From Niche to Mainstream: Community Size and Engagement in Social Media Conversations
- URL: http://arxiv.org/abs/2501.12076v1
- Date: Tue, 21 Jan 2025 12:03:51 GMT
- Title: From Niche to Mainstream: Community Size and Engagement in Social Media Conversations
- Authors: Jacopo Nudo, Matteo Cinelli, Andrea Baronchelli, Walter Quattrociocchi,
- Abstract summary: This study analyzes user behavior across six social media platforms over 33 years.
Our findings reveal that smaller platforms foster richer, more sustained interactions, while larger platforms drive broader but shorter participation.
These findings show an interdependence between platform architecture, user engagement, and community dynamics, shedding light on how digital ecosystems shape the structure and quality of public discourse.
- Score: 0.0
- License:
- Abstract: The architecture of public discourse has been profoundly reshaped by social media platforms, which mediate interactions at an unprecedented scale and complexity. This study analyzes user behavior across six platforms over 33 years, exploring how the size of conversations and communities influences dialogue dynamics. Our findings reveal that smaller platforms foster richer, more sustained interactions, while larger platforms drive broader but shorter participation. Moreover, we observe that the propensity for users to re-engage in a conversation decreases as community size grows, with niche environments as a notable exception, where participation remains robust. These findings show an interdependence between platform architecture, user engagement, and community dynamics, shedding light on how digital ecosystems shape the structure and quality of public discourse.
Related papers
- Characterizing the Fragmentation of the Social Media Ecosystem [39.58317527488534]
We use a dataset of 126M URLs posted by nearly 6M users on nine social media platforms.
We find a clear separation between mainstream and alt-tech platforms.
These findings outline the main dimensions defining the fragmentation and polarization of the social media ecosystem.
arXiv Detail & Related papers (2024-11-25T18:45:03Z) - OASIS: Open Agent Social Interaction Simulations with One Million Agents [147.00696959981173]
We propose a scalable social media simulator based on real-world social media platforms.
OASIS supports large-scale user simulations capable of modeling up to one million users.
We replicate various social phenomena, including information spreading, group polarization, and herd effects across X and Reddit platforms.
arXiv Detail & Related papers (2024-11-18T13:57:35Z) - Characterizing User Archetypes and Discussions on Scored.co [0.6321194486116923]
We present a framework for characterizing nodes and hyperedges in social hypernetworks.
We focus on the understudied alt-right platform Scored.co.
Our findings highlight the importance of higher-order interactions in understanding social dynamics.
arXiv Detail & Related papers (2024-07-31T17:18:25Z) - AI Chat Assistants can Improve Conversations about Divisive Topics [3.8583005413310625]
We present results of a large-scale experiment that demonstrates how online conversations can be improved with artificial intelligence tools.
We employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood in conversations.
We find that these interventions improve the reported quality of the conversation, reduce political divisiveness, and improve the tone, without systematically changing the content of the conversation or moving people's policy attitudes.
arXiv Detail & Related papers (2023-02-14T06:42:09Z) - Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A
Survey [71.43956423427397]
We aim to identify the nonverbal cues and computational methodologies resulting in effective performance.
This survey differs from its counterparts by involving the widest spectrum of social phenomena and interaction settings.
Some major observations are: the most often used nonverbal cue, computational method, interaction environment, and sensing approach are speaking activity, support vector machines, and meetings composed of 3-4 persons equipped with microphones and cameras, respectively.
arXiv Detail & Related papers (2022-07-20T13:37:57Z) - Yourfeed: Towards open science and interoperable systems for social
media [1.8623205938004257]
Existing social media platforms make it incredibly difficult for researchers to conduct studies on social media.
To close the gap, we introduce Yourfeed, a research tool for conducting ecologically valid social media research.
arXiv Detail & Related papers (2022-07-15T13:49:51Z) - 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) - Automatic Evaluation and Moderation of Open-domain Dialogue Systems [59.305712262126264]
A long standing challenge that bothers the researchers is the lack of effective automatic evaluation metrics.
This paper describes the data, baselines and results obtained for the Track 5 at the Dialogue System Technology Challenge 10 (DSTC10)
arXiv Detail & Related papers (2021-11-03T10:08:05Z) - 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) - Echo Chambers on Social Media: A comparative analysis [64.2256216637683]
We introduce an operational definition of echo chambers and perform a massive comparative analysis on 1B pieces of contents produced by 1M users on four social media platforms.
We infer the leaning of users about controversial topics and reconstruct their interaction networks by analyzing different features.
We find support for the hypothesis that platforms implementing news feed algorithms like Facebook may elicit the emergence of echo-chambers.
arXiv Detail & Related papers (2020-04-20T20:00:27Z) - I Know Where You Are Coming From: On the Impact of Social Media Sources
on AI Model Performance [79.05613148641018]
We will study the performance of different machine learning models when being learned on multi-modal data from different social networks.
Our initial experimental results reveal that social network choice impacts the performance.
arXiv Detail & Related papers (2020-02-05T11:10:44Z)
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.