# StoryChat: Designing a Narrative-Based Viewer Participation Tool for Live
Streaming Chatrooms

Ryan Yen  0001-8212-4100 City University of Hong Kong83 Tat Chee AveHong
KongChina University of Waterloo200 University Ave WWaterlooOntarioCanada
<EMAIL_ADDRESS>,  Li Feng  0002-6198-0896 City University of Hong
Kong83 Tat Chee AveHong KongChina The Hong Kong University of Science and
Technology (Guangzhou)83 Tat Chee AveGuangzhouChina<EMAIL_ADDRESS>gz.edu.cn ,  Brinda Mehra  0002-0606-8191 City University of Hong Kong83 Tat
Chee AveHong KongChina University of Michigan Ann ArborAnn ArborMichiganUSA
<EMAIL_ADDRESS>,  Ching Christie Pang  0003-4704-2403 City University of
Hong Kong83 Tat Chee AveHong KongChina<EMAIL_ADDRESS>,  Siying
Hu  0002-3824-2801 City University of Hong Kong83 Tat Chee AveHong KongChina
<EMAIL_ADDRESS>and  Zhicong Lu  0002-7761-6351 City University of
Hong Kong83 Tat Chee AveHong KongChina<EMAIL_ADDRESS>
(2023)

###### Abstract.

Live streaming platforms and existing viewer participation tools enable users
to interact and engage with an online community, but the anonymity and scale
of chat usually result in the spread of negative comments. However, only a few
existing moderation tools investigate the influence of proactive moderation on
viewers’ engagement and prosocial behavior. To address this, we developed
StoryChat, a narrative-based viewer participation tool that utilizes a dynamic
graphical plot to reflect chatroom negativity. We crafted the narrative
through a viewer-centered (N=65) iterative design process and evaluated the
tool with 48 experienced viewers in a deployment study. We discovered that
StoryChat encouraged viewers to contribute prosocial comments, increased
viewer engagement, and fostered viewers’ sense of community. Viewers reported
a closer connection between streamers and other viewers because of the
narrative design, suggesting that narrative-based viewer engagement tools have
the potential to encourage community engagement and prosocial behaviors.

live streaming, narrative, user engagement, moderation, community,
intervention

††journalyear: 2023††copyright: acmlicensed††conference: Proceedings of the
2023 CHI Conference on Human Factors in Computing Systems; April 23–28, 2023;
Hamburg, Germany††booktitle: Proceedings of the 2023 CHI Conference on Human
Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg,
Germany††price: 15.00††doi: 10.1145/3544548.3580912††isbn:
978-1-4503-9421-5/23/04††ccs: Human-centered computing Collaborative and
social computing systems and tools††ccs: Human-centered computing
Collaborative and social computing design and evaluation methods

Figure 1. StoryChat, a live streaming viewer participation tool, obtains real-
time comments in a chatroom and displays a narrative graphical design
representing negative and positive comments. Left: The storyline and graphic
design of StoryChat’s narrative design. Right: A screen capture of StoryChat’s
interface including the live streaming video (left), narrative design (top-
right) and chatroom (bottom-right).

A 3x3 comic representation of the narrative progression for StoryChat,
beginning from the baseline happy state, changing to a darker atmosphere and
the eventual appearance of the ghost as negative comments start being detected
in the live stream chat, followed by a gradual return to the base-state as
viewers start sending in positive comments in the live-stream chatroom. (on
the left) An image of what the prototype looks like. (on the left) the Twitch
live stream. (on the right) the live-stream chatroom, with Storychat embedded
on the top.

##  1\. Introduction

Live streaming becomes an increasingly ubiquitous form of entertainment over
these years, and there has been growing interest in exploring various ways for
viewers to engage in the communities (Lim et al., 2020; Hilvert-Bruce et al.,
2018; Li et al., 2020; Lin et al., 2021; de Wit et al., 2020). Researchers
have also developed a variety of viewer participation tools for live streaming
platforms in order to improve interaction and engagement within the community
via various new communication channels (Seering et al., 2017b; Lessel et al.,
2017c). However, a concerning side effect of these tools is the rising
occurrence of negative behaviors in the community (Seering et al., 2017b;
Striner et al., 2021; Lu et al., 2018), which means that harmful, humiliating,
profane, and toxic comments are spread through the live streaming chatroom,
indicating the need for moderation of these viewer participation tools as
negative comments are likely to reduce viewers’ engagement (Collier and Bear,
2012). Although many live streaming platforms have incorporated systemic
moderation methods with human moderators, the growing scale of live streaming
communities makes it increasingly difficult for platforms to regulate large
quantities of comments efficiently (Jhaver et al., 2019b; Madrigal, 2018;
Ging, 2019). Besides, human moderators usually encounter an intensive workload
and are emotionally stressed when moderating public discussion platforms
(Gillespie, 2018; Wohn, 2019; Seering et al., 2019b; Gerrard, 2020; Steiger et
al., 2021; Dosono and Semaan, 2019). These challenges have increased the
interest in the use of automatic moderation, such as chatbots and machine
learning (Schluger et al., 2022).

While these approaches successfully manage the online community, negative
behaviors remain to be a critical concern (AnyKey, 2016) because these
approaches are primarily reactive, which only responding to already-posted
antisocial information (Schluger et al., 2022). Seering et al. (Seering et
al., 2017a) indicate that, in contrast to reactive approaches, proactive
moderation could have a substantial positive impact on the behavior of other
viewers in the live streaming chatrooms. Proactive moderation could also
influence the viewer to engage in prosocial behavior by creating norms
(Fiesler et al., 2018; Cai et al., 2021a; Cai, 2022). Although research on
proactive moderation is limited (Seering et al., 2019a), some studies leverage
the idea of persuasive design to activate prosocial interaction (Brewer et
al., 2020; Seering et al., 2019a), which proactively moderate the online
community and encourage viewers to behave prosocially. The use of narratives
as a main form of persuasive design was found effective in increasing
engagement and addressing harassment in online communities (Bisker et al.,
2010; Dimond et al., 2013; Wang and Diakopoulos, 2022; Michie et al., 2018).
Narratives have also been widely used as a non-invasive intervention strategy
(Gerrig, 1994; Fiske, 1993; Escalas, 2004) in psychology. Text-based
narratives have often been used to improve feedback quality and decrease the
incidence of harsh comments, particularly in online communities and news (Wu
and Bailey, 2021; van Krieken, 2016; Shen et al., 2014). In addition to text-
based narratives, the power of visual narrative could increase viewers’
empathy (Keen, 2006; Batson et al., 1997; Brunyé et al., 2009), and encourage
prosocial behavior (Batson et al., 1997; Lamm et al., 2007). However, there is
limited research devoted to using the narrative through visual graphics in
online communities for moderation, although the narrative could function the
same way in different media (Green and Brock, 2000, 2002). The combination of
narrative structure and story elements can also create a powerful method of
conveying information that can assist in mental simulation (Taylor and
Schneider, 1989). Inspired by these findings, our study aims to develop a
tool, StoryChat, that uses visual narratives as a persuasive design to
prosocially and proactively moderate live streaming chatrooms.

StoryChat was designed as a viewer participation tool that leverages visual
narrative to proactively moderate live stream chatrooms by the viewer
community. We aim to enhance viewers’ sense of community and engagement
without constraining their participation and encourage them to actively
participate in the ongoing discussion by sending prosocial comments in
response to negative ones. We first crafted the narrative and visual design
through a viewer-centered iterative design process and then integrated the
narratives into StoryChat. To test the moderation and engagement effectiveness
of StoryChat on live streaming viewers, we deployed the visual narrative in a
Twitch-like interface with 48 volunteer viewers to simulate the experience of
viewing real-time live streams. We carefully selected the live streams in the
video game or just chatting category based on the number of viewers and the
frequency of comments determined by the pilot study. We then investigated how
viewers perceived this viewer participation tool through a deployment study
and semi-structured interviews with viewers that focused on (1) How viewers
interacted with the narrative design, (2) If StoryChat effectively increased
prosocial behavior in a live streaming chatroom, and (3) How StoryChat
affected viewers’ engagement and their connection to the community.

Our key findings suggested that StoryChat enhanced viewers’ engagement by
involving them in changing the storyline, increasing both the degree of
entertainment and sense of community during live streams. Additionally,
participants reported a greater sense of community and connection as StoryChat
encouraged them to prosocially engage with the conversation and play a more
proactive role in changing the chat’s environment when they observed negative
comments. StoryChat makes the following contributions to the CHI and the live
streaming community:

  * •

A new interaction paradigm that employs visual narratives to motivate
prosocial behavior and increase viewers’ considerations toward a live
streaming community.

  * •

Empirical evidence demonstrating the effect that visual narratives have on
enhancing viewer engagement, promoting a sense of community, and increasing
social interaction in chatrooms.

  * •

Implications on designing a narrative-based viewer participation tool for the
live streaming community.

##  2\. Related Work

StoryChat builds on the findings of previous research on viewer participation
tools for live streaming, approaches to moderating online community content,
and narrative research that underlies our approach.

