This is the source code for the prototype system CoArgue, a tool that supports lurkers on Community-based QA (CQA) Platforms in making contributions. The system is part of a research paperwork with the above name, which is accepted by CHI 2023.
You are welcome to cite the paper if you find it insightful.
In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by mark- ing their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot fea- tures to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.
The contribution after submitting the post is evaluated by serveral NLP models. Refer to this link to create the evaluation APIs with your own model and update the API URLs at the very beginning of main.js in the "frontend" folder.
The essential code and settings are contained in the "chatbot" folder.
To train and run a chatbot model, refer to the official Rasa documentation here.
To connect the running chatbot model to the frontend chatbot widget, follow the instructions in this link.
Simply open the index.html in the "frontend" folder to run the website.
It contains the augmented information for two question threads. To switch between the two threads, provide the URL param question=bitcoin or question=car. The system defaults to bitcoin if not provided.
It also contains the baseline version, which is no more than a port of plain Quora website, with UI element aligned. To switch to the baseline version, do git switch baseline.
@inproceedings{10.1145/3544548.3580932,
author = {Liu, Chengzhong and Zhou, Shixu and Liu, Dingdong and Li, Junze and Huang, Zeyu and Ma, Xiaojuan},
title = {CoArgue : Fostering Lurkers’ Contribution to Collective Arguments in Community-Based QA Platforms},
year = {2023},
isbn = {9781450394215},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3544548.3580932},
doi = {10.1145/3544548.3580932},
abstract = {In Community-Based Question Answering (CQA) platforms, people can participate in discussions about non-factoid topics by marking their stances, providing premises, or arguing for the opinions they support, which forms “collective arguments”. The sustainable development of collective arguments relies on a big contributor base, yet most of the frequent CQA users are lurkers who seldom speak out. With a formative study, we identified detailed obstacles preventing lurkers from contributing to collective arguments. We consequently designed a processing pipeline for extracting and summarizing augmentative elements from question threads. Based on this we built CoArgue, a tool with navigation and chatbot features to support CQA lurkers’ motivation and ability in making contributions. Through a within-subject study (N=24), we found that, compared to a Quora-like baseline, participants perceived CoArgue as significantly more useful in enhancing their motivation and ability to join collective arguments and found the experience to be more engaging and productive.},
booktitle = {Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems},
articleno = {271},
numpages = {17},
keywords = {Collective Arguments, Lurker Support, CQA Platforms},
location = {Hamburg, Germany},
series = {CHI '23}
}