数字时代的社区塑造:分析在线社交网络话语分裂的时空融合框架

Amirhossein Dezhboro, Jose Emmanuel Ramirez-Marquez, Aleksandra Krstikj
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引用次数: 0

摘要

本研究提出了一个分析社交媒体平台中在线社区动态的框架,利用了文本和网络数据的时间融合。通过结合文本分类和动态社交网络分析,我们揭示了驱动社区形成和演变的机制,揭示了现实世界事件的影响。我们引入了 14 个基于社会科学理论的关键要素来评估社交媒体动态,并通过对 2020 年美国重大事件期间 Twitter 数据的案例研究来验证我们的框架。我们的分析以歧视话语为中心,将性别主义、种族主义、仇外心理、能力主义、仇视同性恋和宗教不容忍作为主要片段。分析结果表明,代表话语片段的社区出现和解散周期很快。我们揭示了现实世界的环境如何影响话语的主导地位,以及社交媒体如何促进echo chamber 的形成和社会极化。我们的综合方法提供了对网络社区的话语碎片、舆论动态和结构方面的见解,为理解网络互动与社会趋势之间复杂的相互作用提供了方法论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks
This research presents a framework for analyzing the dynamics of online communities in social media platforms, utilizing a temporal fusion of text and network data. By combining text classification and dynamic social network analysis, we uncover mechanisms driving community formation and evolution, revealing the influence of real-world events. We introduced fourteen key elements based on social science theories to evaluate social media dynamics, validating our framework through a case study of Twitter data during major U.S. events in 2020. Our analysis centers on discrimination discourse, identifying sexism, racism, xenophobia, ableism, homophobia, and religious intolerance as main fragments. Results demonstrate rapid community emergence and dissolution cycles representative of discourse fragments. We reveal how real-world circumstances impact discourse dominance and how social media contributes to echo chamber formation and societal polarization. Our comprehensive approach provides insights into discourse fragmentation, opinion dynamics, and structural aspects of online communities, offering a methodology for understanding the complex interplay between online interactions and societal trends.
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