在线行为与离线影响：在线社交网络如何影响在线和离线用户行为
Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior
摘要：
许多当今最广泛使用的计算应用利用社交网络特征，并且允许用户连接、彼此关注、共享内容以及评论其他人的帖子。然而，尽管这些功能被广泛采用，但人们对社交网络对用户保留、参与、在线和离线行为的影响却知之甚少。在这里，我们研究了社交网络如何影响用户的行为，在身体活动跟踪应用程序。我们分析了5年来600万用户的7.91亿次线上和线下活动，结果表明社交网络导致用户的线上和线下活动显著增加。具体来说，我们建立了一个因果关系如何社交网络影响用户的行为。我们发现，创建新的社交连接会使用户在线应用程序内活动增加30%，用户保留率增加17%，用户离线现实世界的身体活动增加7%（每天约400步）。通过利用一个自然的实验，我们区分了新的社会联系的社会影响力的效果，同时增加用户使用应用程序的动机，并采取更多的步骤。我们发现，社会影响力占55%的用户行为的观察到的变化，而其余的45%可以解释为用户的动机增加使用的应用程序。此外，我们表明，随后，在社交网络中的个人边缘形成导致显着增加日常步骤。这些影响随着每个附加边而减小，并且基于边属性和用户人口统计而变化。最后，我们利用这些见解来开发一个模型，准确地预测哪些用户将受到新的社交网络连接的影响。
英文摘要：
Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user's increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
