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1 NAIST-IS-MT

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3 LINE Twitter Web, NAIST-IS-MT , i

4 LINE % 20 % 5 72 %,,,, ii

5 A Method for Estimating Appropriate Interruption Timings using Recipient s Context Yuma AKAIKE Abstract Recently, the spread of smartphones and mobile network are promoting a real time communication such as LINE and Twitter. Not only these applications but also various applications such as a whether information or social network services have a function of push notification. As a result, we receive many notifications from various applications. These notifications are useful for recognizing the up-todate information, but frequent interruption can cause a decrease in concentration as well as stress. The goal of this thesis is to estimate an appropriate timing of interruption for notifying something to the recipient. In order to achieve it, We try to recognize a human activity via a smartphone. After showing its feasibility, we try to create a model for estimating a future response time by analyzing the recipient s contexts. In addition, we try to create an another model for estimating a recipient s busyness. At first, we collect the human activity log and the text history of smart-phone applications. Then, we investigate whether those data can be used to infer recipient s context. As a result, we found the recipient s activity inference can be estimated to some extent from those data. Master s Thesis, Department of Information Science, Graduate School of Information Science, Nara Institute of Science and Technology, NAIST-IS-MT , March 12, iii

6 Based on the result of investigation, we conducted the experiment in real environment by using LINE. Various tasks are sent to 9 subjects in various timings. All the subjects did not know anything about the experiment in advance. We measured the response time for each task. Finally, I ask every subject about the actual context when they received a message. Their contexts were recorded by a life logging application Moves. By using these information, We created two models, response time estimation model and busyness estimation model, by using machine learning. As a result, we confirmed that our proposed models can estimate a response time over 50 % accuracy in the case we classify the response time into 3 level ( within 5 minutes, within 60 minutes and over 60 minutes ) and our proposed models can estimate a recipient s busyness level with over 70 % accuracy also in the case I classify it into 5 levels. Keywords: Interruption, Life-log, Data-mining, User-context, Machine learning iv

7 v

8 vi

9 A B A B vii

10 1. LINE 1 Twitter 2 PC [1] Garrett Danziger [2]. Cutrell [3] [4]

11 [5][6] GPS 1 Web 2

12 2 Fitibit Zip Line Twitter LINE Twitter LINE LINE

13 Moves 259 SVM RandomForest J48 [7] 46.3 F

14 2.. Garrett Danziger argue [2] Cutrell [3] Fischer [8] PC Avrahami Hudson PC [9] 5

15 Horwitz [10] PC Ter Hofte [11]. [12] [13] model-based produres [14] Web [15] 6

16 Pielot [16] Veljko Happy Sad Bored [17] 1 Veljko 1: 7

17 Gmail Mail SNS SNS 140 Twitter Facebook messenger

18 [18] 9

19 Fitbit Zip SNS LINE Twitter Fitbit Zip LINE Twitter SNS Facebook Gmail Facebook Gmail 1 10

20 Twitter API 5 Python 5 LINE Twitter API.txt.txt Python SNS 1. LINE 2. Twitter

21 3.1.2 Fitbit Fitbit Zip Fitbit Zip 5 Fitbit Zip bluetooth Web Fitbit API Fitbit API 5 LINE Twitter Fitbit Zip Python matplotlib LINE Fitbit Zip 2 3 A 12

22 B Fitbit LINE Twitter Log count = (raw data of Log count) (Maximum raw data of Log count) 2 A 3 B LINE Twitter (1)

23 1 0 0 LINE Twitter Twitter LINE Foursquare A 5 B x 5 x y y 6 ja.foursquare.com/ 14

24 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 2: 1 A LINE Twitter

25 10:00 10:30 11:00 11:30 12:00 12:30 13:00 13:30 14:00 14:30 15:00 15:30 16:00 16:30 17:00 17:30 18:00 18:30 19:00 19:30 20:00 20:30 21:00 21:30 22:00 3: 1 B

26 4: 5 A Gmail Evernote 7 Facebook Twitter

27 5: 5 B

28 1 LINE Twitter [19] 19

29 4. 3 SNS LINE 20

30 :00 11:59 12:00 14:59 15:00 17: Moves 8 Moves API Moves

31 Moves Moves 5 Moves Moves 1: 1 Yes or No??? 2??? 3??

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33 1:26 1:12 0:57 0:43 0:28 0:14 0:00 レベル 1 レベル 2 レベル 3 6:

34 1:55 1:40 1:26 1:12 0:57 0:43 0:28 0:14 0:00 朝昼夕方 7:

35 2:24 2:09 1:55 1:40 1:26 1:12 0:57 0:43 0:28 0:14 0:00 とても忙しい 忙しい どちらでもな い 暇 とても暇 8:???? 5 2?? ???? ???? 2??

36 ?? ????

37 1:40 1:26 1:12 0:57 0:43 0:28 朝 昼 夕方 0:14 0:00 忙しい 暇 9: 朝 昼 夕方 1 忙しい 暇 10: 2 28

38 4.3 F SVM RandomForest J AM 9:00 AM 11:59 PM 12:00 PM14:59 PM 15:00 PM 17:59 PM 18:00 PM 21: Moves 2 29

39 2: 30

40 :

41 11: SVM 36.9% J % RandomForest 37.0% SVM 36.9% J % RandomForest 43.0%

42 AM 9:00 AM 11:59 PM 12:00 PM14:59 PM 15:00 PM 17:59 PM 18:00 PM 21: Moves 2 5 SVM RandomForest J48 SVM 42.2% RandomForest 71.8% J % %

43 AM 9:00 AM 11:59 PM 12:00 PM14:59 PM 15:00 PM 17:59 PM 18:00 PM 21: Moves 2 5 SVM 53.3% RandomForest 49.0% J % 13% 3 33% 20 34

44 4: J48 precision 35.7% 47.3% 41.6% 63.8% 50.7% recall 41.3% 47.1% 64.5% 65.1% 50.5% F-measure 37.4% 46.3% 50.6% 63.3% 50.0% SVM precision 31.9% 32.1% 41.6% 65.7% 53.9% recall 43.6% 43.6 % 64.5% 54.1% 53.4% F-measure 36.9% 36.9 % 50.6% 42.2% 53.3% Random precision 36.2% 43.0 % 64.5% 71.4% 48.9% forest recall 38.4% 43.0% 65.7% 72.7% 49.0% F-measure 37.0% 43.0% 64.8% 71.8% 49.0% 35.5% 35.5% 35.5% 22.1% 35.5% 4.6 LINE Moves 1 35

45 % Moves 36

46 Fitbit Zip 5 LINE Twitter 2 30 LINE Moves Yes or No 37

47 % 53.3 % 5 70%

48 1 Twitter LINE API, Gmail Moves Moves Moves 39

49 Moves 40

50 41

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