A Case Study of Tiktok and Bilibili

Home , Online community

Understanding User Behaviors of Creative

Practice on Short Video Sharing Platforms – A

Case Study of TikTok and Bilibili

A thesis submitted to the Graduate School

of the University of Cincinnati in partial fulfillment

of the requirements for the degree of

Master of Design

in the Myron E. Ullman Jr. School of Design

College of Design, Architecture, Art and Planning by

[Qiyang Zhou]

Bachelor of Engineer in South China University of Technology

Committee Chair [Heekyoung Jung, Ph.D.]

Committee Member [Matthew Wizinsky, M.F.A.]

3/28/2019

iv ABSTRACT

Ranging from a few seconds to a few minutes, the short video has become a popular form of learning and sharing creative skills such as drawing, photography, and crafting.

Short videos in social media platforms are reshaping the experience of learning creative skills by providing visually rich instructional materials and communication features to question and comment on those materials. These functions and features of a video platform can impact a user's learning experience, and this aspect has been under- investigated. This study is motivated to investigate user behaviors in short video sharing platforms and identify any gap between user expectations and behaviors afforded by those platforms for creative practice. This study focused on analyzing TikTok (i.e., a short video platform) and Bilibili (i.e., a video sharing platform), specifically 1) their information architecture and user interfaces, 2) viewers' comments on selected drawing skill sharing videos in both platforms (which resulted in four themes of viewer activities and three types of viewer attitudes in practicing and learning creative skills), and 3) selected TikTok users’ online activities and expectations for creative practice based on profiles and in-depth interview. The multi-dimensional data about user behaviors and expectations are synthesized into five different personas and their specific user journey maps, leading to the discussion of design recommendations to support creative practice in short video sharing platforms.

ACKNOWLEDGEMENTS

I would first like to thank my thesis chair Heekyoung Jung, without whom I will never be able to finish this thesis. Heekyoung consistently steered me in correct directions during the past seven months, with her professional points of view and her talents. I am, and I will be thankful to her for the most challengeable and fruitful time that I spent at Daap. I would also like to thank my thesis committee member, Matthew Wizinsky. He always led me to explore more possibilities through the whole process of my thesis development.

Matt’s enthusiasm towards design and academics inspired me a lot.

I would also like to acknowledge my support group. My deep thanks to Han Shen, Xiangyi

Duan, Hui Yin, and Jiayang Zhang for bringing me so many supports and care. They supported me from giving me advice of my thesis, teaching me of data analysis knowledge, to even developing a python software for me to collect user data. My accomplishment would not have been possible without them. Thank you!

I would also like to thank my classmates at Daap and my colleagues at Live Well

Collaborative. Many thanks to Adriana Navarro Sainz, Longwei Li, and Shuai Mu for numerous supports in the thesis year.

Finally, I must express my very profound gratitude to my parents, to my aunts and uncles, and to my grandparents for providing me supports and encouragements all the time. I want to especially thank my grandmother, Shixian Huang. I hope you would be proud of me.

vii

viii TABLE OF CONTENTS

ABSTRACT v

ACKNOWLEDGEMENTS vii

TABLE OF CONTENTS ix

LIST OF TABLES xi

LIST OF FIGURES xii

CHAPTER 1. Introduction 1 1.1 Background 1 1.2 Problem Statement 2

CHAPTER 2. Literature Review 3 2.1 Knowledge Sharing on Web 2.0 and Social Media 3 2.2 Short Videos for Sharing Creative Skills 4 2.3 Popular Short Video Integrated Platforms 5 2.3.1 TikTok 6 2.3.2 Bilibili 7 2.3.3 Instagram 7 2.4 Knowledge Sharing in Short Video Sharing Platforms 8

CHAPTER 3. Research Statement and Methods 10 3.1 Design Analysis of Selected Platforms 11 3.1.1 Information Architecture (IA) Analysis 11 3.1.2 User Interface (UI) Analysis 12 3.2 Comment Analysis 12 3.3 User Profile and Activity Analysis 15 3.3.1 User Behavior Patterns 18 3.3.2 User Groups Based on User Data Patterns 19 3.4 In-depth Interviews (with TikTok Users) 23

CHAPTER 4. Research Findings 26 4.1 Findings from Design Analysis: Platform Affordances 26 4.2 Findings from Comment Analysis 34 4.2.1 Four Categories of Comments 34 4.2.2 Three Attitudes of Commenters 35 4.3 Findings from User Profile and Activity Analysis 38 4.3.1 User Behavior Patterns and User Groups in Bilibili 38 4.3.2 User Behavior Patterns and User Groups in TikTok 49 4.3.3 Comparison of User Behavior in Bilibili and TikTok 56 4.4 Findings from In-Depth Interviews: User Expectations 56

ix CHAPTER 5. Synthesis of Findings: User Personas and Journey Maps 59 5.1 User Group 1: Content Browser 60 5.2 User Group 2: Learner Creator 63 5.3 User Group 3: Fan Art Creator 66 5.4 User Group 4: Recognition-Seeking Creator 69 5.5 User Group 5: Influential Creator 72

CHAPTER 6. Design Implications and Discussion 75 6.1 Encourage Creative Practice Through Social Interaction 78 6.2 Preview Video Content and Organize Comments 79 6.3 Support for Planning, Tracking, & Evaluating Creative Practice 79 6.4 Nurture Communities for Collaborative Creative Practice 80 6.5 Adaptive Recommendation for Personalized Creative Practice 81

CHAPTER 7. CONCLUSION 83

REFERENCES 87

APPENDIX A 94

APPENDIX B 95

APPENDIX C 101

APPENDIX D 104

APPENDIX E 105

APPENDIX F 108

APPENDIX G 113

x LIST OF TABLES

Table 1. Summary of selected videos 13

Table 2. The codes of the data of user’s behaviors. 18

Table 3. Users' data category on Bilibili 20

Table 4. Users' data category on TikTok 22

Table 5. The percentage data of the comments categorized by the two classification

schemes. 36

Table 6. The means in each knowledge sharing or learning activity and user’s status on

Bilibili. 42

Table 7. The amount of the quantifies number of all the significant user groups (G)’

behaviors or status. If a value of an item below the mean, it will show the “-”, if it is

above the mean, a “+” will show. 46

Table 8. The means in each knowledge sharing or learning activity and user’s status on

TikTok. 51

Table 9. The amount of the quantifies number of all user groups (G) behaviors or status.

If a value of an item below the mean, it will show the “-”, if it is above the mean, a

“+” will show. 53

Table 10. Integrated comment classifications for evaluating the percentage of the

comments under each selected video on TikTok and Bilibili 95

Table 11. Word count in comments under each selected video on TikTok and Bilibili 98

Table 12. Differences of the comment categories on the two platforms 99

xi LIST OF FIGURES

Figure 1. TikTok, 2019. TikTok App Features Introduction 6

https://www.Bilibili.com/. Screenshot by author. 7

from https://www.instagram.com/stories/zeazhou/. Screenshot by author. 8

Figure 1. Screenshots of selected videos on TikTok 14

Figure 5. Screenshots of selected videos on Bilibili 14

Figure 6. Screenshots of selected videos on Instagram 15

Figure 7. Collected public users' data on Bilibili 16

Figure 8. Collected public users' data on TikTok 17

Figure 9. TikTok Interfaces Analysis 27

Figure 10. TikTok Interfaces analysis – Search Page 29

Figure 11. Information Architecture of the bottom sheet of TikTok. 32

Figure 12. Frequency of navigation among different functions. 33

Figure 13. The percentage of comment in each category on posted videos on general

video sharing platforms. 35

Figure 14. Users’ activities, status, or behaviors of knowledge learning and sharing that

showed various statistical correlation on Bilibili. 38

Figure 15. The comparison of two formats of users' creations uploading behaviors -

Shown in the column chart 40

Figure 16. the dendrogram of Bilibili users. 44

xii Figure 17. Users’ activities, status, or behaviors of knowledge learning and sharing that

showed various statistical correlation on TikTok. 49

Figure 18. The comparison of the number of all videos that user liked and drawing

related videos user liked - Shown in the column chart. 51

Figure 19. Dendrogram of TikTok users 52

Figure 20. Persona of the Content Browser 61

Figure 21. Content Browser journey map 62

Figure 22. Persona of the Leaner Creator 64

Figure 23. Leaner Creator journey map 65

Figure 24. Persona of the Fan Art Creator 67

Figure 25. Fan Art Creator journey map 68

Figure 26. Persona of the Recognition-Seeking Creator 70

Figure 27. Recognition-Seeking Creator journey map 71

Figure 28. Persona of the Influential Creator 73

Figure 29. Influential Creator journey map 74

Figure 30. Integration of users’ journeys of all five persona. The phases with same color

indicate they belong to same category. 76

Figure 31. The design interventions that can fit in different phases of users’ journeys. 77

xiii

CHAPTER 1. INTRODUCTION

1.1 Background

With more accessible social media and communication technologies, many knowledge-

related activities, such as learning and sharing, are taking place in online communities,

introducing new forms of collaborative platforms (Dron & Anderson, 2014).

Research showed that to some extent, social media could facilitate knowledge sharing by

providing social interactions, platforms for experience sharing, relationships construction

and networking opportunities, more throughout observations opportunities, and mutual

swift trust. (Panahi et al., 2012). Many justifications have been offered to explain how

social media succeed in promoting knowledge-related activities. One of the justifications

is interpersonal interaction. Close interaction is necessary for knowledge transfer

amongst individuals (Polanyi, 1967). Interpersonal interactions are necessary for efficient

distribution of knowledge in a particular online community or even among different online

communities (Murray & Peyrefitte, 2007). Also, knowledge-related activities in online

communities are always manifested by the presentation of alternative views, and the

production of new ideas (Eteläpelto & Lahti, 2008). All these theories support that

interpersonal interaction and collaboration are important attributes that contribute to the

knowledge-related activities in online learning. Therefore, emerging social media

presents profound potential in enhancing knowledge sharing via collaborative behaviors

where people have similar goals and similar attitudes to obtain the knowledge necessary

to pursue their achievements (Wahlroos, 2010).

A short video platform is used to produce and browse short videos (Newby, 2018). It has

multiple social media features and all the videos on the platform are limited to a short

duration, which ranges from a few seconds to a few minutes (Su, 2018). The platform

emphasizes building a content generating and sharing community.

One of the representative short video platforms is TikTok. TikTok has emerged to be the

most popular social media platform among millennials in China (Patrick, 2018). This

entertainment-oriented platform succeeds in the immersive user experience and

stimulation mechanisms. Knowledge sharing is becoming an important part of all the

content and experience that the platform provides. In the essence of community, this

platform’s immersive user experience and stimulation mechanism have opened

opportunities for a new collaborative, engaging, and effective learning experience for

users.

1.2 Problem Statement

Motivated by the potential of learning experience in short video platforms, this paper

investigates knowledge-related activities occurring in TikTok with a focus on learning,

practicing, and sharing drawing skills.