###  2.1. Viewer Participation in Live Streams

In recent years, research on viewer participation has shifted the focus from
traditional media to live streaming scenarios (Hamilton et al., 2014; Seering
et al., 2017b; Striner et al., 2019) and from passive spectatorship to active
participation (Smith et al., 2013; Tang et al., 2016). As a result, several
researchers and live streaming platforms, such as Twitch, have suggested
various viewer participation techniques to improve interactivity and promote
social interaction (Cerratto-Pargman et al., 2014; Glickman et al., 2018;
Hamilton et al., 2014; Seering et al., 2017b; Striner et al., 2021). Some
approaches have attempted to improve the viewer-streamer interaction by
designing tools to support emerging forms of community gameplay. For instance,
Helpstone (Lessel et al., 2017c), enabled participants to provide contextual
information or hints to streamers. Twitch Plays Pokémon used viewer commands
from a Twitch chat stream to control Nintendo games (Leavitt, 2014; Ramirez et
al., 2014) and CrowdChess (Lessel et al., 2017b) supported multiple viewers
play chess together against an AI. Apart from games being directly designed
for viewers, viewer participation can occur in a variety of other ways, such
as chatting via text or special emoticons (Hamilton et al., 2014; Tang et al.,
2016), gifting (Hilvert-Bruce et al., 2018), clicking likes or hearts
(Friedlander, 2017), polling (Cheung and Huang, 2011; Lessel et al., 2017a),
subscribing to streaming channels (Gros et al., 2017), moderating chatrooms
(Seering et al., 2017a), or playing games with a streamer in video game stream
(Lessel et al., 2017a). Recognizing the growing attention that has been
devoted to viewer participation techniques, researchers have also begun to
investigate viewer experiences and interaction (Cerratto-Pargman et al., 2014;
Striner et al., 2019), as well as the difficulties associated with designing
these viewer participation tools (Seering et al., 2017b; Striner et al.,
2021). One of the challenges encountered in designing these viewer
participation tools is the negative interpersonal interaction occurred in the
community (Seering et al., 2017b; Striner et al., 2021; Lu et al., 2018). To
address this (Seering et al., 2017b), our research aims to design a viewer
participation tool that can both increase viewer engagement and promote
prosocial behavior or proactively reduce negative comments in a live streaming
scenario.

###  2.2. Content Moderation in Online Communities

Content moderation refers to “the governance mechanisms that structure
participation in a community to facilitate cooperation and prevent abuse
(Grimmelmann, 2015)”, and is a significant topic in online communities such as
live streaming platforms (Haimson and Tang, 2017; Hamilton et al., 2014).
Previous research has developed various moderation techniques, such as
blocklist tools (Geiger, 2016), rule-based word filters (Jhaver et al., 2019b;
Seering et al., 2017a; Jiang et al., 2019), and regular expression filters
(Jhaver et al., 2019b). Prior research on assessing content filtering tools
has focused on user-level interventions (Kiesler et al., 2012; Seering et al.,
2017a; Asthana and Halfaker, 2018). Jhaver et al. (Jhaver et al., 2019c, a)
investigated the impact of implementing community norms and provided
implications on improving user attitudes about fairness and posting frequency.
This research has identified the theoretical underpinnings of the deployment
of user-level interventions to enhance community outcomes. On the other hand,
there has been less research on community-level moderation (Matias and Mou,
2018; Chandrasekharan et al., 2019, 2022, 2017).

Researchers in HCI have investigated the challenges and drawbacks of the
above-mentioned content moderation approaches (Im et al., 2020; Seering et
al., 2019b; Wohn, 2019), and have begun to investigate the influence of
sanctions on community users (Jhaver et al., 2018, 2019c, 2019a; Chang and
Danescu-Niculescu-Mizil, 2019). For example, Seering et al. (Seering et al.,
2017a), discovered that banning any type of behavior had a negative influence
on the frequency of that behavior appearing in subsequent comments. They also
highlighted a flaw in current reactive moderation approaches, suggesting that
they were ineffective at encouraging prosocial behavior. Although various
studies have investigated content moderation techniques in online communities,
many focused on the role of volunteer moderators (Wang and Diakopoulos, 2022)
or social actions conducted by other community members or site users in
reaction to negative behavior (Mahar et al., 2018; Seering et al., 2019b).
Research on proactive interventions is still nascent (Seering et al., 2019a),
and only a few studies investigating the influence of moderation on viewer
engagement (Wang and Diakopoulos, 2022). Research examining the proactive
prevention of negative behavior has found that norms and rules established by
a platform or moderator can influence viewers’ to act prosocially in the
online community (Fiesler et al., 2018; Cai et al., 2021a; Cai, 2022). User
interfaces with persuasive designs such as CAPTCHAs (Seering et al., 2019a)
and the GLHF pledge (Brewer et al., 2020) successfully activated prosocial
interaction, and proactively preventing negative behaviors in online chats.
The benefit of such implicit persuasive designs (Lewis et al., 2011;
Dijksterhuis and Bargh, 2001; Murnane et al., 2020; Nakaoka et al., 2021;
Zhang et al., 2021; Chiu et al., 2009; Purpura et al., 2011; Schultz et al.,
2007) is that they can be adopted subtly and unobtrusively to effectively
modify user attitudes and behaviors without diminishing user engagement.
Previous research leverages narratives to successfully promote prosocial
behavior and address harassment in online communities (Dimond et al., 2013;
Blackwell et al., 2017), providing a fundamental basis for us to design
narratives as an implicit persuasive design to proactively moderate online
community. Our research aims to explore the effectiveness of introducing a
viewer participation tool that can promote prosocial behavior while
strengthening viewer engagement and one’s sense of community.

###  2.3. Narratives as a Moderation Approach

Narratives can act as a cognitive tool for situated understanding, creating
opportunities for mental stimulation and transportation by allowing
individuals to develop a mental model of narrative through immersion or
relating it with their personal experience (Busselle and Bilandzic, 2008,
2009). With narratives, viewers produce internal behavioral episodes that
simulate an event and enable them to imagine actual or potential behaviors as
if they were the main character of the episode(Gerrig, 1994; Fiske, 1993;
Escalas, 2004). These opportunities for mental stimulation or transportation
often add persuasive power to the underlying messages in the narrative by
immersing the individual in the narrative (Green and Brock, 2000; Gerrig and
Rapp, 2004). Previous research has suggested that reading narratives about
others’ unpleasant experiences triggers empathy (Keen, 2006; Batson et al.,
1997; Brunyé et al., 2009), reduces tolerance for negative behaviors (Foubert
and Perry, 2007) and promotes prosocial behaviors (Batson et al., 1997; Lamm
et al., 2007). In our work, we frame the design process extended from prior
research to elicit empathy from viewers by having the narrative design
demonstrate how a protagonist experiences negative comments and how viewers
can help by sending prosocial comments. We also allow viewers to control the
progression of the storyline because the ability to choose paths or actions
within the story makes interactive narratives particularly favorable for
mental simulation (Guzdial et al., 2015). Research on the impact of
introducing interactivity into narratives suggests that they have a stronger
effect on viewer attitudes than more traditional forms of narratives, like
oral storytelling or episodes, because they evoke more vivid mental imagery,
reduce counter-arguing, and increase the identification and character
connection between a protagonist and reader (Green and Jenkins, 2014).

In terms of online communities, prior research has found that sharing and
reading others’ narratives that illustrate how they respond to harassment
assisted harassed people in adjusting their cognition and emotions toward
their experiences (Dimond et al., 2013). The extension of this work applied
narratives to understanding online harassment (Blackwell et al., 2017; Jhaver
et al., 2018) and found that participants draw comfort from sharing their
experiences in the narrative. Previous research has also used narratives to
assist LGBTQ individuals in coming out when recovering from personal crises
and traumatic events (Dym et al., 2019). Michie et al. (Michie et al., 2018)
designed a storytelling platform for critical reflection on the issue of
abortion and found that narratives raise public awareness due to their ability
to motivate social action (Ganz, 2001). Wu et al. (Wu and Bailey, 2021)
utilized negative experience narratives to motivate online community members
to provide more useful feedback. Although transportation imagery theory (Green
and Brock, 2000, 2002) claimed that narrative transportation could work on
various forms of media, a growing body of research suggests the benefits of
using visual narratives over more expository forms as a tool for persuasive
communication through tools like suspension of disbelief and intense
absorption as a consequence of engagement with the narrative – both of which
can often reduce the likelihood of developing counterarguments (Brechman and
Purvis, 2015; Murnane et al., 2020; Slater, 1997; Csikszentmihalyi, 1990)
compared the perception of transportation or mental simulation to a state
similar to ‘flow’, and heavy immersion into the narrative allows individuals
to reduce cognitive resistance and negative psychological reactance while also
allowing them to construct a mental model of the narrative that impacts their
attitude and behaviour (Moyer-Gusé, 2008; Murnane et al., 2020). Although the
current narrative moderation research has shown the effectiveness of text-
based narratives, there has been limited research employing graphical
narratives to promote prosocial behavior in online communities. The most
relevant research, by Murnane et al. (Murnane et al., 2020), created a graphic
narrative design to motivate users to engage in physical activities by
visualizing their activity progress. In a similar manner our research focuses
on arousing viewers’ empathy and engage them in the chat prosocially to
achieve engagement enhancement through the use of a graphic narrative.

##  3\. Iterative Design and System Implementation

To examine the impact of narratives on viewer engagement during live
streaming, we designed StoryChat, which was composed of a narrative graphical
design and a Twitch-like interface (Figure5). The system included Twitch
streaming content and a real-time chatroom (via Twitch’s API) to enable
viewers to watch and engage with the chat as they would on an actual live
streaming platform. The narrative design was crafted via a viewer-centered
iterative design process (Figure6), which included a low-fidelity prototype
workshop (N = 15), a high-fidelity survey (N = 50), and the finalization of
the dynamic graphical narrative. Here, we outline the primary features of
StoryChat, including how the chatroom comments affected the narrative
animations and the design process used to construct the narratives.

