# Awareness Support with Mutual Stimulation Among People to Enrich Group Discussion in AIR-VAS

Mamoru Yoshizoe, Hiromitsu Hattori

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Mamoru Yoshizoe, Hiromitsu Hattori. Awareness Support with Mutual Stimulation Among People to Enrich Group Discussion in AIR-VAS. IFIP World Conference on Computers in Education (WCCE), Aug 2022, Hiroshima, Japan. pp.379-388, 10.1007/978-3-031-43393-1_35. hal-05533293

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# Awareness Support with Mutual Stimulation Among People to Enrich Group Discussion in AIR-VAS

Mamoru Yoshizoe(✉) and Hiromitsu Hattori

Ritsumeikan University, 1-1-1 Noji-higashi, Kusatsu, Shiga, Japan
{yoshizoe,hatto}@fc.ritsumei.ac.jp

**Abstract.** While the Internet has made it possible for people all over the world to be connected, they continue to face the barrier of value diversity. Although we often have to consider or respect other people's values, it is not easy to sense them due to the limitations of our knowledge, experience, and imagination. We tackle this issue by developing AIR-VAS, a discussion support system that supports the mutual consideration of unfamiliar values and thus encourage synergistic communication. AIR-VAS is a group discussion system that supports awareness of other people's values. AIR-VAS can recognize characteristic opinions raised in a session and share them among all discussion participants. Through the sharing of opinions, people can obtain different viewpoints on the issues currently being discussed so AIR-VAS can stimulate people to generate/evaluate/analyze ideas. AIR-VAS has the function of visualizing statements presented during a discussion in the form of a word co-occurrence network. We realized opinion sharing as the process of network re-construction, including the use of sub-networks to represent the opinions of other groups. Experiments show that the system successfully enhances the diversity of ideas.

**Keywords:** Discussion support · Idea generation support · Awareness · Word co-occurrence network · Summarization

## 1 Introduction

Society is becoming more globalized and complex with the development of information technology, and people's values are becoming more diverse. As a result, it has become necessary for society to be aware of diverse and changing values, and to have a broad perspective on all issues.

A wide variety of information is now being exchanged on the Internet, and anyone can easily refer to and post such information. However, this has led to the frequent occurrence of “flaming” [1] on social networking services (SNS), such as Twitter, where people post their thoughts and feelings based on certain values only to find that they are criticized and pilloried by many viewers. In addition, with the development of the Web and social networking services, Internet users
are exposed only to information that matches their interests and the communities to which they belong or have close relationships, which isolates them from information that differs from their point of view and makes them isolated in a homogeneous cultural and ideological bubble. This problem, known as the “filter bubble” [2], continues to become stronger.

These problems suggest that we may lose the opportunity to be exposed to a variety of information and that we may not be able to perceive things from a broad perspective. To prevent such a situation, it is necessary to recognize ideas that differ from our own ideas. To achieve this goal, we believe that technologies and mechanisms are necessary to gain awareness of diverse viewpoints in various situations. In addition, we think that awareness of new perspectives helps to support the generation of new ideas and encourage new ways of perceiving others.

Our research group (AIR: Acceptable Intelligence with Responsibility) has been discussing processes and approaches to make people aware of various viewpoints and has been trying to implement and verify a prototype system that incorporates such mechanisms.

To advance this research, we designed and implemented a system that promotes ideas by presenting new perspectives in the discussions. Specifically, we assume a frequent situation in workshops where participants are divided into several groups and discuss the same topic in parallel. By making it possible to refer to information from parallel discussions that cannot normally be accessed easily, we aim to bring the perspectives of many other participants into each group's discussion, thereby stimulating and improving the overall quality of the discussion. In this paper, we detail the design of the system and evaluate its effectiveness in an experiment.

## 2 Related Work

Various methods for supporting idea generation have been developed, such as brainstorming [3] and brainwriting [4], and various extent systems incorporate these methods [5–7]. These systems visualize the content of the discussion as labels and catch phrases on the screen, and share them with group members to help organize the keywords and main points.