This study aims to provide insights to enhance users’ experiences of knowledge-related

activities with short videos and in short video sharing platforms by identifying the gap

between user expectations and experience that short video platforms provide.

2 CHAPTER 2. LITERATURE REVIEW

2.1 Knowledge Sharing on Web 2.0 and Social Media

Web 2.0 encompasses web technologies and services, including blogs, social network

sites, wikis, communication tools, and folksonomies. They all emphasize the sharing of

content among users and online collaboration, which makes Web 2.0 a highly interactive

and dynamic application platform for fielding new kinds of applications (Murugesan,

2007). Popular applications like Twitter, Instagram, Wikipedia, WhatsApp, and YouTube

are all the derivatives of Web 2.0. Web 2.0 enables users to collaboratively create

information and conduct knowledge sharing in the Internet community (Darwish &

Lakhtaria, 2011).

One of the direct applications of Web 2.0 is social media. Social media refers a group of

Internet-based applications that build on the ideological and technological foundations of

Web 2.0, and that allow the creation and exchange of user-generated content (UGC)

(Kaplan & Haenlein, 2010). Social media is also defined as “collaborative online

applications and technologies which enable and encourage participation, conversation,

openness, creation and socialization amongst a community of users.” (Bowley, 2009)

The attributions of collaboration and interaction of social media open the opportunities

for people to share knowledge on the Internet. People tend to hold a positive attitude to

collaborative learning on social media because it will provide a more interactive

experience and more motivation during their involvement of knowledge-related activities

3 on social media (Manca & Ranieri, 2016; Mao, 2014). The common knowledge-related behaviors happening on social media can be summarized as follows:

• Content creation: On social media, users are not only passively obtaining

information. They are emerging to actively produce User Generated Content

(UGC). The co-creation of UGC is one of the main characteristics of social media

(Bowley,2009). Users can contribute in creating, editing, commenting,

annotating, evaluating, and distributing original contents in social media space

(Lerman, 2007).

• Peer Communication: Communication is essential for knowledge sharing

(Gordeyeva, 2010). Social media has provided an effective platform for social

interactions and real-time communications among users in the forms of text

chatting, and video and voice calling.

2.2 Short Videos for Sharing Creative Skills

Depending on the types of applications, certain forms of media will be shared and exchanged on social media. YouTube is for video sharing; Instagram is for picture and video sharing; Twitter is mainly for text message sharing. New platforms where users can exchange new kinds of media are developed. Short video has become a popular communication medium, shared in short video platforms.

Video as a media carrier of information, has been broadly used in knowledge sharing from social media to Massive Online Open Class (MOOC) platforms. Currently there are two types of video forms that facilitate different ways of knowledge sharing. Lecture videos usually present conceptual (declarative) knowledge, whereas tutorials present how-to (procedural) knowledge (Guo et.al, 2014). Multiple research has shown that

4 viewers preferred to watch the knowledge sharing video which is with a short duration.

Guo et.al (2014) found that viewers are more engaged with videos ranging from zero to three minutes on MOOC platform. Study shows that for the lecture video which introduces discrete mathematics, videos lasting less than 5 minutes provide better knowledge- obtaining experiences by improving students’ learning attitude, learning effectiveness, and learning engagement (Hsin, 2013). Research has showed that, learning with shorter videos will significantly encourage task-relevant activities and reduce task-irrelevant activities of students (Szpunar, 2013). Study also revealed when people are obtaining knowledge through short videos, they would prefer watching skills and experience sharing knowledge rather than conceptual knowledge.

2.3 Popular Short Video Integrated Platforms

Short video platforms have emerged to be the most popular social media among millennials in China (Patrick, 2018) with representative applications including TikTok1,

Tencent Weishi2, and Kwai3. Most of the short video platforms are mobile applications, where users can create, edit, share, and view short videos. The videos have a standardized short duration ranging from few seconds to few minutes. Research shows that the relative convenience of content generation, rapid content transmission, and emphasis on sociality are the distinct attributes of a short video platform (Zhao, 2015).

1 https://www.tiktok.com/en 2 https://weishi.com/ 3 https://www.kwai.com/

5 2.3.1 TikTok

In China, TikTok is already one of the fastest growing apps and the most popular music

video community (PR Newswire, 2018). It is especially popular among Internet users

under the age of 30 in China (Patrick, 2018). Beyond China, it has also become a

phenomenon in North America as well as multiple other Asian markets like South Korea,

Japan, and Thailand (PR Newswire, 2018). In June 2018, TikTok announced that the

number of its monthly active users in mainland China have hit 300 million, and the number

of its monthly active users worldwide reached to 500 million (Jon, 2018). The videos on

TikTok can range from 15 seconds to 60 seconds. Figure 1 shows the screenshots of

TikTok app.

Figure 1. TikTok, 2019. TikTok App Features Introduction

2.3.2 Bilibili

Bilibili is a popular video-sharing platform based in China, which is derived from a

Japanese video-sharing website, Niconico4. It emphasizes UGC and social activities.

There is no limitation of video duration on Bilibili. The videos on Bilibili can range from few

seconds to few hours. Figure 2 shows the screenshots of Bilibili website.

2.3.3 Instagram

Besides short video platforms, some of the other social media platforms integrate short

video as one of the UGC sharing media forms. Instagram has released the “Instagram

story” feature (Figure 3), which is a personal feed of photos and videos within Instagram

4 https://www.nicovideo.jp/

7 and can only exist for 24 hours (Instagram, Inc, 2010). The duration of each story is limited to 15 seconds. The feature has reached a great success. Now it has reached more than 400 million active users (GuruFocus.com, 2018). Also, the duration of video posts is limited to 60 seconds on Instagram.

2.4 Knowledge Sharing in Short Video Sharing Platforms

Statistics show that knowledge sharing is one of the most popular categories of content on TikTok (CBNData, 2017). The categories of shared knowledge vary from skills sharing

(e.g., creating, experience sharing) to explicit knowledge (e.g., common science) on

TikTok (CBNData, 2017). Su’s study shows that one of the reasons that users would have a positive attitude on TikTok is that they can learn and obtain many skills and knowledge, which users consider to be very beneficial in their daily lives (Su, 2018).

8 Many researchers have identified users’ knowledge-related behaviors on video sharing platforms, analyzed users’ experiences, and offered insights on how to optimize the system of video sharing platforms and videos themselves to improve users’ learning experiences and meet users’ needs. Monserrat et. al found that the ease of access to knowledge in comment section will help viewers understand a big picture of knowledge introduced in the video (Monserrat et al., 2014). Research of Wu et.al has showed that collaboration is crucial for increasing users’ engagement in knowledge sharing. Users on a collaborative video platform are more willing to share their opinions and knowledge than in a traditional forum platform (Wu et al., 2018). These studies have investigated how video sharing on MOOC platform would better facilitate sharing and obtaining of knowledge. However, there is no discussion explaining how user’s knowledge sharing behaviors are supported in short video platforms with short videos, system services, and interaction design.

9 CHAPTER 3. RESEARCH STATEMENT AND METHODS

Short video platforms belong to a social media service that provides users with video content, mostly user generated, in short durations from a few seconds to a few minutes.

Many current video sharing platforms also integrate standardized short videos. With the attributions of interaction and collaboration of social media and the entertaining knowledge sharing and learning experiences of short video, short videos and short video platforms attract a great number of viewers. However, there are still significant gaps remaining when it comes to effective learning from an academic perspective, especially regarding user expectations for learning creative skills vs. user experiences available in short video sharing platforms. This thesis is motivated to understand user behaviors related to learning and practicing creative skills with the following research questions:

• What are user expectations regarding creative practices in short video

sharing platforms?

• What are their actual experiences of learning, practicing, and sharing creative

skills in short video sharing platforms?

• Can we identify any behavior patterns, and further identify any gaps in user

behaviors that are enabled by platforms versus user’s expectations? In

addition, can we design opportunities to bridge the gaps?

This study chose drawing as the specific research topic. The rationale for using the drawing as an example is due to the amount of drawing knowledge sharing videos that have been created. According to the data retrieved on November 1st, 2018, the number of total views of the videos with “drawing” hashtags has been viewed 4.3 billion times on

10 TikTok. The number of the total views of the videos with “drawing” hashtags is 129 million

on Instagram. Also, there are 214 million videos with “Drawing” as a keyword in the

headers on YouTube. The statistics prove that drawing is one of the most popular topics

on video sharing platform.

This study selected the following video platforms as examples: TikTok, Instagram, and

BILIBILI. The rationale of using these three platforms as an example is that they have a

significant number of users. According to the New York Times, over one billion users are

Instagram users while half a billion users are TikTok users all around the world (Zhong,

2018). Bilibili as a Chinese video platform has attracted more than 200 million consumers

in China in 2016 (Wang, 2016). The statistics prove that these three video sharing

platforms are very popular in the market.

3.1 Design Analysis of Selected Platforms

3.1.1 Information Architecture (IA) Analysis

Information architecture (IA), defined by Rosenfeld and Morville (2006), is “the

combination of organization, labeling, search, and navigation systems within websites

and intranets”. It represents the user’s needs and the information they pursuit when they

use the website or application. The construction of IA is also based on the user’s

information-seeking behaviors. This study builds up the information architecture of

TikTok and Bilibili to understand how the platforms intend for their users to post and

respond to videos and how the system structure influence user behaviors and

interactions in the platforms.

11 3.1.2 User Interface (UI) Analysis

This study searched videos under the theme “drawing” on different platforms: TikTok, and

Bilibili, and analyzed interfaces and interactive features that guide users through

interaction steps for completing knowledge-related activities.

3.2 Comment Analysis

Based on the classification scheme for content analyses of YouTube video comments

developed by Madden et al. (2013), this study developed new comments categories

especially for knowledge sharing videos. Comment analysis is intended to illustrate the

important role of comments in facilitating different knowledge-sharing activities on short

video platforms and to understand users’ general learning and sharing patterns.

This study searched the keyword “drawing” and “drawing tutorial” on TikTok. The study

searched the keyword “drawing” in Bilibili. The study searched the hashtag “drawing” in

Instagram. Then the study selected three most popular videos and collected the

comments under the eight videos.

Three videos were selected from TikTok (Figure 4), and 456 comments were collected;

two videos were selected from Bilibili (Figure 5), and 509 comments were collected; three

videos were selected from Instagram (Figure 6), and 284 comments were collected. Table

1 shows the summary of the selected videos.

