在线社交网络中相互竞争的流行语的共同扩散建模

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

摘要

在线社交网络极大地促进了各类信息的传播。同时,当今世界信息的丰富性也意味着不同的信息将越来越多地争夺人们有限的注意力。当不同的信息在在线社交网络中共同传播时,为什么有些信息会成为潮流,而另一些信息却没有出现呢?现有研究要么将每条信息的传播单独建模,要么没有考虑用户在在线社交网络中的不活跃情况。本文将每条信息都建模为一个meme,针对这一缺陷,提出了一个统一的模型,用于描述在线社交网络中同时传播的相互竞争的meme的共同扩散。我们首次确定了一个无处不在的竞争记忆阈值。该阈值也是一个有效的预测因子,有助于更好地判断流行语竞争的结果。这项研究的成果对在线活动和动员以及打击错误信息具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the co-diffusion of competing memes in online social networks

Online social networks have greatly facilitated the spread of information of all sorts. Meanwhile, the abundance of information in today's world also means different pieces of information will increasingly compete for people's finite attention. When different pieces of information spread together in an online social network, why would some become trendy while others fail to emerge? Existing research either models the diffusion of each piece of information independently, or fails to consider users' inactivity in online social networks. Modeling each piece of information as a meme, this paper addresses this gap by proposing a unified model for the co-diffusion of competing memes simultaneously spreading across an online social network. We are the first to identify a ubiquitous threshold for competing meme. The threshold also functions as an effective predictor that contributes to better performance in determining the outcome of meme competitions. Outcomes from this study have important implications for online campaigns and mobilizations as well as the fight against misinformation.

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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
期刊介绍: The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).
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