Research on visual-based idea support systems has led to many studies focused on “Awareness” [8–10]. Dourish [8, p. 107] defines awareness as “awareness is an understanding of the activities of others, which provides a context for your own activity”. For example, displaying other people’s information on a screen or working on a virtual shared screen [11–13] are attempts to support “awareness” based on Dourish’s definition. Awareness is a major theme in idea generation support systems, and it has been shown that better awareness of team activities and events that may affect the team can lead to increased team productivity [14]. Robert investigated how awareness of others affects the search task and showed that being aware of the behavior of other users can lead to improved performance and reduced effort in the search task. Wikum+ [15] is a collaborative document writing tool that allows groups working together to
discuss and summarize the issues in a single space. In Wikum+ experiments, the system yielded a reduction in user effort, and the integration of new ideas.

However, these discussion support systems visualize the status of discussions in a group and assume support for discussions within just that group. These systems do not support awareness beyond the group, a key function of the proposal made in our research. The proposed system allows interaction among groups in real-time and improves the quality of the overall workshop discussion through interaction differs from the usual group-discussion style workshop in which people share characteristic viewpoints and opinions of each group when the group discussion is over. The originality of this system is that it improves the quality of discussion in the workshop as a whole through real-time interaction between groups.

# 3 AIR-VAS System

## 3.1 System Design

We present the AIR-VAS system that supports idea generation and awareness of diverse values via intuitive presentation of other people's information. We targeted a framework that could provide a new perspective by sharing discussion information among groups beyond the borders of discussion groups. By receiving idiosyncratic information from other groups in real-time, we can expect the people to recognize diverse viewpoints and ideas, so that the discussion broadens and deepens by taking account of various values and factors surrounding the discussion theme.

In a group-work style discussion where participants are divided into several groups and discuss the same topic in parallel, the system visualizes the status of the discussion in each group and use the information of other groups to stimulate the entire discussion. AIR-VAS aims to exchange the perspectives of the groups and so improve the final quality and efficacy of the discussion. Sharing information raised in different groups and communities, the participants are challenged to expand their perspectives. In addition, by incorporating the perspectives of other groups, we can expect development of new unexpected visions, which may lead to more efficient workshops. The system will also contribute to the creation of learning opportunities in school education and corporate employee training, where people will learn to recognize other people's viewpoints, respect them, and learn the skills to incorporate them into their activities.

The system architecture is shown in Fig. 1, while Fig. 2 shows a UI screen. Each group has a PC and microphone. The system visualizes information extracted from each group's discussion speech text, forms group-specific summaries, and displays them on the appropriate screen. For visualizing the information, we adopt the co-occurrence network method [16]. Word co-occurrence networks are a method of expressing co-occurrence relationships between words in terms of network connections, and they offer effective discernment of the structure and features of text data [17]. Brief explanations of each modules in Fig. 1 are given here.
<figure><img src="image_4.png" /><figcaption>Fig. 1. The AIR-VAS system architecture.</figcaption></figure>

**Text Input Module**

The module that enters the discussion text into the database. Web Speech API<fnref n="1" /> is used for speech recognition.

**Word Co-occurrence Analysis Module**

The module that analyzes the co-occurrence relationship of the words in the database.

**Co-occurrence Network Generation Module**

The module that generates the co-occurrence network diagram from the word co-occurrence data of top 50 frequent words and stimulus word data.

**Stimulus Information Extraction Module**

The module that extracts the other group's discussion text data from the database.

**Text Summarization Module**

The module that summarizes the other group's discussion text by using a Transformers-based summarization model.

**Visual Information Integration Module**

The module that integrates the visual information to create the browser display.

<footnote n="1">¹ https://developer.mozilla.org/ja/docs/Web/API/Web_Speech_API.</footnote>
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** The AIR-VAS system UI. (1) Speech recognition button. (2) Network update stop button. (3) Co-occurrence network view. Users can scroll and zoom. (4) Node color definitions; green shows positive, red shows negative, yellow shows stimulus words. (5) The selected node shows speech text in which the word is used; The stimulus word represents other group's summary. (6) Network stabilize button. (Color figure online)</figcaption></figure>

## 3.2 Word Co-occurrence Network

The Word Co-occurrence Analysis Module calculates the co-occurrence of words. We use the Jaccard index [18] for this calculation. The Jaccard index of two words $w_1, w_2$ is calculated as shown by formula (1).

$$ Jaccard(w_1, w_2) = \frac{|V_1 \cap V_2|}{|V_1 \cup V_2|} \qquad (1) $$

$V_1, V_2$ includes co-occurrence words of $w_1, w_2$.