###  3.1. Crafting the Narrative: Iterative Design

To further explore user needs and suitable design factors for the narrative
design, we conducted a viewer-centered iterative design workshop with 15
participants (7 female, mean age 22 years, SD = 1.4 years) through convenience
sampling. Participants were compensated in accordance with local standards. We
posted recruitment messages on social media and sent personal messages to
participants that were part of an internal recruitment mailing list. All
participants were familiar with live streams and were intermittent to frequent
viewers of live streams. Participants were asked to create stories that they
believed would be engaging during live streams by sketching or utilizing
paper-based elements, which consisted of characters in various emotional
states, different locations, background elements, and objects that we provided
(Figure 2). Participants were asked to design a story whose main plot included
the reactions of the protagonist when encountering negative and positive
comments. Following this, we conducted semi-structured interviews to
understand participants’ thoughts on their narrative design as well as their
design considerations. From the interviews and observations of participants’
design progress, three key design considerations for narratives emerged.

  1. (1)

Coherence and Simplicity The majority of participants (N = 11) suggested that
they wanted a simple, concise design. This implies that the narrative should
be easily understood without the need for explicit explanation, making it
universally comprehensible while also ensuring the coherence of the story
before and after the occurrence of positive and negative comments.

  2. (2)

Emotional Resonance: Empathizing with the Characters Several participants
believed that the style of the narrative would affect their commenting
behavior. Some participants claimed that an adorable character could develop
emotional resonance (P4: “I think cute characters could induce more empathy”).
Participants also emphasized the importance of a logical and understandable
narrative that fostered cognitive empathy, stating that the design should
immerse viewers in the story so that they can effortlessly imagine themselves
in a character’s position or circumstances (P11: “I think the story should
involve the live stream viewers to make it more immersive”).

  3. (3)

Symbolic Meanings Within the Narrative. All participants carefully considered
the underlying meaning and reason behind the use of certain components in
their story, often describing the messages behind their design choices (i.e.,
different emotional states in the character symbolized the mood of the
viewers). Participants also suggested the incorporation of a character as a
signpost for negative comments in the chat.

Figure 2. Participants ideation process during the narrative design workshop
with experienced live streaming viewers and designers

Five images depicting the narrative design workshop. Participants were
encouraged to use pens, paper, and any other tools to develop a narrative to
represent the various state changes that could occur in the storyline we
designed for StoryChat.

Figure 3. Low-fidelity prototypes of two storyboards representing different
storyline development. (bottom-right) Storyboard of _Amazing Plant_ ; (top-
right) storyboard of _Berry and the Ghost_ ; and (left) detailed settings of
_Amazing Plant_.

(on the left) low-fidelity prototype of the storyline ‘Amazing Planet’,
depicting the various characters involved in the story. (on top-right) 2x3
storyline for ‘Berry and the Ghost’, involving a series of emotional state-
changes meant to reflect incidents in the chatroom. (on the bottom-right) 3x3
storyline for Amazing Planet, which used the concept of ‘growth’ to represent
the various state changes in the chat.

Besides the three design considerations from the design workshop, we
identified two types of storyline development: (1) _Comforting the main
character - Amazing Planet_ (Figure 3 (bottom right). Eight of the
participants (P3,5,6,9,10,11,12,13) designed their storyline such that when
negative comments were received, the main character would become distressed,
whereas when positive comments were received, the character would be
comforted. (2) _Driving away the villain - Berry and the Ghost_ (Figure3 (top-
right)). The plot was designed by seven participants (P1,2,4,7,8,14,15) such
that whenever negative comments arose, a villainous character appeared to
threaten the main character. However, if more positive comments appeared, the
villainous character would be punished and driven away. Both of these story
developments were the result of the viewer’s sympathy for the main character,
either through a more progressive or conservative approach, in an effort to
save or comfort the protagonist.

To compare these two storyline developments, our research team refined the
scratch to a higher fidelity design and then conducted a survey study with
N=50 volunteer participants (i.e., 21 males, 1 non-binary, 2 prefer not to
disclose, ages 18-34, Mean=21) recruited via email, word of mouth, and
snowball sampling, who have more than one year of live stream watching
experience. According to previous study, paid participants tend to act
differently than volunteer participants of live streams (Tang et al., 2017).
Thus, we centered on recruiting volunteer participants who were intrinsically
motivated to watch live streams (Lu et al., 2021, 2018). Participants were
asked to select the most suitable storyline for the live streaming platform
and to provide detailed considerations. The survey included likert-scale
questions asking participants to rate the design features like background,
characters, color scheme, and storyline from 1 (strongly dislike) to 5
(strongly like). In addition, the survey contained the Narrative Engagement
Scale (Busselle and Bilandzic, 2009), which measured participants’ engagement
in narratives from four dimensions: narrative understanding, attentional
focus, emotional engagement, and narrative presence. Participants answered 3
questions from each dimension via a 7-point Likert scale ranging from 1
(strongly disagree) to 7 (strongly agree). At the end of the survey, we asked
participants to respond to open-ended questions, such as “Why or why not do
you want the design adopted in the live streaming chat room?” And we did a
thematic analysis (Braun and Clarke, 2012) of these responses to establish
initial codes that were refined and integrated iteratively into overarching
themes that assisted us make sense of participant feedback on the two
storyline developments. From the results, participants overall preferred
design B to be implemented in the live streaming chatroom over design A
because: 1) symbolic representation of negative comments. With the ‘negative
comments’ appearing symbolically in the story, participants reported having
more empathy for the main character and a deeper emotional connection to the
story. 2) Engagement and Thrill. The Narrative Engagement Scale reported that
design B (Mean=16.21, SD=2.97) received higher score than design A (Mean=7.92,
SD=4.37) in all four dimensions. Participants mentioned that the interaction
with the villains was more engaging and thrilling because of the way the
storyline progressed and their interest in returning the main character of the
story to the stable, happy state it was in before the arrival of the villain
3) Obvious Symbolic Meaning of Characters. Participants reported they prefer
characters in design B (Mean=3.72, SD=0.23) over design A (Mean=2.97, SD=1.2).
They mentioned that the characters in design B are more comprehensible, and
the symbolic meaning behind each character (i.e., bear is representing
streamer) could easily be identified, resonated with, and be closely related
to.

###  3.2. Dynamic Graphic Narrative: Final Version

In accordance with the findings of the iterative viewer-centered design
process, we finalized the dynamic graphical narrative with animations. The
Figure4 illustrated the major plots in the storyline. The stable state of the
story involves a bear character (meant to represent the streamer) interacting
with their computer (Figure4. However, as the incidence of negative or toxic
comments increase in the actual chatroom, the storyline responds by showing an
atmospheric shift - going from a warm, cozy room to depicting thunder and
lightning (Figure4. At a certain threshold of negative comments in the live
stream chatroom, the symbolic representation of these comments (an evil ghost)
appears and the bear reacts in a scared and frightened manner to arouse viewer
empathy and encourage them to send positive comments in the chat(Figure4.
These positive comments are represented in the form of hearts emerging from
the bear’s computer and battering the ghost away (Figure4. After a certain
number of positive or prosocial comments in the live-stream chatroom, the
hearts in StoryChat swarm the ghost and the ghost is chased away - returning
the storyline to it’s base state (Figure4

This storyline and graphical design aligned with our design considerations: 1)
Coherence and Simpleness. Some participants in the survey study suggested that
the story background distracted their viewing experience. Therefore, we
reduced the number of elements in the story as well as ambiguous components in
our final design to reduce distraction. 2) Emotional Resonance: Empathizing
with the Characters. In the survey, 6 participants noted that the original
design of the ghost was not attractive and did not match the design style.
Thus, we redesigned the ghost and the main character to depict more facial
expression. The bear’s reaction towards the ghost turned from furious to
frightened to be more synchronized with participants feeling and more likely
to arouse theirs empathy because of the vulnerability (Cohen, 2001). 3)
Symbolic Meanings behind the Narrative. Our purpose was to develop an
allegory-style story that could correlate participants’ behavior to the
storyline development. Participants’ reaction towards negative comments
further reflect on the protagonist’s action in the narrative. To enhance the
participants’ emotional resonance, a translucent red mask appears on the
screen when the ghost character suddenly appears to signify the danger and
evoke visual stimulation for the participants.

###  3.3. System Overview and Implementation

StoryChat was developed as a web-based system to allow users to access it on
desktop and mobile devices. The system was written in NodeJS under the VueJS
front-end framework and used Firebase’s database service. StoryChat had four
primary visual sections (Figure 5): 1) Live Streaming Content, which
visualized the concurrent interactive frames of a live stream using the Twitch
video API; 2) Admin Control, which is only visible to the admin to change the
filter range and negative comment threshold for triggering narrative change.
Additionally, there was a broadcasting function that used by the research team
to take participants’ attendance during the deployment study; 3) Narrative
Design, incorporated the visual narrative design via a series of short clips;
4) Chatroom (Chat), utilized the Twitch Chat API to connect to the real-world
Twitch IRC (Internet-Relay-Chat) server. To avoid interfering with real-world
chatrooms, we created an additional chatroom that uses the Firebase Realtime
Database to connect all participants recruited to our study, while this
chatroom will still receive real-time chat from real-world chatrooms via the
Twitch API.