12 Table 1. Summary of selected videos

Video Platform Collected Video Duration # Comments Content

Date

Video 1 TikTok 10/02/2018 60 seconds 107 Fashion drawing

Video 2 TikTok 10/11/2018 60 seconds 148 Landscape painting

Video 3 TikTok 10/11/2018 60 seconds 201 Eyes drawing

Video 4 Bilibili 10/13/2018 75 seconds 379 Profile drawing

Video 5 Bilibili 10/14/2018 155 seconds 131 Figure drawing

Video 6 Instagram 11/26/2018 60 seconds 117 Blending skills

Video 7 Instagram 11/26/2018 60 seconds 53 Eyes drawing

Video 8 Instagram 11/27/2018 60 seconds 23 Hand drawing

Figure 4. Screenshots of selected videos on TikTok

Figure 5. Screenshots of selected videos on Bilibili

Figure 6. Screenshots of selected videos on Instagram

3.3 User Profile and Activity Analysis

To better understand individual user's activities during knowledge sharing or learning, users’ public profile and activity data was tracked. This study selected the daily popular drawing videos on TikTok as well as on Bilibili and collected public profile and activity data of active users who are selected based on the criteria identified in chapter 4.2.2 through the comment analysis showed in chapter 3.2. This study only collected users’ public data, which can represent their behaviors of knowledge learning and sharing, their status or levels of online activities, and the social interactions they offered or received on the platform. Profile and activity data from 198 users were collected from Bilibili and from

81 users’ activities on TikTok. On Bilibili, 13 kinds of users’ data were collected, which are showed on Figure 7. On TikTok, 8 kinds of users’ data were collected, which are showed on Figure 8.

Figure 7. Collected public users' data on Bilibili

Figure 8. Collected public users' data on TikTok

17 3.3.1 User Behavior Patterns

To understand users’ behavior patterns, the study conducted regression analysis to find

out the relationships of the combinations of each user’s multi-dimensional data. The Table

2 shows the codes of individual user’s behaviors that the study collected from their

profiles.

Table 2. The codes of the data of user’s behaviors.

Activity Code

# Comments per post gained C

# Likes per post gained L

# Views per post gained V

# Post P

# Times per post was shared S

This study applied linear regression to analyze the correlations of different user activities

on Bilibili and TikTok. The tool for correlation calculation is Tableau5, with which the p

5 https://www.tableau.com/

18 value, R2 of each regression can be automatically calculated. This study computed r value

by Excel sheet formula.

Based on the theory of Ronald Aylmer Fisher (1970), p value represents the statistical

significance to reject or accept a hypothesis about the relationship between variables. In

the academic field, people consider when p value < 0.0001, the variables statistically

significantly associated.

The concept of R2 was developed by Steel and Torrie (1960). The value shows how many

independent points meet the regression line. The higher R2 is, the more accurate the

regression line will be.

This study also utilized r value, which is the correlation coefficient, to measure the linear

correlation between two variables. According to Bruce Ratner (2009), an r value between

0.7 and 1.0 indicates that a strong positive linear correlation relationship exists between

the variables. This study identifies all the combinations of behaviors 1) whose r value

show a strong positive linear relationship, 2) the R2 value of the combinations are relatively

high, and 3) the p values of the combinations are < 0.0001. If the combination meets the

three conditions above, it will indicate that the two values in the combination have a

positive linear relationship, which is statistically significant.

3.3.2 User Groups Based on User Data Patterns

This study analyzed seven kinds of user data on Bilibili, which could represent user

status and knowledge-related activities (Table 3) and four kinds of user data on TikTok

(Table 4). To further understand the main groups and their knowledge-related activities,

a dendrogram analysis is used to conduct cluster analysis. Dendrogram is a graphical

19 method used to identify different clusters or groups among a database. It divides groups by different knots which are associated with the groups (Qu et al., 2015). The study chose the mean of each kind of data as a knot of the dendrogram to conduct the cluster analysis. The results of the dendrogram came out with clusters that can be categorized into primary user groups on Bilibili and TikTok. The structure of the dendrogram is based on the frequency of occurrence in each data category.

Table 3. Users' data category on Bilibili

Data Users’ Data Justification

Category

User status User’s N/A on the followers platform

Knowledge- Drawing- In TikTok, “like” is a function of saving and admiring work. consuming related Users can trace back to their “like lists” which contain all the activities. videos that videos they have liked before.

users liked

The ratio of From the design and strategy analysis, users were

the number of encouraged to like others’ work to show their support and

drawing- acknowledge. Like is a behavior that happened very often and

related videos random. And a phenomenon has shown that though some

that a user users liked less drawing related videos than others, most of

like to the their liked videos are drawing knowledge related. Thus, it is

22 number of all not very appropriate to only compare the numbers of videos

videos that a users liked to classify users’ knowledge learning or sharing

user liked behaviors. To understand if users have preferences in the

watching categories, the study used the ratio between the

number of users liked drawing related videos and the number

of all videos that users liked. The data can help to prove if

one’s knowledge sharing/learning intentions are stronger than

another.

Knowledge Uploaded N/A conversion, drawing- creation, related and sharing videos. activities.

3.4 In-depth Interviews (with TikTok Users)

Based on the comment analysis and the user’s profile tracking, this study identified main user groups whose activities are associated with knowledge sharing or learning.

However, several research questions couldn’t be fully answered from the quantitative, meta data: 1) What may be the purposes of each group’s knowledge-related activities?

2) What features or experiences help them achieve or fail to their goals? 3) Why users in each group would choose a particular platform (i.e., TikTok) to conduct related activities?

With these research questions, this study further conducted in-depth interviews to understand each group’s experience in knowledge-related activities, especially their

23 experiences in the whole process. Qualitative research is aimed to interpret, code and present the meanings of detected phenomena with respect to the meaning people bring to them (Northcutt et al., 2014; Denzin & Lincoln, 2011). Qualitative research usually guides the researcher to better generate the insights of the phenomena by: Coming out with interpretive perspectives of participants’ views, and analysis to the social setting and context of the phenomena (Lapan, 2012).

As one of the qualitative research methods, in-depth interview is a useful tool for researchers to understand users’ detailed situations and experiences (Boyce & Neale,

2006). Because the purpose of in-depth interview in this study is to find out different users’ knowledge learning experiences and behaviors, especially their expectations and the real situation they faced, the answers would be complex and personal. Thus, this study took the format of unstructured interview, which is more inter-personal and without too many strict limits during the interview process (Hannabuss, 1996). In-depth interview is normally in-person and processed in the verbal way (DiCicco‐Bloom et al., 2006). However, in this study, users’ geographical locations varied. It’s hard to conduct in-person interviews, so this study thus conducted the interview through direct messages, which is an embedded feature in TikTok. The in-depth interview questions are about the motivations and the experiences of the knowledge-related activities of selected users in each group.

As this study identified previously, 81 users left informational and constructive comments to the selected videos. This study sent out an in-depth interview invitation to each of the

81 users through direct message on TikTok, and 12 users responded. Their ages range from 13 to 25. Participants’ backgrounds range from middle school student, user experience designer, to software developer. Seven out of 12 participants are female.

24 This study conducted thematic analysis of the qualitative interview data. The Thematic analysis required the researcher’s “involvement and interpretation” to codify the raw data of the phenomena researchers have observed before (Guest & Namey, 2012). The results of the process of themes coded is finalized by researcher’s multi-examination of the raw data (Rice & Ezzy, 1999). As Boyatzis defined, the coding process begin with highlighting the worth-noted and important moments that are relevant to the research, while reading the raw data (Boyatzis, 1998). When coding themes, it’s important to make sure each coded theme includes representative amounts and aspects of the phenomena

(Boyatzis, 1998). In this research, we conducted thematic analysis inductively to generate the themes from the raw data to conclude each user group’s knowledge activities intentions and their experiences.

25 CHAPTER 4. RESEARCH FINDINGS

4.1 Findings from Design Analysis: Platform Affordances

Many interactions happened in the TikTok app. Figure 9 shows the main features and

functions that enable interactions. The home screen of the app includes:

1. A full-width, full-height short video as background. Once the current video finishes

playing, the page will automatically scroll up and present the next video. Users are

also able to switch to previous or next videos by swiping down or up on the screen.

2. Information about the video, which includes the name of the video maker, video

description and name of background music.

3. Reactive and interactive buttons, including likes, comments and share. The

number under the button showcases times of the related action that the video

viewers have taken toward the video.

Figure 9. TikTok Interfaces Analysis

27 Users can view others’ comments while a video is playing at the background. Users can show their appreciation or agreement to certain comment by clicking the like icon following the comment text. Another way by which users can interact with each other is to reply to the comment and start a conversation in public or through private messages.

With the share button, users can choose to share the videos to other platforms and applications, repost the video or react to the video.

1. Repost video: By this action, users can post the video for a second or further

time. Users can add their own captions with the reposted video.

2. Duet: Users can create a new video, which will be placed right next to the original

one.

3. React: Users can create a new video, which shows in a small window adhere to

the previous one.

By clicking the "Like" button, users will save the video in their publicized personal list.

The more likes a video has, the more exposure it will get. That is because the algorithms of TikTok will rank the videos with most likes to user’s feeds and main pages.

Another way of supporting knowledge sharing is the message feature. By which users can send private messages to the video makers or people who left comments to share their thoughts and views.

Figure 10. TikTok Interfaces analysis – Search Page

In the search page, showing in the Figure 10, there are trending topic section and trending hashtag section. Each of these hashtags creates a little video community that enables users to browse and create the videos with same or similar topics. Users can follow the hashtags in order to constantly receive the new feeds of the related video posts.

TikTok is encouraging users to create new videos associated with the original one.

Compared to Instagram and Bilibili, TikTok enables more interactive features and ways for users to co-create and share videos and knowledge.

29 From the information architecture shown in Figure 11, we can see that TikTok is a vertical integrated application. Defined by Turk, vertical integrated platform means the application or website that provides and controls many different components or services on the same site, ensuring users are retained in their existing and future ecosystems

(Turk, 2015). For example, users can choose background music of the videos they want to post within TikTok. Whereas, many traditional video sharing platforms are more horizontally integrated. User needs are fulfilled by different applications on various platforms. For example, users need to go to other music platforms to download background music and remix it in the videos they want to post on Bilibili, which is a typical horizontal integrated video sharing platform.

There are five menus on the bottom navigation bar of TikTok: Home, Following,

Uploading, Message, and Me. They will lead users to the top-level navigation destination.

On the Home page, recommended short videos will be playing repeatedly in the full- screen. Users can watch the next or previous video by swiping up or down. The music name shown on the screen leads users to the main page of the background music that the video uses. The uploader’s headshot links to his/her profile. The other functions are comment, share, and like the video.

There are many features on the current platform that facilitate collaborative knowledge learning because the platform emphasizes rapid interaction and reaction with videos or other users. At the strategy side, the short video platform emphasizes the activities and

30 community, which encourage users to deeply get involved in the collaborative creating on the short video platform.

This study further categorized the contents in the information architecture. The results are shown in Figure 12. This study identified that the contents on TikTok are aimed to fulfill three functions: social interaction, videos viewing, and video creation. It is obvious that, on TikTok, users can easily navigate through contents that facilitate different functions.

For example, the contents on the homepage facilitate video viewing. The buttons and information on the homepage are designed to lead the users to the video uploading page that promotes video creations.

Figure 11. Information Architecture of the bottom sheet of TikTok.

Figure 12. Frequency of navigation among different functions.

