An Agent that Facilitates Crowd Discussion

Abstract

Online discussion platforms are perceived as the next-generation method of citizen involvement. Such platforms can collect, integrate, and synthesize opinions to achieve social good. Crowd-scale platforms are being developed and deployed in social experiments that involve citizens and local governments. In such platforms, human facilitation is often used to preserve the quality of the discussions. Human facilitators often face difficulties when the discussions grow in size. In this paper, we present “D-agree,” a crowd-scale discussion support system based on an automated facilitation agent. The agent extracts discussion structures from online discussions, analyzes them, and posts facilitation messages. We conducted small- and large-scale social experiments in Japan to assess the social impact of the platform. The results showcase the success of our automated facilitation agents in gathering valuable opinions from citizens. In addition, our experiments highlight the effect of an automated facilitation agent on online discussions. In particular, we find that combining the agent facilitator with human facilitators leads to higher user satisfaction.

Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

“…Besides completing a burdensome amount of work before, during, and after the collaboration (Vivacqua et al 2011 ), facilitators must also evince particular character and behavioral traits (Dissanayake et al 2015a ). Training and experience (Clawson and Bostrom 1996 ), appearance and behavior within a group (Franco and Nielsen 2018 ; Ito et al 2021 ; McCardle-Keurentjes and Rouwette 2018 ), and the handling of feedback and reflection (Azadegan and Kolfschoten 2014 ; de Vreede et al 2002 ) play an essential role here. Thereby, facilitators maintain a delicate balance between situations in which they moderate and observe the group and instances in which they intervene – for instance, due to content-related issues (Khalifa et al 2002 ) – without compromising the outcome of the exercise (Dissanayake et al 2015b ).…”

“…We refer to these as AI-augmented facilitation systems, which could have a vast impact on team collaboration (Maedche et al 2019;Seeber et al 2018Seeber et al , 2020. For instance, Derrick et al (2013) and Ito et al (2021) propose the first results of conversational AI capable of issuing instructions to team members or responding to workers' contributions. Further inspired by the widespread application of AI (Dwivedi et al 2021;Wilson and Daugherty 2018), researchers have also begun to explore more specifically AI's potential use in crowdsourcing facilitation (de Vreede and Briggs 2018; Rhyn and Blohm 2017;Tavanapour and Bittner 2018a).…”

“…AI carries various functionalities, including text mining or natural language processing, that can support macro-task crowdsourcing facilitation. For instance, intelligent conversational agent systems could guide the crowd through the crowdsourcing process (Derrick et al 2013;Ito et al 2021) or issue detailed instructions to crowd workers in the form of specific tasks (Qiao et al 2018). The evaluation of the workers' contributions could also be drastically simplified by designing appropriate systems that leverage the potential of text mining and natural language generation to automatically generate reports or summaries (Füller et al 2021;Rhyn et al 2020).…”

Towards Hypedermocracy: Case Studies on an Agent-powered Online Discussion Support Systems

“…In addition, the post-questionnaire showed that users' satisfaction with the discussions facilitated by the AI agent was almost the same as that facilitated by a human, and the satisfaction level was above the average. For details, please refer to the paper [3]. D-Agree is also widely used in Afghanistan.…”

Graph Convolutional Networks for Link Prediction in Argument Structure Extraction

“…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].…”