The Word Co-occurrence Analysis Module inputs word co-occurrence data to the Co-occurrence Network Generation Module, and then, the co-occurrence network diagram is made from frequent word data and stimulus word data (Fig. 2 (3)). The co-occurrence network diagram is displayed by javascript package vis.js<fnref n="2" /> on the browser. Figure 2 (1) is the button for speech recognition and Fig. 2 (2) (6) are network function buttons. Users can check the network using zoom-in-out and stop physical network simulation. We set three node colors so the network diagram can offer intuitive recognition of the discussion (Fig. 2 (4)). The green node shows positive feelings, the red node shows negative feelings, the yellow node shows stimulus words, while other nodes are default white. We

<footnote n="2">² https://visjs.org/.</footnote>
defined the emotional polarity of positive or negative by applying the Polar Phrase Dictionary [19] and the opinion lexicon<fnref n="3" /> of Hu and Liu [20].

## 3.3 Stimulus Information

The system displays the other group's word and discussion summary as stimulus information. The *Stimulus Information Extraction Module* picks up other group's discussion text from the database and defines the target text to be summarized. In this system, we set the discussion text of the last 5 min as the target part (left of Fig. 3). The discussion text of the target part is input to the *Text Summarization Module* which then outputs a summary of the other group's discussion (Fig. 2 (5)). This extraction of stimulus information is iterated at 5 min intervals. Therefore, if the discussion takes 20 min, stimulus information is created at 5, 10, and 15 min.

For text summarization, we use the abstractive summarization model of T5 [21]. T5 is a pre-trained model for language generation, translation, and comprehension. It has high performance on many generation tasks, including summarization and abstractive question answering. We adopt the generative model T5⁴, and fine-tune it for the short text summarization task [22]. We use this fine-tuned model for summarization in the *Text Summarization Module*. The model outputs a summary of the discussion text, and a word is selected as the stimulus word; this word is not been added to the co-occurrence network (right of Fig. 3).

<figure><img src="image_6.png" /><figcaption>Fig. 3. Text Summarization Module overview</figcaption></figure>

## 4 Evaluation

We conducted a discussion experiment with the system to examine how stimulus information affects the diversity of the discussion and the awareness of diverse

<footnote n="3">³ http://www.cs.uic.edu/~liub/FBS/opinion-lexicon-English.rar.</footnote>

<footnote n="4">⁴ https://huggingface.co/google/mt5-base.</footnote>
values and perspectives. The discussion was undertaken as a Zoom online meeting. We recruited 16 college students for the experiment, and formed four groups of four people. Two groups (G1, G2) could refer to stimulus information, while the other two groups (G3, G4) could not. The experiment had two rounds for 20 min, one for each condition. The discussion members were shuffled at the beginning of round 2, so all members have experienced two conditions. In the first round, each group discussed how to improve campus dining options and mobile food stands. In the second round, each group discussed how they would like their lectures to operate if classes continued to be online in the next semester due to COVID-19. In both rounds, we asked each group to write ideas and proposals about the theme posed and answer a questionnaires at the end of the experiment. We compared discussion status and user experiences in both conditions on the point of whether the system's display of information from the other group helped them become aware of diverse values and perspectives.

## 4.1 Activation by Stimulus Information

The users answered a questionnaire on the discussions at the end of the experiment. From the quantitative results in Fig. 4, users felt that stimulus information helped in discovering diverse ideas (Round1: $t(11)= 3.000, p = 0.012$; Round2: $t(10)= 2.494, p = 0.032$) and in satisfaction with the discussion (Round1: $t(10)= 2.236, p = 0.049$; Round2: $t(12)= 2.393, p = 0.034$). The results of the number of ideas that each group wrote in the discussion also reflected the efficacy of the information provided. The groups that could refer to stimulus information had more ideas and proposals than the groups that could not (Table 1).

