both empirical data and predictive models that explore the pattern of user behavior in the process. We propose a model to predict user performance with two types of Chinese pinyin input methods on mobile phones. The model integrates a language model (digraph probability) with Fitts' law for key presses, a keystroke-level model for navigation, and a linear model for visual search in pinyin marks and Chinese characters. We tested the model by comparing its predictions with the empirical measures. The predictions are satisfactory and the percentage differences are all within 4% of the empirical results, suggesting that the model can be used to evaluate user performance of Chinese pinyin text entry solutions on mobile phones.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Predicting Chinese text entry speeds on mobile phones

Liu

Räihä

2010

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Our coding scheme was developed based on existing works on content analysis of social messaging [2,15,23]. We reviewed the collected messages, carefully adjusted the categories to better present our data, and refined the coding scheme iteratively.…”

Section: Codebook Creationmentioning

confidence: 99%

Food messaging

Wei

Ma²,

Zhao

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Food is more than just a means of survival; it is also a form of communication. In this paper, we investigate the potential of food as a social message carrier (a.k.a., food messaging). To investigate how people accept, use, and perceive food messaging, we conducted exploratory interviews, a field study, and follow-up interviews over four weeks in a large information technology (IT) company. We collected 904 messages sent by 343 users. Our results suggest strong acceptance of food messaging as an alternative message channel. Further analysis implies that food messaging embodies characteristics of both text messaging and gifting. It is preferred in close relationships for its evocation of positive emotions. As the first field study on edible social messaging, our empirical findings provide valuable insights into the uniqueness of food as a message carrier and its capabilities to promote greater social bonding.

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