This study further calculated how frequent that one function redirected the users to another. The result shows that with TikTok’s strategy, most of the functions through the application lead users from video viewing to conduct social activities and video creations. The three elements integrated into an ecosystem for usage.

On the basis of the interface analysis and IA, this study finds that the knowledge-related activities are highly depended on the three elements of TikTok. They are video viewing, video creating, and social activities. The two core elements of TikTok are video viewing and creating, they respectively support knowledge obtaining and knowledge production activities. Social activities accelerate and intermediate the transition of the two core elements. Social activities facilitate knowledge collaborative learning, because the platform emphasizes them by providing users with rapid interactions and reactions to videos or other users. It encourages users to deeply get involved in the collaborative learning on the short video platform.

33 4.2 Findings from Comment Analysis

4.2.1 Four Categories of Comments

This study found that as one of the most prevalent carriers with which many social

activities and interactions would happen, comment, expressing users’ points of views and

reflecting users’ knowledge-related behaviors, would help this research to identify users’

activities and associated intentions on short video platform. Madden et al. (2013)

developed a comment classification which includes ten main categories of YouTube

comments. The categories reflect users’ different use of comment and their intentions of

commenting in most of the scenarios. Based on this study, this study came out with a

comment characteristic classification which are specifically related to creative practice

sharing in short video integrated platforms. The classification includes four main

categories of comments: Information, Opinion, General Conversation, and Feedback.

The detailed information is listed in appendix A.

34 The percentage of comment in each category on posted videos on general video sharing platforms.

24.68%

45.56%

9.32%

24.07%

Information Feedback Opinion General Conversation

Figure 13. The percentage of comment in each category on posted videos on general video sharing platforms.

As Figure 13 shows, in most circumstances, compared with the other three categories,

feedback type comment has the less comments in every video. General conversation

type comment is the most prevalent comment style on video-sharing platform. More

comments are intending to start conversations with peers.

4.2.2 Three Attitudes of Commenters

In order to identify users’ intentions of viewing creative practice video, this study looked

at the attitudes to the video content or context expressed in comments. Madden et al.

(2013) categorized YouTube comments into three categories based on their relevance to

the videos:

• Comment related to video content: Comment directly derived from the knowledge

showed in the video.

35 • Comment related to video context: Comment related to the knowledge showing in

the video but not featured in the video.

• General category: Comment that are entirely unrelated to video content or context.

Based on these categories, this study further specifies three distinct attitudes of the commenters that reflect their standpoints toward the video. The three attitudes are:

• Constructive and positive comments: Comments related to knowledge shared

through the video content and context the commenters showed interests, learning

intentions and self-organizations to the knowledge and the topic. The activities

always generate questions, hypotheses, and models; testing knowledge for

viability; defending and discussing knowledge. (Fosnot & Perry, 1996; Madden

et.al, 2012)

• Judgmental and negative comments: Comments related to video content or context

but didn’t contribute to knowledge construction, show creative skills sharing

intentions or actions; Comments showing negative interests or not acknowledging

the values towards the shared information.

• Irrelevant comments: Comments unrelated to the video content or context (general

category) and irrelevant to the subject or topic of the video; Comments that are

ambiguous to indicate their learning goals. Incomplete sentences or words.

Table 5. The percentage data of the comments categorized by the two classification schemes.

Total Information Feedback Opinion General Conversation

Constructive, 29% 42.81% 29.94% 14.97% 28.14% Positive

36 Judgmental, 52% 15.56% 0.0% 19.70% 64.40% Negative

Irrelevant to 19% 20.81% 0.9% 54.75% 26.70% the Video

Total 100% 24.68% 9.32% 24.07% 45.46%

Table 5 shows the percentages of different commenting attitudes: Constructive and positive (29%), Judgmental and negative (52%), and Irrelevant: (19%). The data shows that the Judgemental and negative type comment is the most prevalent comment type in short video platforms.

From Table 5, the statistics show that there are 42.81% of all the comments with constructive and positive attitudes under the information type of comments, while 29.94% belongs to the feedback type of comments. The data proved that commenters with constructive and positive attitudes tend to post more information and feedback comments.

There are 64.40% of all the comments with judgmental and negative attitudes under the general conversation type of comments and 54.75% of all the comments with irrelevant commenting attitudes belonging to the opinion type of comments These results showed that commenters with judgmental and negative attitudes tend to make more general conversation type of comments. Commenters with irrelevant attitudes make more opinion type of comments.

37 4.3 Findings from User Profile and Activity Analysis

4.3.1 User Behavior Patterns and User Groups in Bilibili

Figure 14. Users’ activities, status, or behaviors of knowledge learning and sharing that showed various statistical correlation on Bilibili.

Figure 14 shows that the number of a user’s video posts and the number of the user’s image

posts present a strong positive linear relationship, which is statically significant as the R2 of the

combination is 0.57, implying that they are highly associated with each other. This reveals that

38 users tended to utilize all available and appropriate media forms to create corresponding formats of their drawing work on Bilibili. If users have utilized either the form of video or image to upload work, they would be likely to use the other forms to upload their work.

Figure 15. The comparison of two formats of users' creations uploading behaviors - Shown in the column chart

40 Although video and image uploading behaviors present a linear association, from the Figure 15, it's still evident that most users will tend to upload images of their work rather than videos. Among the tracked users, the total amount of uploaded images is 1,257, significantly exceeding the number of uploaded videos, which is 193. The vast difference may be because sharing drawings by uploading images is comparatively easier than uploading videos on Bilibili. The convenience of sharing work is crucial for users to make knowledge sharing decisions.

The number of each user’s uploaded video posts, likes received per video (coded as L/P), comments received per video (coded as C/P), views received per video (coded as V/P) also show multiple relationships. Any two of L/P, C/P, and V/P present a statistically significant positive association, especially for the L/P and V/P. The R2 for L/P and V/P is 0.92, which means in 92% of data points fit the linear association generated by Tableau. The more times a post is viewed, the more possible that it will have more likes. It indicates that users will tend to show their appreciation to a video post through different integrative ways after viewing it. However, significant moderate linear associations were detected from the combinations of the number of users’ uploaded video posts and L/P, C/P, and V/P. This finding indicates that the video uploading behavior is more likely not to have a linear relationship with L/P, C/P, V/P. On Bilibili, productive users will not receive equal prolific recognition.

The number of each user’s uploaded image posts, likes received per image (coded as L/P), comments received per image (coded as C/P), views received per image (coded as V/P) showed some different relationships. Only C/P and L/P presented a statistically significant positive association. Although V/P presents a positive linear association with C/P and L/P, the R2 between each combination is all less than 0.2, representing an insignificant linear association. The results indicated that compared to video post, it is most probable that a user’s image posts cannot receive corresponding viewers’ appreciation or acknowledgment presented in the way of "like" or

"comment" on Bilibili.

41 Table 3 shows the three categories of users’ data and also the justifications of each category. on

Bilibili. Every user on Bilibili has their membership level shown in a scale from 1 to 6. There are

146 out of 198 users having followers in the sample data. Based on this information, it could be concluded that user status, including user membership level and users’ followers, is one of the most comprehensive measures of user activities in the platform. Thus, it is on the top hierarchy of the dendrogram. The number of users who have saved work reached 128, while the number of tags that they follow reached 45. Accordingly, the knowledge consuming activities ranked second in the hierarchy of the dendrogram. Users who have the experiences of paying others’ work with Bilibili coin reached 81. This group of users ranked third in the hierarchy of the dendrogram. Finally, the number of users who have uploaded images is 56, the number of users who have uploaded videos is 27. The creation of images or videos is relatively a less frequent activity. Thus, they ranked fourth in the hierarchy of the dendrogram.

Table 6. The means in each knowledge sharing or learning activity and user’s status on Bilibili.

Behavior Mean

User’s membership level 4

# User’s followers 6

# User’s saved work 10

# User’s following tags 0

# User’s paid work in 30 days 0

# User’s video creations 0

42 # User’s image creations 0

Figure 16. the dendrogram of Bilibili users.

44 Table 6 presents the mean frequencies of each activity occurring for the selected users in

Bilibili, which are the knots of the dendrogram shown in the Figure 16. If the number of users’ certain activity exceeds the mean, the user will be on the left branch. If the amount of a user's certain activity is less or equal to the mean, the user is placed on the right branch. The final cluster situation is shown in Figure 16. There were 52 different final clusters. The users in each cluster ranges from 1 to 29 while the mean is 3. This study thus identifies the clusters which contain more than 3 users, which are significant user groups to further analyze the user's activities, intentions, and experiences associated with knowledge learning or sharing. As the result, 15 groups are identified as the significant user group. They are coded as “Group 1” to

“Group 15” in the Figure 16.

The dendrogram of the user's behaviors shows that there are three major groups whose activities are highly related to knowledge sharing and learning: 1) Content Browser group, 2) Learner

Creator group, and 3) Creator group. Each group presents a distinct user model in knowledge- related activities.

From the dendrogram, it's obvious that the number of users above membership level 4 is much fewer than the number of users whose membership levels are below 4 or equal to 4. It indicates the membership level mechanism on Bilibili is strict with the increments in a user’s experiences.

45 Table 7. The amount of the quantifies number of all the significant user groups (G)’ behaviors or status. If a value of an item below the mean, it will show the “-”, if it is above the mean, a “+” will show.

G1 G2 G3 G4 G5 G6 G7 G8 G9 G10 G11 G12 G13 G14 G15

# Users 9 4 4 4 5 10 5 15 4 18 5 11 8 4 29

User + + + ------Membership

Level

Followers + + - + + + + + ------

# Saved + - - + + + - - + + + + - - - Work

# Paid work - - - + - + - - + + - - + - -

# Saved Tags ------+ - + - - + -

# Images - - - + - - + ------Uploaded

# Videos ------+ ------Uploaded

• Content Browser Group:

The Content Browser groups are highlighted in the orange boxes in Figure 16. User Group

2, 3, 8, 13, and 15 belong to this group. Their activities are shown in the Table 7. It can be

concluded that the user group didn't save or create any work or share any information on

Bilibili. Majority of the group (44 out of 60) have membership levels below or equal to the

mean value of 4. More than half users in the group (33 out of 52) had followers below or

equal to the mean value of 6. Statistically, these results suggested that the Content

Browsers typically tend to meet at least one of the following patterns:

46 o Relatively new to the platform: Users are the member of the platform for from 1

day to 3 years, compared with the rest of the users, who were the member of the

platform from 3 to 8 years.

o Users have been less recognized by others. Fischer & Reuber (2011) indicated

that users will receive recognized values through their creation and interaction on

social media. This group of users presents no public creation and limited

community construction, which can be seen through their membership level.

o Users tend to have fewer social interactions with other users, which can be proved

by their status data on the platform.

