Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Community

• URL: http://arxiv.org/abs/2310.02515v1
• Date: Wed, 4 Oct 2023 01:28:17 GMT
• Title: Community Archetypes: An Empirical Framework for Guiding Research Methodologies to Reflect User Experiences of Sense of Virtual Community
• Authors: Gale H. Prinster, C. Estelle Smith, Chenhao Tan, Brian C. Keegan
• Abstract summary: We interviewed 21 researchers to understand how they study "community" on Reddit. We surveyed 12 subreddits to gain insight into user experiences of SOVC. Some research methods can broadly reflect users' SOVC regardless of the topic or type of subreddit.
• Score: 22.86975187254343
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Humans need a sense of community (SOC), and social media platforms afford opportunities to address this need by providing users with a sense of virtual community (SOVC). This paper explores SOVC on Reddit and is motivated by two goals: (1) providing researchers with an excellent resource for methodological decisions in studies of Reddit communities; and (2) creating the foundation for a new class of research methods and community support tools that reflect users' experiences of SOVC. To ensure that methods are respectfully and ethically designed in service and accountability to impacted communities, our work takes a qualitative, community-centered approach by engaging with two key stakeholder groups. First, we interviewed 21 researchers to understand how they study "community" on Reddit. Second, we surveyed 12 subreddits to gain insight into user experiences of SOVC. Results show that some research methods can broadly reflect users' SOVC regardless of the topic or type of subreddit. However, user responses also evidenced the existence of five distinct Community Archetypes: Topical Q&A, Learning & Perspective Broadening, Social Support, Content Generation, and Affiliation with an Entity. We offer the Community Archetypes framework to support future work in designing methods that align more closely with user experiences of SOVC and to create community support tools that can meaningfully nourish the human need for SOC/SOVC in our modern world.

Related papers

• The Future of Open Human Feedback [65.2188596695235]
  We bring together interdisciplinary experts to assess the opportunities and challenges to realizing an open ecosystem of human feedback for AI. We first look for successful practices in peer production, open source, and citizen science communities. We end by envisioning the components needed to underpin a sustainable and open human feedback ecosystem.
  arXiv  Detail & Related papers  (2024-08-15T17:59:14Z)
• COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities [5.0261645603931475]
  We introduce Community-Cross-Instruct, an unsupervised framework for aligning large language models to online communities to elicit beliefs. We demonstrate the method's utility in accurately representing political and diet communities on Reddit.
  arXiv  Detail & Related papers  (2024-06-17T20:20:47Z)
• SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations [56.31816995795216]
  Socially-Aware Dialogue Assistant System (SADAS) is designed to ensure that conversations unfold with respect and understanding. Our system's novel architecture includes: (1) identifying the categories of norms present in the dialogue, (2) detecting potential norm violations, (3) evaluating the severity of these violations, and (4) implementing targeted remedies to rectify the breaches.
  arXiv  Detail & Related papers  (2024-01-29T08:54:21Z)
• This Must Be the Place: Predicting Engagement of Online Communities in a Large-scale Distributed Campaign [70.69387048368849]
  We study the behavior of communities with millions of active members. We develop a hybrid model, combining textual cues, community meta-data, and structural properties. We demonstrate the applicability of our model through Reddit's r/place a large-scale online experiment.
  arXiv  Detail & Related papers  (2022-01-14T08:23:16Z)
• It is rotating leaders who build the swarm: social network determinants of growth for healthcare virtual communities of practice [0.0]
  The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis.
  arXiv  Detail & Related papers  (2021-05-26T16:15:31Z)
• Can crowdsourcing rescue the social marketplace of ideas? [1.936291271591564]
  Facebook and Twitter recently announced community-based review platforms to address misinformation. We provide an overview of the potential affordances of such community-based approaches to content moderation based on past research and preliminary analysis of Twitter's Birdwatch data. We call for multidisciplinary research utilizing methods from complex systems studies, behavioural sociology, and computational social science to advance the research on crowd-based content moderation.
  arXiv  Detail & Related papers  (2021-04-28T13:35:28Z)
• Advances and Challenges in Conversational Recommender Systems: A Survey [133.93908165922804]
  We provide a systematic review of the techniques used in current conversational recommender systems (CRSs) We summarize the key challenges of developing CRSs into five directions. These research directions involve multiple research fields like information retrieval (IR), natural language processing (NLP), and human-computer interaction (HCI)
  arXiv  Detail & Related papers  (2021-01-23T08:53:15Z)
• CASS: Towards Building a Social-Support Chatbot for Online Health Community [67.45813419121603]
  The CASS architecture is based on advanced neural network algorithms. It can handle new inputs from users and generate a variety of responses to them. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support.
  arXiv  Detail & Related papers  (2021-01-04T05:52:03Z)
• Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents [60.27066549589362]
  Social language used by human agents is associated with greater users' responsiveness and task completion. The model uses a sequence-to-sequence deep learning architecture, extended with a social language understanding element. Evaluation in terms of content preservation and social language level using both human judgment and automatic linguistic measures shows that the model can generate responses that enable agents to address users' issues in a more socially appropriate way.
  arXiv  Detail & Related papers  (2020-12-29T08:22:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.