在社交媒体上,行动比语言更响亮

R. Korolov, Justin Peabody, Allen Lavoie, Sanmay Das, M. Magdon-Ismail, W. Wallace
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引用次数: 30

摘要

我们研究了社交媒体(如TWitter)上的聊天水平与观察到的与聊天相关的行为水平之间的关系。例如,在一场灾难中,Twitter上关于救灾捐款的讨论与收到的金额有什么关系?一种假设是,有一小部分采取行动的人也会在推特上谈论这件事,这意味着线性扩展,行动∝闲聊。另一方面,如果存在传染效应(那些关于捐赠的推文煽动其他人捐赠),并且这些被煽动的捐赠者倾向于“安静”,不传播他们的行为,那么我们预计超线性尺度作用∝chatterγ,其中γ > 1。我们使用一个简单的模型表明,社交媒体“追随者”网络的度序列在确定缩放指数γ方面起着关键作用。对于随机图和幂律图,缩放指数等于或接近2(二次放大)。我们利用桑迪飓风后美国各州的位置配对捐赠和社交媒体数据,对模型的预测进行了实证验证。理解将社交媒体上的闲聊与真实的身体行为联系起来的尺度行为,是估计反应程度和确定影响反应的社交媒体策略的重要一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Actions are louder than words in social media
We study the relationship between the level of chatter on a social medium (like TWitter) and the level of the observed actions related to the chatter. For example, in a disaster, how does relief-donation chatter on Twitter correlate with the dollar amount received? One hypothesis is that a fraction of those who act will also tweet about it, which implies linear scaling, action ∝ chatter. On the other hand, if there is a contagion effect (those who tweet about donation incite others to donate) and these incited donors tend to be "quiet" and not broadcast their actions, then we expect superlinear scaling action ∝ chatterγ where γ > 1. We show, using a simple model, that the degree sequence of the social media "follower" network plays a key role in determining the scaling exponent γ. For random graphs and power-law graphs, the scaling exponent is at or near 2 (quadratic amplification). We empirically validate the model's predictions using location-paired donation and social media data from U.S. states after Hurricane Sandy. Understanding the scaling behavior that relates social-media chatter to real physical actions is an important step for estimating the extent of a response and for determining social-media strategies to affect the response.
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