In the questionnaire responses, many users commented that the stimulus information helped to find new ideas. One user explained “*I was able to get ideas from the stimulus information. I think it had an impact on the discussion that followed.*” Another user said “*I was able to find a new starting point for the conversation from the stimulus words. In particular, the stimulus words seemed to advance the discussion more.*” In addition, some users felt that stimulus information helped when the discussion stalled. One user explained “*I felt the discussion less often because the stimulus words allowed us to look at them and talk about them even after everyone had expressed their opinions*”. Another user said “*I had the impression that the discussion was less likely to get stuck when there was stimulus information.*” On the other hand, one user commented, “*It was difficult to understand how the node word impacted the flow of the discussion*,” and pointed out that it is necessary to present not only the word by itself but also the context of the word.

In Fig. 4, the stimulus information and summary were positively evaluated (high scores) by users. One user said, “The summary of the other group was natural sentences, and it was useful”. Another user said “As for the stimulus information, it was short and coherent and helped to develop the discussion”.
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Box plots showing quantitative values (out of 5). Scores that resulted in different means from *t*-tests are starred ($p < 0.05*$).</figcaption></figure>

<table><caption>**Table 1.** The number of ideas</caption><thead><tr><td>Group</td><td>Condition</td><td>Number of ideas (Round1)</td><td>Number of ideas (Round2)</td></tr></thead><tbody><tr><td>G1</td><td>Stimulus</td><td>11</td><td>6</td></tr><tr><td>G2</td><td>Stimulus</td><td>11</td><td>9</td></tr><tr><td>G3</td><td>No Stimulus</td><td>8</td><td>4</td></tr><tr><td>G4</td><td>No Stimulus</td><td>7</td><td>5</td></tr></tbody></table>

## 4.2 Impact on Discussion Direction

Based on the speech-to-text logs, we investigated how the stimulus information impacted the direction of the discussion.

**Round1.** In round 1, each group discussed how to improve campus dining options and mobile food stands. We found that G3 discussed “*the cafeteria gets crowded during lunchtime...*”, and G2 explained “*it would be convenient to be able to see the menu of the cafeteria and pay via our smartphone.*” The system summarized these discussion texts, “The problem of crowded cafeterias during lunchtime” and “Useful ways to use the smartphone”, and sent the summaries to G1 as stimulus information. G1 received this stimulus information, and they developed a discussion about a smartphone application for checking the crowding of the cafeteria in real-time (left of Fig. 5). In the questionnaires, one user of G1 said “*The stimulus information about “smartphone” gave the discussion a distinctly different orientation from the previous discussions*”.

**Round2.** In round 2, each group discussed how to improve campus education in the environment of COVID-19 restrictions. We found that G3 discussed the video recording of lectures, and the resulting stimulus information increased the discussion scope of G1 and G2. One user of G1 said “*The discussion had been going in the direction of making online classes better, but negative comments from other groups led to a renewed discussion of the advantages and disadvantages of both online and face-to-face classes.*” and another user of G2 explained “*From the stimulus information about recording the lectures, we discussed the effects of recording and measures to counter skipping classes.*” (right of Fig. 5).
<figure><img src="image_8.png" /><figcaption>Fig. 5. The co-occurrence network diagram of round1-G1 (left) and round2-G2 (right).</figcaption></figure>

# 5 Conclusion

In this paper, we presented the AIR-VAS system; it supports idea generation and awareness of diverse values by exchanging summaries of other people's discussions. The system allows users to recognize the ideas and perspectives of other discussion groups in real-time through stimulus information. Using an abstractive summarization model, AIR-VAS allows users to intuitively perceive the contents of another group's discussion. The results of an experiment on an implemented AIR-VAS system showed that stimulus information activated the discussion and gave awareness of different perspectives of the discussion theme. A limitation of this study is that it considers only a single fixed pattern of timing and frequency with which the system presents stimulus information. In future work, we will intend to investigate more variation in the information display parameters and focus on differences in the semantic distance of the contents of the discussion.

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