Besides these patterns, some general conclusions can be drawn to identify this group

from the data. All users in the Content Browser group does not show intentional

searching or watching video activities for knowledge consumption or learning. They

get involved in limited activities like viewing videos, following other users, commenting,

and liking work, which require minimum commitments. In User Group 13, the activity

of paying work was detected, which indicates that some of them will tend to reward

the video uploaders. This phenomenon can be recognized as another interaction by

which they are involved in the community. Their status levels are random. No observed

association can be detected between users' status and their behaviors. To further

understand these groups’ experience, this study will later select typical users for the

in-depth interview.

• Learner Creator Group:

The learner Creator clusters are highlighted in the yellow boxes in Figure 16. User Groups

1, 5, 6, 9, 10, 11, 12, and 14 belong to this group. Their activities are shown in the Table

7. The commonality of the nine clusters can be categorized into the typical patterns of

Learner Creator groups' identities.

47 o Individuals have knowledge learning goals and interests on some topics. In seven

out of nine clusters, users had more than ten (mean value) pieces of work saved.

Some users only followed certain tags instead of saving work actively.

o Individuals would like to share valuable knowledge and information with others but

with no intention of work creation. They are more willing to self-disclose to share

knowledge through comments than through their creation of work.

• Creator Group:

The Creator clusters are highlighted in the green boxes in Figure 16. User Group 4 and 7

belong to this group. Their activities are shown in the Table 7. Individuals in this group

have clear knowledge learning and sharing goals. They are actively in knowledge

conversion, creation, and sharing. In this group, users' knowledge learning activities may

vary, but they all have experiences of uploading work on the platform.

Based on the three groups in Bilibili identified in the list below, this study further specifies their behaviors, experiences, and expectations with in-depth interviews data.

• Group 1. Content Browser. (30%: 60/198)

• Group 2. Learner Creator. (33%: 66/198)

• Group 3. Creator. (5%: 9/198)

48 4.3.2 User Behavior Patterns and User Groups in TikTok

Figure 17. Users’ activities, status, or behaviors of knowledge learning and sharing that showed various statistical correlation on TikTok.

The results demonstrated that the number of a user's followers, likes received per video (coded

as L/V), comments received per video (coded as C/V), shares received per video (coded as S/V)

show multiple relationships. Any two of L/V, C/V, and S/V present a statistically significant positive

association. A possible explanation of this finding is that the current TikTok platform affords

49 viewers to take more diverse reactions to others’ creations. It is more likely that viewers will reward the video uploader with multiple interactions at the same time.

Another important finding is that the number of a user’s followers presents a significant weak positive relationship with C/V and S/V, as well as offers an insignificant moderate positive relationship. It suggests that the number of a user's followers and the viewers’ reactions to their work are not statistically associated. These relationships may be due to the decentralized video recommendation mechanism of TikTok. This mechanism is different from the traditional centralized social media platform. It pushes to reveal user-generated contents to other users more equally. The algorithm behind this mechanism emphasizes the contents generated by influencers who have more followers and those who have less attention on the platform. The essence of it is to encourage users to create their work. So, to the common users, their work is more likely to receive equal attention and recognition as influencers on TikTok than on other platforms.

This study also highlighted the number of the total videos that users liked and the drawing related videos that users liked presents an insignificant linear positive relationship. A possible explanation for this might be that the knowledge learning goals and behaviors significantly vary between different user groups. The Figure 18 shows that there were different types of users based on the percentage of the drawing-related knowledge-sharing videos they liked in comparison to their total liked videos. Some users only liked very few videos, but most of their likes were drawing related videos. Some users liked lots of videos, but few of them were about drawing.

Figure 18. The comparison of the number of all videos that user liked and drawing related videos user liked - Shown in the column chart

Table 8. The means in each knowledge sharing or learning activity and user’s status on TikTok

Behavior MEAN

# Followers 36

# Drawing related videos that user liked 189

# Drawing related videos that user liked / # all 8.29% videos that user liked

# Drawing related video that user created 0

Figure 19. Dendrogram of TikTok users

52 Table 8 presents the means of each happening activity on TikTok. The means are the knots of the dendrogram shown in Figure 19. The final cluster result is shown in Figure

19. There are 16 different final clusters. The number of users in each cluster ranges from 1 to 17 with the mean value of 3. This study thus considers the clusters which contains more than 3 users as significantly large user groups, to further analyze users’ behavior patterns in these representative groups. As a result, seven different groups were identified and color-coded in figure 19. Their detailed behaviors were shown in the table 9.

Table 9. The amount of the quantifies number of all user groups (G) behaviors or status. If a value of an item below the mean, it will show the “-”, if it is above the mean, a “+” will show

G 1 G 2 G 3 G 4 G 5 G 6 G 7

# Users 16 5 8 7 5 5 17

Followers + + + - - - -

# Liked drawing related videos + + - + + - -

(L)

# Liked drawing related videos / + + - + - + -

# All liked videos (L/A)

# Drawing related video + - - - - - + posts(P)

53 From the Table 9, similar to the results of Bilibili, users on TikTok can be categorized into three types of groups. They are Content Browser group, Learner Creator group, and Creator group. Though the three groups on TikTok have the same title as the ones on Bilibili, but there are multiple differences in their behavior patterns. Each of group presents a distinct behaviors pattern explaining as follows.

• Content Browser Group

The Content Browser groups were marked with orange boxes on Figure 19. User

group 3, 5, and 7 belong to this group. They presented less knowledge

consumption than the average users which can be seen from data L and data

L/A. In User Group 3 and 7, the number of drawing knowledge-related video

each user liked is less than the mean value of 189. Same with the L/A, the ratio

between drawing knowledge-related videos and all the videos users have liked is

below the mean value of 8.29% in User Group 3 and 5. In general, the results of

the L amount can be interpreted that those users are inactive in saving work or

showing support to others’ work on TikTok. The result of the L/A amount further

indicates that users in the two groups relatively didn’t show interest specifically in

drawing knowledge learning and sharing. Also, they never created any work or

shared knowledge and information on TikTok. More users in this group tend to

have less followers than average users, which can indicate that they have fewer

interactions or online relationships with others.

• Learner Creator Group

54 User Groups 2, 4, and Group 6 belong to this group, showing in the Table 9. All

users in the three groups preferred to click the like button on drawing knowledge-

related videos, which is evident from their data of L/A. Each user in Group 6 liked

fewer drawing related videos than others, and the total videos they liked are also

fewer than others. The phenomenon indicates that compared to others, Group 6

has less knowledge consuming activities. However, the group has more

concentrated intention of using TikTok, which is the knowledge learning intention.

Thus, this study categorized this group as Learner Creator group. Users in the

group would like to share value knowledge and information with others, with no

intention of work creation sharing.

• Creator Group

Group 1 was identified as the Creator group. It is interesting that the number of

users in this cluster is significant. It’s the second largest cluster among all seven

groups. Different from the Creator group in Bilibili, this group shows very

consistent learning behaviors while using TikTok, which includes knowledge

consuming, practicing, and sharing. They liked an above average amount of

videos and comparably more videos they liked are related to drawing knowledge.

Each Creator tends to have more followers than the mean of user’s followers.

This echoes to the results of user’s behavior patterns the paper discusses at

chapter 4.1.3.2, that user’s will tend to follow the active work creators and react

with multiple interactive ways to their work.

From the data, the largest group on TikTok is Content Browser group. It is 36.7% of the whole user group. Learner Creator group (20.7%) and Creator group (19.5%) are

55 consistent, presenting similar percentages of the population of users in the whole user

group.

4.3.3 Comparison of User Behavior in Bilibili and TikTok

The findings and analysis showed that there are many differences in the users

commenting behaviors between the two platforms, resulting from their different design

strategies and platform affordances. TikTok users tend to copy, repeat, and imitate

existing comment themes or styles more in the comment section than Bilibili users.

People are more willing to share longer comments on Bilibili than on TikTok. Also, users

are more willing to share opinions in the comment section on TikTok. More detailed

comparisons are included in Appendix B.

4.4 Findings from In-Depth Interviews: User Expectations

This study contacted 81 users based on their public profile analyzed and grouped in

chapter 4.3 and conducted in-depth interviews by direct messages in TikTok with 12 users

who voluntarily responded back to conduct an interview: 2 from the Content Browser

group, 4 from the Learner Creator group, and 6 from the Creator group. The in-depth

interviews provided further insights these main user groups’ experiences of creative

practice on TikTok.

The two participants in the Content Browser group commonly expressed that their life is

busy for every day. They are also very stressed about their high-pressure work. They

have interests in drawing. They think it was relaxing to watch a creative process from start

to finish. They enjoy knowing more knowledge of the drawing skills, which they can use

in socializing. They only watch short videos because the short period can fit in the limited

56 free time of their busy life, so they always watch videos on TikTok. They think TikTok provides an immersive and engaging watching experiences. In average, they hope that there can be content-credibility control system on TikTok as they always doubt the credibility of the content. They hope to have a more efficient way in key information collecting and finding among the content. They also hope to have a friendly community without negative contents.

The four participants in the Learner Creator group are interested in drawing but they never had been systematically educated in drawing. They always watch short videos of drawing skills on TikTok for self-directed learning. They also watch other drawing related videos for inspiration collecting. They think that the video quality on TikTok meets their expectation. They think the significant content and user population on TikTok can provide them with more information and interaction. Also, they believed there would be more chances for them to find the high-quality work with the large quantity of content and user population. They think learning by watching TikTok videos is very efficient. They often apply what they watched and create their own drawings. However, they never post and share because they are afraid of receiving negative evaluation by public. They wish to have reliable evaluation of their work to make further progress in their creative practice.

They wish to have accurate content searching results, personalized recommendations, positive feedback and reactions from others, friendly community, enjoyable ways to build online relationships, and efficient information management systems on TikTok.

The six participants in the Creator group have different intentions, experiences and unmet needs while involving with TikTok. Three out of the six are fans of popular culture (e.g.,

One is a fan of an animation – My Hero Academia; One is a fan of a movie star; Another

57 is a fan of furry fandom). They like creating fan art drawings and sharing with other users on TikTok. They will conduct both creative practice as well as social interactions with other fans on TikTok. Rich and high-quality contents and resources, the presence of interest communities or fan communities, and relatively more efficient learning experience are the reasons for using TikTok. They wish the platform to have control over vicious and negative comments and provide efficient information retrieving system. They have other needs of accurate and detailed search functions and personalized content recommendation mechanisms.

Two out of the six participants from the Creator group are intended to have social recognition and attract people’s attention by sharing work on TikTok. They used TikTok because they thought its decentralized content distribution mechanism can always let their work have great among of people’s recognition. They hope to have some recommendation to let them know how their creation and post can be more popular. They wish to have more users appreciate them.

One out of the six participants from the Creator group is an influencer on TikTok (with more than 9,1000 followers. The data is collected in November 2018). She posts several drawing tutorials every week. She thought that it is easier to have followers and others’ appreciation by posting work on TikTok than on other platforms. She felt good that she can help more people with her tutorials on TikTok. She felt that it was hard to use the short duration to convey concepts. There are still lots of people who can’t understand the tutorials. Few viewers cannot fully master the skills. People would even have some misunderstandings of what she shared. She hopes she could have accurate viewers’ feedback and guidelines for video maker to help her overcome the problems.