Figure 4. StoryChat’s narrative design can be divided into six major plot
points that reflected different events that occurred in the chatroom.

a 2x3 depiction of ‘Berry and the Ghost’, the final storyline, and its
progression from 1) stable baseline state to 2) thunder and lightning as
negative comments appear in the chat to 3) the appearance of the ghost to 4)
the appearance of hearts (meant to represent viewers) battering the ghost to
5) the ghost fleeing and a return to 1) baseline state.

Figure 5. StoryChat’s admin interface with live streaming content on the left,
admin control with multiple threshold adjustments in the middle and chatroom
with narrative design on the right

A snapshot of StoryChat’s admin interface displaying live-streaming content,
threshold adjustments, and a quick view of the chatroom with the embedded
Storychat storyline. It contains four sections: live streaming content on the
left; admin control with multiple threshold adjustments in the middle;
narrative design in the top-right; and the chatroom in the bottom-right side.

###  3.4. Negative Comments and Pilot Tests

In terms of the mechanisms through which narrative design evolved, we began by
defining ‘negative comments’ according to the default setting on one of the
most extensively used Twitch conversation moderation bots111Twitch bot:
https://moo.bot/. By definition, negative comments were those containing
profanity, excessive use of emotes (Kobs et al., 2020; Fox and Tang, 2017),
capital letters, or symbols. StoryChat passed each comment through a comment
filter, upon receiving chat from the Twitch IRC server via a socket
connection, and determined if it was negative via if-else conditional
statement with the definition mentioned above. The classified results affected
the narrative’s plot change when the number of comments had passed a
threshold. To determine the threshold, we conducted six pilot tests with
eighteen volunteer participants and collected the number of negative comments
in proportion to all other comments. The threshold was set to be 1.12 negative
comments per ten thousand viewers every 10 seconds (SD = 1.83), which means
that whenever negative comments exceed the threshold in the time window, the
narrative plot would change. However, we eventually adapted a more flexible
solution by providing the research team a section to adjust the threshold in
the admin interface because the distinction between various live streams is
obvious.

Since it is difficult to define ‘prosocial’ comments with existing rule-based
or machine learning classification techniques in real time, the narrative plot
was controlled via the negativity of the chat room, i.e., when there were less
negative comments, the narrative plot shifted to a positive plot (i.e., plot
3, 4, 5 in Figure4). After the study, we conducted a Deductive Thematic
Analysis (Braun and Clarke, 2006; Fereday and Muir-Cochrane, 2006) and two
researchers classified all comments as either prosocial, negative, or normal.

Figure 6. The flowchart of the iterative design process and studies for
StoryChat, from left to right, including the low-fidelity design workshop, the
survey study on the high-fidelity prototype, the finalization of the dynamic
visual narrative, the pilot testing, the deployment study with the post-stream
interview, and the post-hoc evaluations from moderators and streamers.

A flowchart illustrate four phases of design process for StoryChat, including
understanding viewers need, evaluation, findings, and reflections. Under these
four phases, several studies and design methods being used throughout the
study, from left to right, including the low-fidelity design workshop, the
survey study on the high-fidelity prototype, the finalization of the dynamic
visual narrative, the pilot testing, the deployment study with the post-stream
interview, and the post-hoc evaluations with moderators and streamers.

##  4\. Deployment Study and Post-Hoc Reviews

The goals of the study were to understand the effect of moderation on viewers
and their sense of community and to understand the degree to which StoryChat
improved viewer engagement using a narrative-based tool. To this end,
StoryChat was evaluated via a controlled deployment study with six live stream
sessions that simulated a real time live streaming experience. We chose to
conduct a small-scale deployment study using a ‘staging instance’ (Grevet and
Gilbert, 2015) due to methodological limitations of conducting this study at
scale. Previous research has found that larger population samples are needed
to thoroughly evaluate the usability of a live streaming tool (Lu et al.,
2018; Miller et al., 2017), however, a large proportion of Amazon Mechanical
Turk-hired viewers tend to be more active than volunteer viewers (Tang et al.,
2017), which might lead to distorted results. Moreover, as discovered in a
pilot study, participants would not actively send negative comments throughout
the experiment. Therefore, we created a staging instance with a massive real-
time chat copied from actual live streams (Chandrasekharan et al., 2019).
Similar to real-world live streams, the study replicated an environment where
active viewers could comment and interact with the live streaming community
through chat in real-time and experience the mental or behavioral changes that
resulted from the occurrence of negative comments. Because the chatroom of
StoryChat was linked to an actual Twitch stream, there were viewers of the
Twitch streams who were not involved with our study (non-participants) but
their comments were presented during the deployment study.

In addition to the deployment study, we conducted a post-hoc review study with
two moderators and two streamers to collect their perspectives and reflections
on the findings and StoryChat. One of the design considerations for StoryChat
is to leverage the concept of ‘community-led,’ utilizing narrative to
influence viewers to make prosocial comments and avoid negative ones. Our
research focused on learning how viewers’ behaviors changed when using a
narrative-based tool, and we did not want the new interface to distract
streamers, which would affect viewers’ experiences. As a result, we did not
design features for streamers or moderators to interact with StoryChat.
Although we did not involve streamers and moderators in the deployment study,
their reflections about such a narrative-based tool are critical for future
design and contribute deeper insights and implications to our findings.

###  4.1. Participants

Using social media and gaming networks, 48 volunteer participants (24 female,
mean age 22 years, SD=2.1 years) were recruited from streamers’ Discord
channels (N=11) and word-of-mouth and snowball sampling (N=37) within live
streaming communities. All the participants had watched live streams for at
least six months and watched live streams at least 3 times a week.
Participants for this study were limited to the ages of 18 - 24 and recruited
largely from university campuses based on Twitch metrics, which suggested that
this was the biggest demographic on Twitch (str, 2022).

For the post-hoc review study, two streamers (S1 and S2) with more than two
years of experience in live gaming streaming and two moderators (M1 and M2)
with at least one year of experience moderating live streaming chat rooms and
Discord channels were recruited through personal connections. Streamers and
moderators were compensated with a $30 PARKnSHOP Gift Coupon for the
approximately 45-minute session.

###  4.2. Apparatus, Method, and Procedure

After considering several live streaming platforms, Twitch was selected as the
streaming platform for the study because of its community-based structure
(Seering et al., 2020), abundance of large-scale live streams with massive
chatrooms, and vulnerability to disruptive behavior (Brant, 2016; Seering et
al., 2017a). To enable participants to experience a change in the narrative
design in a short time, we selected six streaming sessions (4 from the game
category and 2 from the just chatting category) with an average of 10k viewers
and 1.67 comments per second, and we attempted to select chatrooms that
contained the similar occurrence rate of negative comments as in the previous
pilot test. The 48 participants were evenly distributed among six sessions
(i.e., eight participants per session). Four of the participants begin viewing
the live stream in the with-story mode (Figure7 Left) of StoryChat, while the
remaining four were assigned to the without-story mode (Figure7 Right).

Before the study, the participants received a consent form and a demonstration
of StoryChat via a video call that explained how the narrative design changed
based on the comments that were made and how the basic interface features
worked. Participants were then asked to complete a survey about their live
streaming habits and to use their own desktop or laptop computer to conduct
the study. Participant participation was implemented and maintained via a web-
based system (Ryan, 2021) to facilitate the online deployment study. Once
every viewer entered the designated streaming session, they started watching
live streaming content and interacted with the community through chat for 15
minutes. The research team then asked them to complete a questionnaire and
switch to the other mode. Four participants were picked at random from each
session to participate in a semi-structured interview, resulting in a total of
24 interviewees.

Following the deployment study and preliminary data analysis, we invited two
streamers and two moderators to provide us their perspectives toward StoryChat
and reflections on the findings from the deployment study. Before the review,
moderators and streamers were given a consent form, information about the
study, and they were instructed to freely explore the interface and
familiarize themselves with StoryChat’s key features. During the review
session, they evaluated deployment study clips, system logs indicating how the
study participants engaged with StoryChat, and participants’ responses from
post-stream interviews.

Figure 7. StoryChat’s deployment study which used a with-story mode (Left) and
without-story mode (Right)

A comparative view of the tools used in the deployment study to test the
efficacy of Storychat (on the left) the piggyback prototype displaying the
Twitch stream along with the embedded Storychat system, (on the right) the
normal Twitch interface.

###  4.3. Metrics

We collected quantitative data from system logs and questionnaires and
gathered qualitative data from semi-structured interviews to gain a deeper
understanding of the factors that contribute to participants’ behavioral
changes and sense of community.

####  4.3.1. System Log Data

For each session, we logged every comment submitted by participants and non-
participants in the live stream. These comments were saved with a timestamp,
the comment type (negative or neutral), and context. All of the logged data
was saved in a Firebase Realtime Database in the JSON format. It should be
noted that 1) The system automatically categorized the negative comments using
the aforementioned classification technique ( paragraph 3.4), however the
remaining comments were manually coded as neutral or prosocial during
analysis. We evaluated several sentiment analysis APIs (Hatfield et al., 1993;
Loper and Bird, 2002; Manning et al., 2014) and machine learning algorithms
(Reis, 2020; Barbieri et al., 2017; Kobs et al., 2020; Chouhan et al., 2021),
but the results were either insufficiently accurate or unsuitable for the
unique language used in the live streaming chatroom (Hope, 2019; Olejniczak et
al., 2015); and 2) we collected comments from both participants and non-
participants, but only analyzed comments and report on the findings from our
own participants.

