Improving User Attention of Terms and Policies: NLP-based Highlighting and a User Study
- Title
- Improving User Attention of Terms and Policies: NLP-based Highlighting and a User Study
- Author(s)
- Junsu Lim
- DGIST Authors
- Junsu Lim ; Sunjun Kim ; Donghoon Shin
- Advisor
- 김선준
- Co-Advisor(s)
- Donghoon Shin
- Issued Date
- 2022
- Awarded Date
- 2022/02
- Type
- Thesis
- Description
- Human-Computer Interaction, Usable Privacy, Natural Language Processing, Crowdsourcing
- Table Of Contents
-
I. Introduction 1
1.1 Research Problem and Approach 1
1.2 Abbreviation Definition 4
1.3 Contributions 4
1.4 Thesis Structure 5
II. Background 7
2.1 Terms of Service & Privacy Policy 7
2.2 Crowdsourcing 8
2.2.1 Amazon Mechanical Turk 9
2.3 Natural Language Processing 10
2.4 Evaluation Metrics 11
2.4.1 Confusion Matrix 11
2.4.2 ROC Curve 13
2.4.3 Precision Recall Curve 14
2.5 Human-Computer Interaction Security 15
III. Methodology 17
3.1 Qualitative Research 17
3.1.1 Grounded Theory 17
3.2 Quantitative Research 18
3.2.1 Likert Scale 18
3.2.2 Cronbach’s Alpha 19
3.2.3 Shapiro-Wilk Test 20
3.2.4 Paired Samples t-Test 20
3.2.5 Wilcoxon Signed-rank Test 21
IV. Research Design 23
4.1 Research Question 23
4.1.1 Solution 23
V. Implementation 24
5.1 Data Collection 24
5.1.1 Crawler 24
5.1.2 Data Preprocessing for Crowdsourcing 24
5.2 Crowdsourcing 27
5.2.1 Task Design 29
5.2.2 Data Preprocessing for Natural Language Processing 30
5.3 Natural Language Processing 35
5.3.1 Bidirectional Encoder Representations from Transformers 35
5.3.2 Classification Model Evaluation 39
5.3.3 Dataset for User-study 40
5.4 Test Webpage 41
VI. Pre-study 43
6.1 Participants and Experiment Design 43
6.2 Apparatus 44
6.2.1 Interview Design 46
6.3 Result Analysis 47
VII. User-study 52
7.1 Participants and Experiment Design 52
7.1.1 Procedure 53
7.1.2 Apparatus 54
7.1.3 Interview Design 55
7.1.4 Questionnaire Design 56
7.2 Result Analysis 57
7.2.1 Qualitative Result Analysis 58
7.2.2 Quantitative Result Analysis 60
VIII. Discussion and Conclusion 67
8.1 Discussion 67
8.2 Conclusion 69
IX. Appendix 71
9.1 Demographic Survey 71
9.2 Interview Questions 71
9.2.1 Pre-study Interview Questions 71
9.2.2 User-study Interview Questions 72
9.3 User-study Questionnaires 73
9.4 Github Link 75
9.5 Critical Values of the Wilcoxon Signed Ranks Test 75
References 77
- URI
-
http://dgist.dcollection.net/common/orgView/200000594728
http://hdl.handle.net/20.500.11750/16291
- Degree
- Master
- Department
- Information and Communication Engineering
- Publisher
- DGIST
- Related Researcher
-
-
Kim, Sunjun
- Research Interests Human Computer Interaction; Text Entry; Touch screen; Pointing
-
- Files in This Item:
-
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- Appears in Collections:
- Department of Electrical Engineering and Computer Science Theses Master
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