58 CHAPTER 5. SYNTHESIS OF FINDINGS: USER PERSONAS AND

JOURNEY MAPS

To have a comprehensive understanding of user activities during knowledge sharing processes, including user intentions and experiences, specifically on TikTok, this chapter synthesized the multi-dimensional user data collected and analyzed in chapter 4 and developed four different types of personas and their according journey map. These two methods help to identify design opportunities to improve user experience of learning and sharing creative practice with short videos on short video platforms.

Defined by Pruitt & Adlin (2006), the persona is a fictitious, specific, concrete representation of target users, representing one group of users who share the similar or same characteristics and behavior patterns. It is based on the user-cantered design principles. A persona is usually generated from the raw user data (Nielsen, 2013; 2011;

2012; 2015;). It’s a method for product design and development, helping researcher to better understand how users would involve themselves with the products and system in different scenarios (Pruitt & Adlin, 2006). This study categorized similar themes of user behaviors and expectations based on the thematic analysis of the interview data and developed five distinct personas. This study also mapped out the user journey maps for each of the persona. User journey map is a diagram that illustrates the steps users take when they are involved in a design or a product (Richardson, 2010). It’s an optimal tool for researchers to synthesize the categories of the poor experiences that users had during their involvements with the products. User journey map is also helpful in identifying design opportunities and planning design interventions for products or services.

59 5.1 User Group 1: Content Browser

Based on the user group classification that this study identified in the previous chapter,

two users from the Content Brower Group shared their experience and expectations in

in-depth interviews. Through the thematic analysis, this study found that the coded

themes of these two users’ data are similar; merged their themes to create a persona:

Content Browser. The user profile and experience are specified in Figure 20 and the user

journey is mapped in Figure 21.

Figure 20. Persona of the Content Browser

Figure 21. Content Browser journey map

62 5.2 User Group 2: Learner Creator

Four users from the Learner Creator Group shared their experience and expectations in

in-depth interviews. Through the thematic analysis, this study found that the coded

themes of these four users’ data are similar; merged their themes to create a persona:

Learner Creator. The user profile and experience are respectively specified in Figure 22

and the user journey is mapped in Figure 23.

Figure 22. Persona of the Leaner Creator

Figure 23. Leaner Creator journey map

65 5.3 User Group 3: Fan Art Creator

Six users from the Creator Group shared their experience and expectations in in-depth

interviews. Through the thematic analysis, this study found that there are slight

differences in the coded themes of these six users’ data. Unlike the previous two

personas, these six Creators’ behaviors patterns and experiences cannot be merged into

one persona although they all actively create and post their drawings. Thus, this study

conducted themes categorization and developed three personas: Fan Art Creator (Figure

24 the persona profile; Figure 25 for the user journey map), Recognition-Seeking Creator

(details in the next section), and Influential Creator (details in the following section).

Figure 24. Persona of the Fan Art Creator

Figure 25. Fan Art Creator journey map

68 5.4 User Group 4: Recognition-Seeking Creator

According to the themes that the research concluded, two users from the Creator Group

are categorized into a persona: Recognition-Seeking Creator, as they have similar goals,

experiences, and behavior patterns on TikTok (Figure 26 for the persona profile; Figure

27 for the user journey map).

Figure 26. Persona of the Recognition-Seeking Creator

Figure 27. Recognition-Seeking Creator journey map

71 5.5 User Group 5: Influential Creator

According to the themes that the research concluded, one user from the Creator group

are categorized into a persona: Influential Creator (Figure 28 for the persona profile;

Figure 29 for the user journey map).

Figure 28. Persona of the Influential Creator

Figure 29. Influential Creator journey map

74 CHAPTER 6. DESIGN IMPLICATIONS AND DISCUSSION

In chapter 4.4, this study categorized different user groups who have distinct intentions, behaviors, and experiences of knowledge sharing and learning on TikTok. The user personas revealed the problems that each persona faces. Additionally, the user journey was mapped for each persona’s different experience phases with corresponding design opportunities. This study re-categorizes and prioritizes the design opportunities in relation to key phases of each persona’s user journey.

By synthesizing all the outcomes from the multi-dimensional user data analysis presented the previous chapters, this study proposes design recommendations for short video platforms to create a smooth and enjoyable learning experience for users. The design recommendations are organized according to the different phases in users’ journeys. The key phases of user journeys across the five personas are integrated and color coded in in Figure 30 and the design recommendations are mapped to specific phases in the integrated journey map in Figure 31.

The recommendations can be categorized into five strategies to fulfill users’ needs of creative practice learning and sharing. They are as follows: 1) Encourage creating, sharing, and responding to content, 2) provide a system to preview video content and organize comments, 3) support for planning, tracking, and evaluation creative practice, 4) nurture communities for collaborative creative practice, and 5) adaptive content recommendation system for personalized creative practice.

Figure 30. Integration of users’ journeys of all five persona. The phases with same color indicate they belong to same category.

Figure 31. The design interventions that can fit in different phases of users’ journeys.

77 6.1 Encourage Creative Practice Through Social Interaction

Rich and updated content is the vital element for user experiences of knowledge sharing

and learning on short video platform. This study came out with four ways to encourage

users to continue creating work and share on short video platforms: Visualize use’s

accomplishment; Update activities and interactive ways for users’ content creations;

Diversify the strategy for rewarding content creation; Aid and motivate users with work

creation. The details of each ways are listed in Appendix C.

As Figure 31 shows, this design strategy can be applied across users’ journeys to improve

their experiences in multiple phases. It should be broadly applied in beforehand, content

watching, interaction and participation, work practicing and creating, and work posting

process. This design strategy should target the problems of user groups with creation

intention. For Learner Creators, the mechanisms should address two of three most

important phases to them, content watching and work practicing and creating phases; For

Fan Art Creators, the mechanisms should address two of three most important phases to

them, content watching and work posting phases; For Recognition-Seeking Creators, the

mechanisms should address three of four most important phases to them, work practicing

and creating, interaction with users and review management, relationship construction,

and relationship maintenance phases; For Influential Creators, the mechanisms should

address all the three most important phases to them, work posting, interaction with users

and review management, and self-brand promoting phases.

78 6.2 Preview Video Content and Organize Comments

The research shows that there are various gaps between users' expectations of video

content and the content the platform provides. This study came out with approaches to

leverage the mechanisms of short video platforms to make the to-be-viewed content

meet the users’ expectations: Provide multiple media formats to describe the videos

more accurately in the preview; Provide scale system for skills level requirement in the

videos. More details of approaches are listed in Appendix D.

From the Figure 31, this study has showed this strategy will highly target the problems of

user groups with knowledge learning intentions, like the Content Browser, the Learner

Creator, and the Fan Art Creator, as well as user group who are with knowledge sharing

intentions, like the Influential Creator group.

6.3 Support for Planning, Tracking, & Evaluating Creative Practice

The research shows that users encounter many problems in multiple learning processes

on short video platforms. This study thus proposed the strategy of supporting for

planning, tracking, and evaluating creative practice: Provide learning evaluation and

situation prediction system for users better understanding their circumstances; Provide

knowledge management system for users better revising and reflecting what they

learned; Provide content credibility verification system; Provide comment categorization

system for users exploring information more accurately. More details of approaches are

listed in Appendix E.

79 From the Figure 31, this strategy is can be applied across users’ journeys to improve their

experiences in multiple phases. The strategies should be broadly applied in content

watching, reaction to videos, related content browsing, and work practicing and creating.

Figure 31 also shows that he recommendation system targets to the experiences of

following three user groups: The Content Browser, the Learner Creator, and the Fan Art

Creator. Comparing the other two groups, these three groups more likely learn knowledge

for self-improvement rather than recognition. For Content Browsers, the mechanisms can

address all the two most important phases to them, content watching and related work

browsing; For Learner Creators, the mechanisms can address all the three most

important phases to them, content watching, reaction to videos, and work practicing and

creating phases; For Fan Art Creators, the mechanisms can address two of three the

most important phases to them, content watching and work posting phases.

6.4 Nurture Communities for Collaborative Creative Practice

This study reveals that Creators value viewer's positive recognition and support. At the

same time, Creators would like to show their support to other Creators and their work.

Thus, the system should consider better peer support and a collaborative community to

encourage users to have deeper involvements and improve users' experiences on

TikTok. The study proposes following approaches that implemented by this strategy:

Visualize and emotionalize support and appreciation to each other and work among

users on TikTok; Encourage peer supports on content creations; Emphasize a friendly

and supportive platform ethics; Diversify ways for relationships and companionships

between users. More details of approaches are listed in Appendix F.

Figure 31 shows that this strategy is universally applied across all current users’ journeys

to improve their experiences on TikTok. The strategy highly promotes interactions, work

creation, and knowledge learning behaviors of users.

6.5 Adaptive Recommendation for Personalized Creative Practice

One of the factors by which TikTok has successfully provided users with enjoyable

experiences is its content recommendation system. However, as the research results

shown in chapter 5, the content recommendation system still needs improvement in

several areas. The study thus proposed the idea of adaptive content recommendation

system for personalized creative practice. The detailed approaches are: Personalized

search for contents or videos; Balance the emergence of repeated content and

recommendation based on user’s preferences. More details of approaches are listed in

Appendix G.

Figure 31 demonstrated that the adaptive content recommendation system should be

implemented in three phases of the users’ journeys: They are beforehand phase, content

watching phase, and the related contents browsing phase. The recommendation system

solution prioritizes the situations that users encounter in content watching phase.

The strategy should be tailored the experiences of following three user groups: The

Content Browser, the Learner Creator, and the Fan Art Creator. Comparing the other two

groups, these three groups are more likely to learn knowledge for self-improvement rather

than recognition. For Content Browsers, the mechanism will address the two most

81 important phases to them, content watching and related work browsing; For Learner

Creators, the mechanisms should address one three most important phases to them, content watching phases; For Fan Art Creator, the mechanisms should attend to one of three most important phases to them, content watching phases.

82 CHAPTER 7. CONCLUSION

This study was motivated to understand users’ knowledge learning and sharing activities in short video sharing platforms and analyzed the information architecture and interface design of TikTok and Bilibili, popular videos of drawing practice selected each platform

(3 from TikTok and 2 from Bilibili), and user comments posted to each video.

From the platform design and video content analysis, this study found that the platform design of TikTok strategically supports different types of social interactions among users with more options for viewers to respond to posted videos and engage with the creators by co-creating and sharing short videos beyond liking or commenting on the original videos.

