Core time mechanism for managing large-scale internet-based discussions on COLLAGREE

Nishdia, Tomohiro; Ito, Takayuki; Ito, Takanori; Hideshima, Eizo; Fukamachi, Shunpei; Sengoku, Akihisa; Sugiyama, Yumika

doi:10.1109/agents.2017.8015299

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…An open web-based forum called "COLLAGREE," designed in 2013, supports online discussions with features such as (human) facilitator support, a point-ranking system to incentivize participation, and hierarchical post organization [19]. Various studies [20]- [23] build upon this research and conduct social experiments to evaluate the system's acceptability [24]- [28]. Simultaneously, some researchers [29], [30] contended that relying solely on human facilitators introduced challenges such as decision-making burdens and inherent facilitation biases.…”

Section: Related Workmentioning

confidence: 99%

Conversational AI as a Facilitator Improves Participant Engagement and Problem-Solving in Online Discussion: Sharing Evidence from Five Cities in Afghanistan

SAHAB,

HAQBEEN,

ITO

2024

IEICE Trans. Inf. & Syst.

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Despite the increasing use of conversational artificial intelligence (AI) in online discussion environments, few studies explore the application of AI as a facilitator in forming problem-solving debates and influencing opinions in cross-venue scenarios, particularly in diverse and war-ravaged countries. This study aims to investigate the impact of AI on enhancing participant engagement and collaborative problem-solving in online-mediated discussion environments, especially in diverse and heterogeneous discussion settings, such as the five cities in Afghanistan. We seek to assess the extent to which AI participation in online conversations succeeds by examining the depth of discussions and participants' contributions, comparing discussions facilitated by AI with those not facilitated by AI across different venues. The results are discussed with respect to forming and changing opinions with and without AI-mediated communication. The findings indicate that the number of opinions generated in AI-facilitated discussions significantly differs from discussions without AI support. Additionally, statistical analyses reveal quantitative disparities in online discourse sentiments when conversational AI is present compared to when it is absent. These findings contribute to a better understanding of the role of AI-mediated discussions and offer several practical and social implications, paving the way for future developments and improvements.

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Section: Related Workmentioning

confidence: 99%

Conversational AI as a Facilitator Improves Participant Engagement and Problem-Solving in Online Discussion: Sharing Evidence from Five Cities in Afghanistan

SAHAB,

HAQBEEN,

ITO

2024

IEICE Trans. Inf. & Syst.

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“…In 2016, we conducted in a large-scale experiment with local governments in Aichi Prefecture [13] (AICHI DESIGN LEAGUE 2016). In this experiment, we verified the core time mechanism that provides the time settings for the facilitator and the participants to gather and discuss.…”

Section: Aichi Design League 2016mentioning

confidence: 99%

Towards Agent-based Large-scale Decision Support System: The Effect of Facilitator

Ito¹

2018

Proceedings of the 51st Hawaii International Conference on System Sciences

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“…Argument structure extraction has a broad application in many fields [ 2 ] . F or i n s tance, largescale online discussions on the Web such as D-Agree [3] [4] and Slack usually require to process argument structure extraction for efficient d i s cussion. To c onstruct a t ree-like discussion structure, argument structure extraction majorly has two types of tasks : node classification and link prediction [5] [ 6].…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks for Link Prediction in Argument Structure Extraction

Ding

Ito²

2023

IIAI Letters on Informatics and Interdisciplinary Research

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Argument structure extraction is a fundamental problem in argumentation mining which aims at automatically constructing arguments from unstructured textual documents. Re-garding each argument as a node, then predicting relationships of those nodes is a key task for argument structure extraction, which is called link prediction. Graph neural networks (GNN), as an efficient approach to cope with graphical data, have shown good performances on link prediction tasks. However, they usually focus on predicting whether parts of links really exist in one argument graph, rather than predicting existence of the links between any two nodes, though it is more practical in real applications of argument mining. Thus, in this paper our goal is to predict links between any two nodes given only nodes information. This task is more difficult than the traditional link prediction tasks since the prediction scale exponentially increases with node number. We propose a GNN based model for link predic-tion which outputs the embedding of all the nodes. Then, the probability of link existence between any two nodes is calculated by their embedding results. Finally, we use a dataset of essay comments to perform evaluations and the results confirm the effectiveness of our proposed method.

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Core time mechanism for managing large-scale internet-based discussions on COLLAGREE

Cited by 11 publications

References 2 publications

Conversational AI as a Facilitator Improves Participant Engagement and Problem-Solving in Online Discussion: Sharing Evidence from Five Cities in Afghanistan

Conversational AI as a Facilitator Improves Participant Engagement and Problem-Solving in Online Discussion: Sharing Evidence from Five Cities in Afghanistan

Towards Agent-based Large-scale Decision Support System: The Effect of Facilitator

Graph Convolutional Networks for Link Prediction in Argument Structure Extraction

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