####  4.3.2. Post-Stream Surveys

We designed two sets of surveys for the with-story mode and without-story
mode, both of which used the (1) System Usability Scale (SUS) (Bangor et al.,
2008) to evaluate the usability of StoryChat for live deployment, on a scale
from 1 (low) to 5 (high). The (2) Inclusion of the Other in the Self (IOS)
(Aron et al., 1992) was used to measure the sense of relationship between
participants and non-participants during the study. This questionnaire has
been adopted in video gaming contexts to comprehend the connection between
players or viewers (Robinson et al., 2022; Chen et al., 2022; Culbertson et
al., 2016). Participants were presented pairs of circles with varying degrees
of overlap, ranging from just touching to almost completely overlapping. In
each pair, one circle was labeled “I” and the other “Other.” Participants were
asked to choose one of these seven pairings to answer “Which image best
depicts your connection with the community?”. The (3) Brief Sense of Community
Scale (BSCS) (Peterson et al., 2008) was used to evaluate participants’
connection to the community and operationalize sentiments of group membership,
need fulfillment, mutual influence, and emotional connection (Maruyama et al.,
2017). On a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly
agree), participants rated their agreement with eight statements.

For the with-story mode, participants were also asked to complete the
Narrative Engagement Scale, which measured their narrative understanding,
cognitive perspective taking, narrative engagement, and emotional engagement
(Busselle and Bilandzic, 2009) using a 7-point Likert scale ranging from 1
(strongly disagree) to 7 (strongly agree). This scale has been adopted in
video and audio narrative scenarios before (Richardson et al., 2020). They
also completed self-defined 5-point Likert questions which were tailored to
the StoryChat and narrative design probed participants’ feelings toward
StoryChat (Figure8).

####  4.3.3. Post-Stream Interviews with Participants.

To gain more insight from participants’ connection with community and
behavioral changes, we conducted a semi-structured interview (questions in
Appendix:A) to collect participants’ perceptions of the differences between
using the system with and without story mode in several aspects, including
comment sending, sentiment changes, and moderation effectiveness. All 24
interviews were conducted remotely over Zoom, and lasted between 30 to 50
minutes. Interviews were recorded and then transcribed using a paid
transcription service222https://www.iflyrec.com/. Finally, two co-authors read
through the transcripts and coded them using thematic analysis with 201
extracted key phrases.

####  4.3.4. Post-Hoc Reviews with Streamers and Moderators.

The post-hoc review interviews were semi-structured, audio recorded, and
think-aloud data (Van Den Haak et al., 2003) was collected from these
additional participants. We focused on how they reflected on the results and
how they anticipated how the features of a community-led narrative-based tool
in a live streaming setting could be enhanced.

##  5\. Findings

In this section, we present findings from the deployment study of StoryChat to
understand how participants interacted with, engaged with, and perceived the
narrative-based participation tool, the extent to which participants changed
their behavior, and how the narrative proactively moderated live streaming
chatrooms. In addition to the deployment study results, we also report
streamers’ and moderators’ reflections on StoryChat during post hoc reviews.
In general, both the deployment study and the post hoc reviews found StoryChat
to be engaging and effective in promoting prosocial behavior.

###  5.1. Use of StoryChat

At the beginning of the study, participants were curious and made many
different comments and tried to influence the narrative due to the novelty of
the system. Participants found that the StoryChat is well integrated and the
SUS usability score indicates the StoryChat is easy to use for many
participants albeit there is space for improvement.

####  5.1.1. System Usability

The SUS was used to assess the difference between usability of with and
without-story mode. We conducted a Wilcoxon Signed-rank test on the result of
all SUS questions for the with-story (Mean=70.67, SD=10.46) and without-story
(Mean=67.87, SD=13.28) modes. We chose this test because the Likert scale
result is interval (normality assumed) ordinal data from two paired groups.
Among all the questions, only one showed significance: “I thought the features
in this StoryChat were effectively integrated” (W =17.0, p ¡ 0.05). This
indicates that with narrative design, features in the system were more
effectively integrated. Apart from this, all other scales had a p-value
greater than 0.05, indicating that the narrative design integrated well with
the system interface, without introducing negative impact.

####  5.1.2. Novelty Effect

Participants were first intrigued by this new appearance and sent numerous
comments attempting to understand the mechanism behind StoryChat. One user
stated that “I attempted to send a great number of monetary symbols, thinking
that it would be a sort of negative comment” (P3). According to the comment
analysis, participants submitted 53.71% of the total comments, 89.35% of the
negative comments, and 43.1% of the prosocial comments in the first two
minutes. However, considering that this was caused by the novelty effect, we
excluded these comments from our quantitative analysis. The participants’
curiosity about how comments impacted the narrative (P1, 8, 12, 14) at the
beginning made them contribute more negative comments. Two participants also
stated that they would be tempted to send some prosocial or negative comments
to see more graphical changes in the narrative design because they felt that
it provided a more enriching and fascinating live stream experience. P8 stated
that “if there are no negative comments, I’ll troll to see the story’s
response”, while P14 expressed an interest in seeing “how bad the story could
go”.

###  5.2. Viewers’ Engagement and Participation

When viewing a live stream using the narrative design, participants
contributed an average of 84.5 (SD=67.82) total comments, Mean=1.17 (SD=1.34)
negative comments, and Mean=20.83 (SD=21.04) prosocial comments to the
chatroom (Figure9). In the without-story mode, they provided an average of
37.33 (SD=36.35) total comments, Mean=1 (SD=0.82) negative comments, and
Mean=4.17 (SD=5.58) prosocial comments. A paired T-test was conducted on the
number of chat comments between the two modes, and the results showed that
with the narrative design, participants sent significantly more chat comments
(p=0.0286).

The Self-Defined Likert Scale (questions in Appendix: B) Results revealed that
participants were affected by the narrative design (Mean=4.13, SD=0.97) and
were helped in interacting more with chat (Mean=4.02, SD=0.83). Although they
believed that their comments had little effect on the narrative (Mean=3.43,
SD=0.84), they were inspired to respond after seeing the negative comments
(Mean=4.18, SD=0.75) due to their concern about the narrative (Mean=4.13,
SD=1.04). The level of narrative engagement was also evaluated via the
Narrative Engagement Scale (Figure10). Participants showed high scores on
Narrative Understanding (Mean=13.73, SD=2.88), and Narrative Presence
(Mean=13.38, SD=3.06). The majority of the participants were easily able to
comprehend the narrative plot (Mean=4.1, SD=1.43), both in the characters
(Mean=4.73, SD=1.34) and the story thread (Mean=4.9, SD=0.99). This was
supported by the findings of the interview, where all the participants were
able to recall the plot of the story when asked to do so, although some were
not able to remember all the minute details. However, some participants noted
that they lost interest in the narrative after watching it repeatedly, stating
that: “I was interested in it at first, but I lost my interest later since it
is monotonous ” (P15) and “I was interested in it at first, but I didn’t pay
much attention to it later” (P12). This may suggest that the story used in
this study was too easy to comprehend. The following are detailed descriptions
of emotional engagement and attentional focus, the highest and lowest scores
on the Narrative Engagement Scale, respectively.

Figure 8. Responses of the Self-Defined Likert Scale questionnaire about the
experience of using the narrative design in StoryChat.

A stacked bar graph displaying the percentage of responses (middle point
refers to 3, and 4, 5 would be on the right hand side of the graph with 1, 2
on the left) that agreed with one or more statements about the experience of
using Storychat. All the self-defined Likert scale questions are listed in the
appendix. This self-defined Likert Scale depicts the responses from users
using StoryChat’s narrative design. There are ten questions with a rating from
1 (strongly disagree) to 5 (strongly agree). The bar chart shows the
percentage of average ratings from 48 participants, where most of the answers
were rated 4 or 5, by approximately 85% of users. However, the question, “I
felt like I was able to influence the narrative story” only received around
60% of ratings of 4 and 5. And the question: “I felt like it is my
responsibility to moderate the chatroom” received the lowest score, where over
half of the users disagreed with it.

In general, the narrative design enabled participants to connect with
streamers and comment in an engaging manner, and participants believed that
the story had an effect on the chatroom’s environment (Mean=4.23, SD=0.97)
(Figure 8). They indicated that using the narrative design to watch live
streams and interact with the chat was engaging and fun (Mean=4.0, SD=0.96),
and they felt more active in the community while doing so (Mean=3.9, SD=0.87).
For example, P9 noted “I felt more engaged with a feeling of participation”
and P11 concluded the reason for the attentional focus was that “I will take
more time to read the chat”. Another reason might be that they were worried
about the narrative development. Many participants stated that they were
worried about the main character when negative comments appeared. In addition,
we discovered that the majority of participants did not feel they were
responsible for moderating the chat (Mean=3.08, SD=1.12), but were instead
participating in the community,e.g., “I believe the story helped me
participate more actively in the community, but I do not see myself as a
moderator.” (P12). This result suggests that the narrative design may not lead
participants to become moderators or assist in developing a sense of
responsibility, but it helped them to participate more in the community and
increase their engagement while watching the live stream.