This study also collected viewers’ public comments to selected videos in each platform

(456 from TikTok and 509 from Bilibili) and analyzed them based on the classification scheme for content analysis of YouTube video comments by Madden et al. (2013) in

2018, developing two new classification schemes: comment characteristics (information, feedback, opinion, general conversation) and commenters’ attitudes (constructive and positive, judgmental and negative, and irrelevant). The study found that TikTok users are more willing to share information and opinion types of comments to knowledge sharing videos, while Bilibili users post more feedback and general conversation types of comments. Also, TikTok users are more likely to copy, repeat, and imitate others’ contents and comments than Bilibili users. This might have been influenced by the design strategy of TikTok that encourages more social interactions among its users [Table 10].

83 Integrating the comment characteristics and the commenter’s attitude classification schemes, the statistics show that users that leave information and feedback types of comments are more likely to show a constructive and positive knowledge learning and sharing attitude. While the commenters engaged in general conversation are more likely to show judgmental and negative attitudes in knowledge learning and sharing [Table 5].

This study also calculated the percentage of each category of commenter’s intentions.

The results shown that 30% commenters showed the constructive and positive knowledge learning or sharing attitudes. Most of the commenters (62%) showed the judgmental and negative attitudes. The results show that generally, less users are actually participating knowledge-related activities while viewing videos of creative practice in these platforms. However, in two out of three collected TikTok videos, there are more comments that show constructive and positive knowledge learning a sharing attitudes rather than judgmental and negative attitudes [Table 10]. Most of the repeated comments are from the commenters with judgmental and negative attitudes.

This study further tracked and analyzed 8 types of public data from 81 TikTok users and

13 types of public data from 198 Bilibili users that can reflect knowledge-related activities, their status in each platform. By the regression analysis, this study discovered interesting findings in the relationships between users’ behaviors patterns. As Bilibili supports creating and uploading multi-media file formats, users who post more videos are more likely to post more images as well. And users would prefer to post their drawing creations in an image format than a video format, since sharing drawings by images is easier. This study thus suggests employing multiple formats that are compatible to the formats of users’ creation and make the uploading processes more convenient and simpler to

84 motivate users to create and share more. This study also found that on both platforms, creators with more active interactions are more likely to receive rewards and reactions from other users. Another result showed that the number of followers a user has and the reactions that a user received are not strongly statically associated. It shows that TikTok is a decentralized UGC community, where users receive reactions and feedback based the quality and quantity of their work, regardless of the number of followers they have.

This study also finds three major user groups through the patterns of users’ data by dendrogram: Content Browser group, Learner Creator group and Creator group. The percentage of Creator group (19.5%) on TikTok is greater than on Bilibili (5%), showing that more TikTok users create and share their work. And based on the data of Content

Browser group, TikTok users would have more interactions and relationship constructed than Bilibili users.

Based on the user group categories synthesized by the quantitative research, this study also conducted in-depth interview with 12 TikTok users through the direct messages and phone call to further understand experience of knowledge learning and sharing through the platform. The results are synthesized into five distinct types of personas and user journey and experience phases for each persona, concluding with five design recommendations: 1) encourage creative practice through more social interactions, 2) provide video previews and organize comments, 3) support for planning, tracking, and evaluating personal learning, 4) nurture communities for collaborative creative practice, and 5) adaptive recommendations for personalized creative practice.

85 In summary, the findings of this study imply the potential of new learning models with short videos and social media platforms. The five personas and their user journey maps will provide a constructive foundation to design new platform services and experiences for collaborative learning of creative skills. The methods used for analyzing and identifying distinct user groups could be applied to other online user experience research in the future.

Still, this study has access to only public user data and it is hard to generalize the findings.

Different kinds of user behavior data, such as comments that a user has made or liked and videos that a user has viewed, could provide more thorough understanding of each user’s trajectory of learning and sharing creative practice. Also, face-to-face interview could lead to in-depth conversations regarding users’ expectations and suggestions for improving their learning experience in short video sharing platforms. Future work can consider collecting more comprehensive data from a larger sample to validate the resulted user groups and their behavior patterns.

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93 APPENDIX A

The comment classification scheme based on the comment characteristic.

Category Definition Instance

Information Comments provide information about something in the video content, video “So, the brows will decide context or a completely unrelated topic. the styles of the eyes.” The contents of the comments are usually explicit information, providing an objective statement or point of view.

Opinion The comment that request or give the “Am I the only one feeling commenter’s points-of-view. opinion it looks like a medicine comment doesn’t encourage actions. It icon?” contains subjective value judgements. It is usually a completed and long sentence. “I use all of them! It General Comment intends to initiate or maintain depends on what I'm Conversation conversations. It contains thanking, going for... My art is greetings, status description, experience @contrition.creativity - If sharing, request of other’s information you wanted to see a mix blending artist.” Comments about learning and Feedback knowledge processing results and “I just got stuck in drawing evaluation. Related to the action that ellipse for the whole directly related to the learning. afternoon. None of them are in good proportion.”

94 APPENDIX B

The analysis of the comparisons of user behavioral patterns and user groups in Bilibili and TikTok.

1. TikTok users tend to copy, repeat, and imitate existing comment themes or

styles more in the comment section than Bilibili users.

On both platforms, users tend to comment with existing punchlines or Internet memes,

which are categorized in this study as "General Conversations". Through the in-depth

interview, users who will comment with punchlines and Internet memes are with little

intentions of knowledge sharing. The main goal of such comments is to engage social

activities or attract people’s attention. The data also showed that 82.6% of general

conversations have judgmental and negative and irrelevant learning attitudes. Users

expressed that they wanted to comment because of their desires for self-expression.

Users consider it as an easy and convenient way to comment with the premade

punchlines or Internet memes under the video. Also, those comments are easily

"liked" by other users, which can help those comments rank on the “Top comments”

section. The likes that users gain help them attract viewers’ attention.

Table 10. Integrated comment classifications for evaluating the percentage of the comments under each selected video on TikTok and Bilibili

Video1 Video2 Video3 Video4 Video5 TikTok Bilibili video

TikTok TikTok TikTok Bilibili Bilibili video on on average

average

95 Constructive and 38.32% 42.57% 20.40% 33.25% 22.90% Positive

Comments 31.80% 30.59%

Judgmental and 29.91% 29.05% 79.10% 49.60% 67.94% Negative

Comments 51.32% 54.31%

Irrelevant 31.78% 28.38% 0.50% 17.15% 9.16% Comments 16.89% 15.10%

# Comments 107 148 201 379 131 456 510

Through the data analysis, this study found that the comments below Video 3 on

TikTok, and the comments below Video 5 on Bilibili are very distinct. On average, the

differentiation of percentage between Constructive and positive comment and

Judgmental and negative comments is around 10%. But in these two videos, the

differences are respectively 58.7% and 45.04%.

This study further analyzed the contents of the comments in each video. On TikTok,

most of the Judgmental and negative comments are the repeated punchlines or

Internet memes. It’s worth-noting that, four users left punchlines and Internet memes

among the first 10 commenters below Video 3, unlike the other two videos. Three out

of the four received the most likes and ranked among the "Top comments" list in the

comment section. Among the 79.1% comments with judgmental and negative learning

attitudes under Video 3, many of them either repeat or use similar words to the three

comments mentioned above.

96 As to video five on Bilibili, the large percentage of judgmental and negative comments is believed to be a social event. In the video, the uploader, who was an influencer on

Bilibili for a long time, spoke for the first time. Thus, many users were showing compliments to the uploader's view, which led to a tremendous amount of general conversations without detected learning goals.

In the comment section of Video 4 and Video 5. Some repeated punchlines and

Internet memes also appeared. But many users expressed their resentfulness to such comments. No repeated punchlines or Internet memes were ranked in the “Top comments” list. Users replied and criticized such comments as meaningless and discouraging. Users also indicated that they had “downvoted” the repeated comments.

“To tell the truth, those people who said "I need skillful hands" in the comment

section are awful. It's already an old joke and everyone knows that. it is without

any value. Nobody will think it's fun anymore. People will only feel very frustrated

when they are seeing that. This is the video introducing the most fundamental

skills…...This is a completely entry-level tutorial, but you still complained that you

couldn’t get it. I mean this is not any master-level video at all. What are you doing

right here if you refuse to learn and practice drawings? The poster was trying to

provide a platform for novice and beginner to learn skills. There is no such place

for you to make these jokes and meaningless words.”

--- Comment from a Bilibili user.

The reasons behind this phenomenon are probably the different features and designs of the two platforms. On TikTok, users can post, like, reply, and repost comments. The

97 comment presents in the infinite scrolling style, which emphasizes quick browsing

behaviors rather than reading or thinking on certain comments. The design is not

friendly for users to relocate the viewed comments. While on Bilibili, users can not only

post, like, unlike, reply, and repost comments but also reply to others’ replies. The

comments present in the page loading style, which encourages users to read through,

respond to, and generate comments. These activities allow users to put more effort to

consider and think through more personalized thoughts and post them as comments.

The results showed that users on TikTok tend to be led by others' comments more

easily. Potentially their learning goals may be more easily biased by others compared

to the users on Bilibili, who tend to be more inspired to express personal thoughts.

2. People are more willing to share longer comments on Bilibili than on TikTok.

Comments on TikTok are shorter and contain less information than Bilibili. Users tend

to comment more on Bilibili than on TikTok.

Table 11. Word count in comments under each selected video on TikTok and Bilibili

Video Video1 Video2 Video3 Video4 Video5 TikTok video on Bilibili video on

Comment TikTok TikTok TikTok Bilibili Bilibili average average

# Word / 7.86 6.35 7.75 13.96 9.76 7.32 12.88 Comment

Max # Word in a 31 40 25 158 61 40 158 Comment

3. Users are more willing to share opinions in the comments on TikTok.

98 The Table 10 shows each learning goal represented by the comments on both

platforms are consistent. It could represent that on TikTok, the structure of learning

attitudes is similar to Bilibili. Half of the users have a judgmental and negative learning

attitude. About a third of users have a constructive and positive learning attitude.

Table 12. Differences of the comment categories on the two platforms

Platform TikTok VIDEOS BILIBILI VIDEOS

Comments categories

Information 30.70% 24.90%

Feedback 4.39% 15.49%

Opinion 26.32% 15.88%

General conversation 39.47% 52.16%

This study further analyzed the characteristics of comments on both platforms shown in

Table 12. The results showed there are more information comments and opinions comments on TikTok, while there were more feedback and general conversation comments on Bilibili. Fewer users on TikTok tended to share feedback of the knowledge in the videos they had watched.

This result suggested that on the current platform there were multiple user groups. Their knowledge sharing intentions, behaviors, attitudes, and experiences may vary. It's essential for this study to understand different groups further and find out the users’ behavior patterns, as well as the following experience situations.

99 From the data collection shown in chapter 4.3.1 and 4.3.2, this study found that the

Learner Creator group is the largest user group on the both platforms. On TikTok, there is a significant percentage of the collected users that belong in the Creator group, account for 19.5% of the collected users. However, it only is 5% of the collected users on Bilibili.

This represents users are more willing to conduct creation behaviors on TikTok rather than on Bilibili. Finally, the percentage of users of Content Browser group among all the collected users is 30% on Bilibili and 20.7% on TikTok. The statistics indicates that there were more users tended to have limited interactions and relationships on Bilibili than on

TikTok.