####  5.2.1. Attentional Focus

While the presence of the narrative design made the experience novel and
appealing for participants, the majority of participants mentioned that most
of their focus remained on the streaming content since that was their primary
reason for using Twitch and that they “focused more on the streamer” (P6).
However, participants acknowledged that watching the stream actually motivated
them to check back from time-to-time so they were aware of what was going on
in the chat and did not miss anything, paying “more attention to the chat and
story” than usual (P19), as well as to the “negative comment and others’
reaction” (P7) and driving their “attention to the negative comment” (P4).
Although P5 mentioned that they “felt a bit distracted since they had to pay
more attention to the right-hand side, which is not the streaming content”,
and the Attentional Focus (Mean=8.83, SD=3.87) score related lower in the
Narrative Engagement Scale. The findings suggested that the narrative was an
attractive, but not disruptive, tool for most of the participants and
influenced their community participation but not their live stream watching
experience.

Several participants also pointed out that they stopped frequently checking
the chatroom and the narrative once they understood the link between comments
in the chat and changes in the narrative, only switching their attention back
to the chatroom once they observed graphical design changes, which indicated
the potential presence of negative comments. In short, participants treated
the narrative as a summary or indication of the atmosphere of the chatroom and
used it to make sense of the events occurring in the chatroom (P4, 5, 6, 9,
10, 13). P9 stated that “the most important element for me is the function of
summarization”, while P12 mentioned that they “don’t usually watch chat, it’s
too distracted, but the story helped me to summarize the current situation of
the chat”. This finding was particularly prominent for participants who were
not used to viewing or participating in the chat, with many of them pointing
out that they were more inclined to pay attention to the chatroom in the
conditions with StoryChat.

Figure 9. Amount of comments sent by users from the six sessions in two
different modes (i.e., with-story and without-story).

A stacked bar graph displaying the number of different types of comments sent
by users in either of the two testing modes (with or without Storychat).
Through six deployment study sessions, a bar chart depicts the number of
comments sent when using with and without-story mode. In every session, the
amount of comments send when using with-story mode was higher than when using
the without-story mode. Session 1 and 3 had the largest differences, while
session 2 had the smallest.

Figure 10. Participants’ responses to the Narrative Engagement Scale.

A box and whisker plot displaying the spread of responses sent in by
participants for the Narrative Engagement Scale, showing the means and SD of
four scales of the Narrative Engagement Scale, which included narrative
understanding, attentional focus, narrative presence, and emotional
engagement. Emotional engagement, narrative presence, and narrative
understanding received similar scores of around 13 to 15. While the
attentional focus received the lowest score of 8 with the largest amount of
variance.

###  5.3. Moderation Effectiveness

The following section demonstrates how participants moved from becoming
emotionally attached to the story, to becoming more connected to the
community, to sending more prosocial comments.

####  5.3.1. Emotional Engagement and Empathy

According to the Emotional Engagement questions (Mean=14.73, SD=3.36) from the
Narrative Engagement Scale (Figure10), participants reported that the
narrative affected them emotionally (Mean=4.8, SD=1.3), and they felt the same
way the main character (the bear) was experiencing (Mean=5.1, SD=1.28). Some
participants could not describe their emotional change in detail, and they
just stated that there was some change: “it affected my emotions at first,”
(P15) and “There is some change in my mood, but I think it is not nervous”
(P10). Other participants reported several types of emotions, which were
summarized into four main categories: curiosity, empathy, irritation, and
happiness. Curiosity was one of the main feeling while watching live streams
with StoryChat at the beginning of the study. Participants were “curious about
how the story would go when there was a negative comment” (P16). This
curiosity also drove them to influence the story by themselves, e.g., “I was
curious, so I participated in the chat to influence the story” (P1) and “I was
curious about whether the story plot would change because of my sending
comments” (P12). Some participants were more interested in the principle
behind the narrative, expressing curiosity about the “working principle of the
story system … by the sentiment analysis?” (P11). Overall, most participants
were quite interested in the graphic and storyline changes occurring in the
narrative, at least in the first two minutes.

The interactive narrative also helped participants cultivate a sense of
empathy during their viewing experience. Observing the bear protagonist become
scared or angry as a result of the ghost antagonist appearing as a
representation of negative comments occurring in the chat, led participants to
feel sympathetic towards the bear and made them want to engage in actions that
could improve the situation for the bear, citing that the “bear is so poor. It
is always scared by the ghost” (P1) and that they could “feel that the bear’s
emotions are not good” (P5). Increased empathy with the bear protagonist also
resulted in increased levels of irritation towards negative comments,
especially since these comments were visualized, for many participants. P3
stated that “this story amplified the annoyance of the negative comment”, P21
noted that “I felt annoyed when the ghost comes out, and this feeling was
emphasized by the story” and P6 also suggesting that they were “annoyed by the
same comments repeatedly sent”.

Participants also reported feeling happy once the ghost antagonist was
expelled from the narrative and the plot returned to its original peaceful
setting, with P6 explicitly referencing the impact of the hearts that chased
away the ghost antagonist, “making the audience feel better” and “happy that
the bad scene was gone” (P9). P9 also compared their experiences between the
two modes, stating that “after the two tasks, I felt I was happier with the
story than without it”.

An analysis of the chat logs showed a dramatic increase in the number of
prosocial comments being sent by viewers right after the ghost antagonist
appears in the story (256% increase ten seconds after the event), suggesting
that the main motivation behind viewer behavior was the desire to help the
protagonist of the story by eliminating the features that were causing the
main character pain or unhappiness. Participants explained their behaviour
through statements like “I wanted to protect the bear” (P5); “I wanted to do
something for the story” (P4). Although these motivations seem to be rooted in
empathy for the main character of the narrative, a deeper probe into
participants’ emotional engagement suggested that the ability to influence
something greater than the narrative (i.e., the community) inspired
participants to continue to engage with the chat and the narrative. This was
supported by statements such as “the story motivated me to type something
nice,” (P20) “I want to influence the chat,” (P5) “I want to participate in
the community,” (P16) and “I feel that it is my task to improve the chat”
(P9).

####  5.3.2. Connecting to the Narrative, Chat, and Community

While participants’ beliefs that their actions were having an impact on the
community led many to actively contribute in the chatroom and make a
difference in the community, the system did not induce any forced behavioral
change, as there were no punishments for not participating in the chat.
Participants felt that they were “not responsible for doing anything” (P7),
but they were more likely to think about the type of comment they wanted to
send in the chatroom and the impact it would have on the community. They often
choosing to take time to think about what they would like to say, with P12
stating that “I want to send some comments, but I don’t want the story to go
badly”, while P16 said the presence of the StoryChat narrative may “lower the
negative comments, because people will worry when it begins to affect the
story”. This suggests that the presence of StoryChat was a non-intrusive yet
reliable behavioral nudge, with participants thinking about, and interacting
with, the community more despite not being forced to do so.

##### Connecting to the Chat

The results of the survey and the interviews suggest that participants were
either influenced by the chat or influenced the chat when viewing live streams
in the with-story mode. Participants were able to identify a connection
between the narrative design and events in the chat, with P5 summarizing this
relationship by stating that “the chat is reflected in the story and the story
could influence the chat. They are connected”, while P9 succinctly described
the relationship as “the story is affected by the chat”. Almost all of the
participants comprehended the logic behind how the narrative reflected the
events of the chat and engaged with it by sending comments and observing
changes in the plot. The findings of the qualitative interviews further
suggested a marked effect of changes in the graphical design elements of the
narrative on participants’ attention towards the chatroom. During plot changes
in the narratives, participants became “curious about what happened in the
chat” (P3) and “motivated to look at the chat” (P11). P16 and P19 stated that
they were interested in using the narrative to identify the negative comments
in the chat, checking the “negative comment when the ghost came” or “who was
trolling”, respectively. These unexpected findings allowed participants to
connect more closely with the chatroom and quickly understand the atmosphere
and trend of the chatroom discussions in real-time.

##### Connecting to Other Viewers

Findings from the interviews suggest that participants increased level of
interaction with the chat by sending comments resulted in feelings of a
stronger sense of community and belonging. Although it is difficult to
completely quantify such feelings, the IOS scale (Figure 12) showed a
significant increase from the without-story mode (Mean=2.81, SD=1.31) to the
with-story mode (Mean=4.52, SD=1.55). The results from the Brief Sense of
Community Scale (BSCS) (Figure 11), reporting higher scores in with-story mode
in all four categories, including Needs Fulfillment (with-story: Mean=6.0,
SD=1.67 / without-story: Mean=5.68, SD=1.71), Membership (with-story:
Mean=8.1, SD=1.61 / without-story: Mean=5.35, SD=1.8), Influence (with-story:
Mean=8.0, SD=1.16 / without-story: Mean=5.4, SD=1.79), and Emotional
Connection (with-story: Mean=8.18, SD=1.67 / without-story: Mean=5.05,
SD=1.86).