100 APPENDIX C

The approaches for the Encourage Creating, Sharing, and Responding to Content design strategy in short video platforms:

▪ Data visualization

o The system of TikTok can visualize the user’s accomplishments, which

play a role as reward of the user’s involvements and participation. It would

encourage users to continually produce content.

▪ To the Creator, the system can showcase their viewer’s data as

well as the data of their creation, including viewing amounts,

ranking amounts, etc.

▪ Visualize the increment of the data of the user’s outcome and

recognition. Quantify and visualize the impact that users have

made through the content they created.

▪ Visualize the user’s efforts in supporting other’s work and the

accordingly outcomes

▪ Updated activities and interactive ways for users’ content creations

o From Duet and React, TikTok should consider more easy and fun way for

interactive video re-creation.

o The system can encourage more new content and style of information and

presentation with trendy activities that are, for example, related to current

social events.

101 o The platform can consider specializing or monetizing viewers’ value to

creators’ work. The system can assign more meaning for the “value work”

behaviors, which can emphasize that the “value work” behavior is the

valuable interactions from both sides.

▪ Redesign the strategy for rewarding content creation.

o On current platform, users highly value high quality works, which

negatively impacts the willingness of some users group, like persona II, to

post their original work. So, the system can create activities, challenges,

hashtags or special sections to show the support of user’s creation, by

leading the community to admire and value users’ practice and progresses

in addition to the quality of the drawings. In this way, more users would be

encouraged to create content.

o The platform should provide users with unique ways to express their

appreciation or admiration of creator’s content. The new mechanisms can

be the new reward system on TikTok, to encourage users to create work.

o The system can diversify ways of showing viewer’ s reactions to creators,

such as the viewership of a work, users who have viewed a work, and the

influencers who have viewed the work.

o TikTok can emphasize the comment section and the interaction features

that can collect representative users’ feedback, to better facilitate the

iteration of the creation for creators.

▪ Users can leave comments in the exclusive space so that their

comments will easily to be seen and reacted to.

102 ▪ They strategy can deploy the exclusive conversations opportunities,

by which common users can have conversations with influencers

through work sharing.

▪ Creation preparation and motivation

o The system should facilitate common users to create and post their work

by recommending trending topics that are yet to be populated solely by

famous influencers.

Prediction of results of creation: The system can generate analysis and repost to help creators to understand the potential outcomes of the work they are planning to create and post. For example, the system can inform users how many views their work is going to have based on the captions and hashtags they added. In this way creators can adjust their creations to let the outcomes of their creation closer to their expectation.

103 APPENDIX D

The approaches for the Provide a System to Preview Video Content and Organize

Comments design strategy in short video platforms:

• The system should consider providing guidelines or forms for uploaders to input

key information and key timestamps, for example, requirements of learning: tools

and supplies, in the phase of preparing video posting. The system should also

help users to preview the outcomes of the videos.

• In the video viewing decision phase, the platform design should consider

exploring and applying multiple media - highlighted video clips, text, images, and

graphics interchange format (gif) - adjunct to the video to highlight and present

the preview of the video information.

• Skill Level: The system should consider the co-creation of video content. Based

on the in-depth interview and the comment analysis, incapable creators could

mislead viewers with their descriptions in the preview of the videos. Thus, the

strategy of public editing design is proposed, through which the video brief can

be edited and selected by both uploaders and viewers. In this way, people would

easily avoid being misled by limited information in preview.

• The system should help users Identify information category better, showing the

percentage of the content of personal views or knowledge in the preview to help

users make better viewing decisions.

104 APPENDIX E

The approaches for the Support for Planning, Tracking, and Evaluating Creative

Practice design strategy in short video platforms:

• Learning Evaluation and Situation Prediction System

o TikTok should employ an estimation system for users to predict when and

how their questions in the comment section of the videos to be answered

by analyzing metadata.

o The system should inform users of their current learning stage,

effectiveness, and efficiency. This study suggests that the platform should

provide features to track and report user’s learning behaviors, learning

phases, achievements, and ranking of their achievements in the

community. These strategies are aimed to build users' learning confidence

and help users to keep motivated on the knowledge learning.

o The system should utilize the simple category mechanisms to balance the

ease of videos saving and the ease of videos retrieving.

• Knowledge Management System

o TikTok should have a better system for users to collect inspirational work.

The system should further suggest multi-media forms for inspirations or

knowledge collection of corresponding work.

o The system needs to provide more accessibility for users to find

references and related content to further users’ knowledge.

• Interactive Video Guiding Design

105 o The system can apply interactive reminders of guidance to encourage

users to practice and post work responding to the original one on TikTok,

leading users to learn and practice knowledge and skills step-by-step and

making progress and outcome more visible.

• Comment Feature Redesign

o The system should provide a comment categorization and a key

information finding feature.

o This study suggested that the comment categorization should enable

content viewers to browse specific types of information, such as missing

key information, opinions that share commonalities with other users.

o This study suggested that the comment categorization should enable

creators to check people's feedback and reactions so that they can better

their iteration of their creation in the future.

o The system should provide the question section in the comment section

and collectively show people’s questions.

• Information Credibility

o In the content viewing process, the credibility of the data source always

concerned users who are with constructive and positive knowledge

learning attitudes. Thus, the system should apply credibility validation

algorithms for videos and comments. By assigning credibility weights to

users who have active involvements and creators who produced high-

quality content, the system can build the content credibility system

106 combining with Professional Generated Content (PGC). The PGC content

can bring more authentication to the information on the platform. o The system should provide a credible preview of videos built by uploaders

and public: including descriptions, outcome previews, and skill level

suggestions.

107 APPENDIX F

The approaches for the Nurture Communities for Collaborative Creative Practice design strategy in short video platforms:

• Visualize and emotionalize support and appreciation to each other and work

among users on TikTok.

o The system should show the outcome of the viewer’s support for the work

that they appreciated. For example, after an individual reposting a work,

the system can show the individual how many other users saw the work

through his or her support.

o The system needs to consider emotionalizing and Quantifying how

viewers show recognition for the work. It can give people a simple way to

express thoughts or feelings in a positive, supportive manner to the

creation.

o The system should balance the “ease-of-react” and the “meaning-of-

react”. The “ease-of-react”, like the "Like" button, always results in a very

weak expression of appreciation. It is not capable of showing viewers'

appreciations. However, reactions with more displaying emotions, like

comments and repost, always come with relatively more significant

commitments. Thus, the system needs a balance between the low

commitment while low emotional meaning and the high commitment while

high emotional meaning reaction manner.

108 o The system can diversity the info that shows the information and

engagements of viewers’, like amount of views, information of viewers.

viewing time of each viewers.

• Encourage peer supports on content creations

o The system should emphasize the features that let users know their work

or comments have been viewed by others, which is to give users feedback

on their outcomes and fulfil their senses of accomplishment.

o The platform should provide users with unique ways to express their

appreciation or admiration of creators' content. And there should be the

following reward for users’ involvement of participation:

▪ The system should adopt a smart ranking mechanism for

comments. The comment ranking will be biased and decided by the

credits that the commenters have earned through their involvement

in the system, including high-quality content creation, involvement,

and participation in the community.

▪ The system should be able to show users' "maintenance abilities"

and contributions of the community. If the user shows more support

to other's work and creates more quality content on the platform,

the user will have a higher reputation on the platform showing their

accomplishment to the community construction, which can be

represented by the appealing visual features, such as the virtual

badges, and special visual effects.

• Platform ethics

109 o The system needs to employ a better comment specification to categorize

the vicious, not contributed, and emotionally harmful comments. There

should be a specific personalized section according to users’ preferences

so that users can avoid viewing those comments.

o There should be a strategy that emphasizes the friendly atmosphere of the

community. The design should adopt hints in the text field, notification,

and interaction indicators to lead users to support each other more in an

imperceivably and non-intrusive way.

• Relationship construction

o Group: Through the research results, this study found that users on

TikTok tend to aggregate and join in the specific group based on their

interests and values. Thus, the system should consider a group feature to

better meet users' needs.

▪ The system should promote the group where members could have

intimate relationships with each other on TikTok.

▪ The system should adopt the strategy of building the small groups

or communities, emphasizing the value of knowledge practice,

creation, and application as well as the final outputs, which are

users’ posted work on TikTok. Users can work collaboratively

helping who are in needs of learning and creating. The strategy will

be good for promoting work creation, strengthening learning

engagement, and encouraging outcomes sharing.

o Interpersonal relationship

110 ▪ Relationship with influencers

▪ The strategy can deploy exclusive conversations

opportunities. The common users can have conversations

with influencers through work sharing.

▪ The system should construct influencer-users ecosystem to

encourage influencers to interact more with users. The

common users will have more opportunities to interact with

influencers whom they admired. On the other hand, the

influencers can get more feedback through that ecosystem

that identifies the representative users' opinions and

suggestions.

▪ The mechanisms should keep further decentralize the

content distribution. It should help the creators better identify

users who are eager to have responses. o Features that highlight intimate relationships.

▪ The system should adopt similar user recommendation

mechanisms.

▪ The system should develop a better way for internet relationships

construction on TikTok by inventing ways to help users to know

each other better. For example, the system can set up challenges

that users can finish collaboratively. o Promoting vivid interactions among users

111 ▪ The "Duet" and "React" are proved to be well-accepted features

due to its vivid and fun style for co-creation among users. The

system should adopt more ways and design for co-creation based

on the current designs.

▪ The research has identified that many closed relationships are

constructed through conversation in the direct message. It will be

optimal for the system to support more frequent and enjoyable

conversation for users. TikTok thus can consider offering

suggestions for accessible and friendly conversation starters or

ways.

112 APPENDIX G

The approaches for the Adaptive content recommendation system for personalized creative practice design strategy in short video platforms:

▪ Personalized search for contents or videos: The current search mechanisms

should consider user’s past preferences and experiences, to provide more

detailed filtering options for the users, creating a personalized search based on

user’s specific needs

▪ Repeated content recommendation: The design strategy of current platform

could consider recommending content based on different user groups’

preferences, which are summarized in chapter 5. The content here includes both

videos and the comments on TikTok. The results of the user persona indicate

that persona II, Learner Creator and persona III, Fan Art Creator all hold a

strongly negative attitudes towards the repeated video contents. Also, as the

user behaviors analysis results shown in chapter 4.3, users with constructive and

positive learning attitudes were detected to be annoyed to see the repeated

comments with internet memes, punchlines. The content recommendation

system thus should be more personalized.

o Based on user’s preferences to recommend contents. System should

Identify user groups, like persona II and persona III and avoid pushing

similar contents that they’ve viewed before.

113 o The system needs to further identify video content. Its recommendation

standards should not only base on the descriptions that uploader created,

but also the contents that the video presents. o In the comment section, the system should base on user’s preference to

priority the repeated internet memes or punchlines.

114