The results suggested that participants did feel a sense of inclusion and
belonging to the live stream viewer community as a result of the presence of
the narrative in StoryChat. P13 contrasted their experiences of watching the
live stream with and without-story modes, noting that she was “upset when no
one responded to my comments. The story makes it better and more interactive”.
Participants also enjoyed the sense of camaraderie that occurred as a result
of all of the participants working towards the same goal, i.e., eliminating
the negative comments, e.g., “the community can help drive the ghost away”
(P16) and “I felt that the community and I win the fight together when seeing
the ghost expelled” (P23). Other participants reported feeling more connected
to other viewers in the community in the presence of the narrative (P5, P6,
P9, P19), with some participants explicitly stating that the presence of the
narrative strengthened their interaction with the chat and the community,
encouraging them to send more comments to make a difference to the atmosphere
in the chat (P1, P6). Although some participants were uncertain about the
extent of their impact on the community, believing that their main focus was
to alter the narrative (P5, P12), the majority of participants strongly
believed that their actions influenced both the community and the narrative.
P10 stated that “I definitely influenced the community since I was sending
comments” and P23 noted that “positive comments are good for the chat and us”.
Some participants also believed that they were indirectly protecting the
streamer by sending prosocial comments into the chat, with P1 believing that
their actions could help them “protect the streamer and the community” while
P13 believed that sending prosocial comments in the chat “changed the
atmosphere because they protected the streamer”. Participants also reported a
sense of achievement after sending a few pleasant or friendly comments to the
chat, which inspired them to continue sending prosocial comments and improve
the community once they started receiving similar responses.

Figure 11. Brief Sense of Community Scale score distribution of with and
without-story modes.

A box and whisker plot displaying the score distributions of the Brief Sense
of Community Scales across the with and without-Storychat conditions on four
different scales: needs fulfillment, membership, influence, and emotional
connection. All of the with-story modes received higher scores than the
without-story modes, where needs fulfillment had the lowest difference and a
similar average score of 6. Among the other three scales, the with-story mode
had an average score of 8, and the without-story mode varied from 5 to 6.

Figure 12. Mean (+/-SE) ratings to the Inclusion of the Other in the Self
(IOS) of with- and without-story modes.

A vertical bar graph displaying the overall ratings for the Inclusion of Other
in the Self scale for the with and without-Storychat modes. The higher the
score, the stronger the link between self and others, while 0 indicates no
intersection. The with-story mode received an average score of 4.5, which
outperformed the without-story mode’s average score of 2.8.

####  5.3.3. From Thought to Action

A notable impact of narrative was that it not only motivated participants to
consider elements of community and togetherness, but it also encouraged them
to put them into action. Once participants gained familiarity with the system,
the majority of the participants sent prosocial comments as a way to support
and protect the community and the streamer against negative comments.
Prosocial comments were more prevalent in the with-story mode than in the
without-story mode (Figure9), However, it is difficult to discern the
difference in the quantity of negative comments since, after the first two
seconds, there are merely no negative comments in both with and without-story
mode. This finding is consistent with our pilot test observation and past
research (Glickman et al., 2018) indicating that participants in the ‘research
study’ were unlikely to provide negative comments. Although there was no
discernible effect in terms of the number of negative comments, participants
stated that they were less likely to send negative comments due to the empathy
evoked by the narrative and the community’s awareness, e.g., “I aware that
people don’t swear as much as usual. I think maybe it’s because we don’t
really want to affect others and let the story turn bad because of us.”
Participants noted that the narrative had a positive impact on them as well,
with their “behaviour being affected and influenced in a good way” (P13), and
that this gave them a chance to “strengthen their communication with others”
(P1). Two participants elaborated on this by stating that although they were
not able to change the behavior of the negative comments, they could still
improve the live stream viewing experience for other viewers by sending
prosocial comments with others and adopting a “haters gonna hate” attitude
towards the negative comments (P14).

###  5.4. Reflections and Perceptions from Streamers and Moderators

Both streamers (S1, S2) and moderators (M1, M2) found the idea of leveraging a
graphical narrative as an implicit and proactive moderation tool interesting
and valuable in terms of setting norms in the chat and increasing engagement.
However, they also suggested a few limitations of doing so, which needed to be
addressed in the future.

##### Usability, Engagement, and Community Sense

Both the streamers and the moderators noticed that both prosocial and negative
comments increased in the first few minutes. While they understood that this
is a common phenomenon when a new extension is applied to a chatroom, the
novelty effect is more severe with StoryChat than chatbots. S1 suggested that
this might be caused by the visualization involved, i.e., “the appearance of
the story on top of the chat allows viewers to see the visual change based on
what they just sent without the need to consider consequences”. They also
recognized that viewers tended to be more interactive than usual in the with-
story mode, which they believed was a positive indication they were engaged in
both the community and the chat. However, both S1 and S2 indicated that most
of the chat was related to the storyline, suggesting that they might possibly
be distracted from the live streaming content, e.g., “Although more comments
were sent by these viewers, I feared that they were actually more engaged with
the chat than with our streaming content” (S2). Both streamers and moderators
also noted that viewers participated more in the chat and built stronger
relationships with other viewers, which is an important factor in feeling more
connected to a streaming session and being more willing to participate in the
future (e.g., “I love the bonding between them (the viewers) as they fight the
ghost together” (S1) and “Although the quality of the stream is the most
important thing, belonging to the community that developed through the story
is also valuable when speaking to attract viewers to our channel” (M1)).

##### Moderation Effectiveness

Although the streamers and moderators observed that the disparity between the
number of negative comments made by modes with- and without-story did not
vary, they were surprised to observe an increase in prosocial comments, which
encouraged more viewers to participate in the chat and avoid negative
comments, e.g., “I believe it is a great achievement that most of the
moderation tool I used before can only decrease amount of negative comment and
might even decrease the volume of chat after ban” (M1). However, both
moderators noted that comment removal is still required, since some viewers
continued to propagate toxicity in the chat and might irritate other viewers
or the streamer. M1 also mentioned that the story facilitates the community’s
pursuit of justice, which is what most moderators and viewers anticipate when
unpleasant comments are made.

Moderators also reported that the narrative would reduce the mental and
emotional toll of moderating because moderators often play the role of ‘bad
guy’ between the interactions of viewers and streamers. The narrative design
could divert viewers’ attention, and the protagonist (bear) could serve as a
role model in the chat, which is typically the moderators’ responsibility.
This was supported by statements such as “it’s nice that I can hide behind the
story and communicate with the chat like a normal viewer” (M2) and “the bear
has replaced me as the good example in the chat, which means I don’t have to
act as the ‘good student’ that everyone hates in school” (M1).

M2 mentioned that the narrative would allow him to spend more time on the
chatroom or answering questions from viewers, e.g., “I would prefer to join in
the chat and answer viewers’ questions rather than spend too much time banning
users or setting up moderation rules” while M1 would prefer to maintain their
professionalism by keeping a distance from the viewers and focusing on
moderation. M1 also raised concerns about the idea of content moderation since
the tool only validates the comment and treats all viewers the same, e.g., “I
believe the design, like most chatbots, has a flaw in that it does not
consider that the importance of different comments varies. If the negative
comment is from a superfan, for instance, we don’t usually ban it as it’s most
likely to be some sort of joke.” This suggests that chat is often tolerated
differently by different viewers and that identifying violators is
significantly more difficult than distinguishing them based on their comments.

##  6\. Discussion and Implications

This study has laid the groundwork for future research on the effects of
visual narratives on prosocial behaviors and highlighted several future
directions and challenges for improving viewer engagement and viewer sense of
community.

###  6.1. Engagement and Empathy Arousal

The results demonstrated that participants were emotionally engaged in the
narrative and were motivated to behave prosocially. One possible explanation
is that the participants built a mental model of the story plot points by
combining their own beliefs, emotions, and goals with the story they are
watching (Busselle and Bilandzic, 2009, 2008). Previous studies demonstrated
that such narrative involvement might have a substantial effect on the
behavior or attitude of viewers (Dahlstrom, 2010). The Extended Elaboration
Likelihood Model offers an alternative explanation for our findings, namely
that narratives may drive individuals to engage in prosocial behavior with
less cognitive resistance (Slater and Rouner, 2002; Moyer-Gusé, 2008; Quick
and Stephenson, 2007) and the more enjoyment with a narrative, the greater the
likelihood of overcoming negative psychological barriers (Cohen, 2001). Our
findings also revealed that viewers identified with the protagonist and
claimed they were driven by a desire to help. This is congruent with the
Entertainment Overcoming Resistance Model, according to which a ‘vulnerable’
character might evoke a higher emotional response (Cohen, 2001; Moyer-Gusé,
2008). These findings open up new avenues for designing and exploring
narratives that can engage and immerse viewers on a deeper level, particularly
by incorporating themes related to real-world issues such as racism, sexism,
or harassment.

Our results have also shown that proactive moderation provided by the
narrative could increase user participation and engagement with the narrative
and others in the community. This finding is consistent with previous work,
which suggested that positive feedback can encourage users to become more
active (Schneider et al., 2011; Wang and Diakopoulos, 2022) and more engaged
in the online community (Burke et al., 2009; Fanfarelli et al., 2015; Lampe
and Johnston, 2005). This engagement could also translate into “on-the-ground”
activism (Lee and Hsieh, 2013), encouraging viewers to post prosocial comments
in the chatroom. This finding is similar to previous research on implicit
persuasive design (Michie et al., 2018), which suggested that the more users
engage with the design, the more likely they are to develop empathy and engage
in prosocial behavior (Batson et al., 1997; Lamm et al., 2007). Future work
can leverage these feedback loops to encourage overall shifts in attitudes in
the chatroom, bring about proactive behaviors in online communities, and
examining how narratives can affect individual viewers.

In addition to narrative engagement, our findings revealed that empathy was
another critical component in promoting prosocial behavior. The results were
consistent with previous research, suggesting that narratives enhance the
quality of feedback and decrease negative feedback (Wu and Bailey, 2021;
Batson et al., 1997; Keen, 2006; Lamm et al., 2007). The ability for
narratives to elicit empathy and encourage prosocial conduct is supported by
studies on perspective taking, which claim that when viewers are engaged in a
narrative and envision how the protagonist feels, empathy is elicited and
altruistic motivation is stimulated (Batson et al., 1997). Future research
could design tools aligned with these narrative models and theories to
investigate viewer motivation in depth. Future work could also extend the
research to provide a more participatory narrative or system to viewers, which
could increase their involvement and enjoyment (Green and Jenkins, 2014; Riedl
and Bulitko, 2013).

###  6.2. Connection to the Community

In addition to the moderation effectiveness brought by the narrative
engagement, our findings indicated that an increase in viewers’ sense of
community prompted them to consider their comments more thoroughly. This
result might be explained by studies on bystander intervention, which
emphasized the significance of establishing norms to curb negative behaviors
(Kiesler et al., 2012; Naab et al., 2018). Empathic sympathy, a psychological
method for encouraging bystander intervention, could enable bystanders to
perceive the harm caused to victims (bears) by negative comments and behave
prosocially (Zaki, 2019; Konrath et al., 2011). StoryChat’s narrative design
encouraged viewers to post prosocial comments rather than interfering with
negative comments. This finding is similar to the study on cyberbullying in
social media (Taylor et al., 2019), which indicated that empathic concern
predicted the likelihood of participants clicking the ‘like’ button on a
cyberbullying victim’s post but not other intervention behaviors. Future works
could examine the bystander intervention regarding to the effectiveness of
narrative-based moderation tool. It is also possible to focus on the
examination of bystanders and explore the ways in which visual narratives are
or are not able to influence their behaviors. Both IOS and BSCS indicated that
bystanders were more interconnected, which increased their accountability and
framed themselves as a major factor in bystander interventions (DiFranzo et
al., 2018; Darley and Latané, 1968). Research on in-group framing provides an
extra rationale for the efficacy of moderation in our research, i.e.,
perceptions of others as in-group members may increase prosocial behavior
(Stürmer et al., 2006). The use of in-group framing may elicit empathy from
viewers and enhance inter-group connections (Stephan and Finlay, 1999). Future
research may employ the idea of in-group framing to design the narrative-based
moderation tools and investigate the efficacy of viewer-in-group framing
across time to determine how framing evolves.

###  6.3. Community-Led Moderation

Current methods of content moderation on Twitch are both ‘visible and
invisible’, with viewers being able to view the moderation occurring in real-
time chat but being unaware of or unable to access information about the
backstage processes relating to sanctions or suspensions by human moderators
(Cai et al., 2021b). Although Twitch’s reliance on human moderators often
allows for the development of a relationship between viewers and moderators
through the use of live explanations of moderation or creating accountability
through the deletion of content, timeouts, or bans, there are still occasions
where moderation cannot occur quickly enough or exceeds the moderator’s
capabilities (Thach et al., 2022). Additionally, moderators often choose to
dismiss actions that would generally result in moderation either because they
consider such behavior as being part of the viewer’s persona, they feel they
have a good grasp on the viewer’s intentions, or they just want to distance
themselves from the situation (Cai et al., 2021b). However, the presence of
such behavior may still have a negative impact on viewers, who may not agree
with or understand the moderator’s rationale. StoryChat’s narrative design
provides viewers with the opportunity to actively participate in the chat to
‘drown out’ the toxicity and negativity of other comments by engaging in their
own type of moderation, with successful efforts on the part of the viewers
resulting in the return of the story to its baseline state. The presence of
StoryChat itself acts as a motivator to engage in this type of behavior, with
viewers being encouraged to work together to create a harmonious environment
in the chatroom in response to watching StoryChat state-changes. Future
studies can take the next steps in harnessing the power of StoryChat to
inspire prosocial and empathetic behaviors in other online social media
communities. This includes exploring the development of dynamic visual
narratives based on the nature of feedback, discussion, or comments, to
building connections and prosocial behavior in interactions with content
creators, moderators, or other users.

##  7\. Limitations and Future Work

The current StoryChat was tested as a proof-of-concept in a controlled staging
instance. To understand the extent to which narrative-based moderation
paradigms can impact and influence viewer behavior, future versions of this
study can expand to the use of field observations in regards to how viewers
respond to the presence of StoryChat and how well it facilitates user-
initiated community-led moderation. It is also important to note that the
effectiveness of the moderation tool may change over time, as the platform,
content, and viewers evolve. This opens up the scope of this line of research,
allowing for the exploration of various methods and design tools to develop a
generalizable version of narrative-based moderation. Key limitations
identified in this study included the impact of narrative based moderation on
viewer’s cognitive load, opening up avenues to test whether the use of this
tool predominantly increased viewer engagement with the community or acted as
a distractor as well as questions about the effect of this tool on specific
communities (Ren and Kraut, 2011), inviting investigation on the extent to
which the narrative is useful for minority communities as well as exploration
on specific narrative for such communities.

Additionally, the growing occurrences of targeted negative behaviour like
hate-raids and harassment towards women (particularly women of color) and
LGBTQ+ groups (Gray et al., 2017; Uttarapong et al., 2021) may require the
need for narrative structures that take into consideration their experiences.
Besides viewers, we acknowledge that the findings may not apply to all live
streams as the norms vary in different live streaming communities (Cai et al.,
2021a). The narrative structure tested in this study is uniform and repetitive
in the case of longer live streams. In the future, research can expand to
investigate narrative designs that are customizable for each live stream.
Lastly, while initial findings suggest that StoryChat is successful at
encouraging viewers to post positive comments and proactively intervening with
viewers’ intentions to post negative comments, it is difficult to gauge the
impact it has on viewers who are purposely being toxic. We hope that future
iterations of StoryChat can be implemented in tandem with moderators to reduce
the negative impact of viewing toxic chats while also creating a sense of
community.

##  8\. Conclusion

In this paper, we developed a novel, narrative-based viewer participation
tool, StoryChat, that was designed to encourage prosocial behavior and enhance
viewer engagement in live streaming chatrooms. We first utilized an iterative,
viewer-centered design process to conceive and construct a captivating, yet
simple, interactive narrative with a graphical design that altered the
narrative’s plot based on the current chatroom comments. We then implemented
StoryChat in a controlled staging instance that simulated the Twitch viewing
experience connecting to a real-world chatroom. The deployment study analyzed
the StoryChat’s usability, the effectiveness of moderating, the viewers’
engagement, and their sense of community. Qualitative data from interviews and
quantitative results from a questionnaire and system logs indicated that
StoryChat’s increased the likelihood of viewers engaging with the chat,
particularly in the form of empathetic or prosocial comments and also enhanced
the viewers’ sense of community with the streamer and other viewers in the
live streaming, resulting in increased feelings of responsibility and
consideration when sending comments. Overall, we believe that these findings
support the benefits of using graphical narratives as a moderation tool for
viewer-led community moderation and increasing viewer engagement and set the
stage for future works that explore the benefits of encouraging behavior
change through the use of dynamic, graphical narratives.

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##  Appendix A Post-Stream Interview Questions

##### Live Stream General Questions

  1. (1)

For what purposes do you watch live streams?

  2. (2)

How many times a week do you think you watch live streams on either Twitch,
Youtube Live, TikTok, or any other platform?

  3. (3)

In general, when you interact with the live streaming chat room, what kind of
messages do you post?

  4. (4)

Why do you post these kinds of messages and what do you hope to gain from
interacting with the chat channel?

  5. (5)

In one of the streams, there was a short story playing repeatedly. Can you
recall what the story’s plot was?

  6. (6)

Can you explain what you think the story was put there for?

  7. (7)

Which part of the story did you think was the most important?

  8. (8)

What elements or aspects made you think this part of the story was the most
important?

  9. (9)

In what ways do you think the story impacted your overall live streams
experience?

  10. (10)

Can you outline some differences you felt while watching the stream normally
or with the story?

  11. (11)

How does the story influence you? Why? What in the story influences you? (chat
behavior, perception to community)

  12. (12)

Can you give us an example of how the presence of the story influences your
behavior or perspective towards the live streaming chatroom? Why?

  13. (13)

Going back to your response on how often you interact with the chat channel –
do you think you would interact with chat or community more because of the
story? Why would you like to do this?

  14. (14)

Do you think you can influence the community? Why do you want to influence it?
How can you influence?

  15. (15)

In what ways do you think the story influenced your ability to engage with the
chat channel?

  16. (16)

What elements of the story were particularly interesting for you? Why?

  17. (17)

What elements of the story would you change if given the opportunity? Would
you even want to have a story at all? Why?

  18. (18)

Which character were you more interested in - the bear or the ghost? Why?

  19. (19)

When and why do you send a message?

  20. (20)

When and why do you pay more attention to the story?

##  Appendix B Self-defined Likert Questions

  1. (1)

I enjoyed using the StoryChat interface to watch live streaming.

  2. (2)

My attention is often being attracted by the narrative design.

  3. (3)

I felt StoryChat helped me better interact with live streaming chatroom.

  4. (4)

I felt more active when watching live streams with StoryChat.

  5. (5)

I felt like I have been influenced by the narrative design.

  6. (6)

I felt worried about the story when the troll appears in chatroom.

  7. (7)

I felt like I was able to influence the narrative story by sending messages.

  8. (8)

I felt urgent to send good messages after the trolling messages appeared in
chat.

  9. (9)

I felt like it is my responsibility to moderate chatroom.

  10. (10)

I felt that the presence of the narrative affected the atmosphere of the
chatroom.