[论文解读] Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior

Tim Althoff, Pranav Jindal+1 more|arXiv (Cornell University)|Dec 9, 2016

Impact of Technology on AdolescentsSocial Sciences被引用 20

一句话总结

本研究探讨了在一款健身追踪应用中，社交网络特征如何影响用户的线上参与度与线下身体活动。基于600万用户长达5年的自然实验与因果推断分析，发现新社交关系的建立使线上活动提升30%，留存率提高17%，每日步数增加7%（约400步），其中社交影响解释了55%的行为改变。

ABSTRACT

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.

研究动机与目标

• 理解在线社交网络对线上应用参与度与线下身体活动的因果影响。
• 区分社交影响与用户加入社交网络后内在动机提升对行为改变的独立作用。
• 识别在建立新社交关系后最易发生行为改变的用户群体。
• 构建预测模型，识别在形成新社交关系后更可能增加身体活动的用户。
• 使用客观的传感器采集的身体活动数据，避免自报行为数据固有的偏差。

提出的方法

• 利用延迟好友关系形成的自然实验，隔离新社交关系的因果效应。
• 应用计量经济学中的双重差分法与匹配技术，估算社交关系对行为改变的因果影响。
• 使用Azumio Argus应用的大规模数据集，包含600万用户、6.31亿条活动动态及1.6亿天的加速度计步数记录。
• 将自报的在应用内活动（线上行为）与被动步数记录（线下行为）作为独立的结果指标。
• 基于用户人口统计特征、连接前的行为、关系属性及时间模式，构建预测模型，以预测用户对社交影响的敏感性。
• 使用AUC评估模型性能，在预测连接后活动增加方面AUC超过78%。

实验结果

研究问题

• RQ1 新社交网络关系在多大程度上因果性地提升了线上应用活动与线下身体活动？
• RQ2 观察到的行为改变中有多少可归因于社交影响，又有多少可归因于加入网络后内在动机的提升？
• RQ3 哪些用户特征与关系属性可预测用户在建立新社交关系后对行为改变的敏感性？
• RQ4 我们能否准确预测哪些用户在建立新社交关系后会增加身体活动？
• RQ5 社交影响的效果如何随时间变化，以及在不同类型社交关系（如好友与关注）之间是否存在差异？

主要发现

• 新社交关系的建立使线上应用活动提升30%，用户留存率提高17%。
• 新关系带来线下身体活动7%的提升，相当于每天约多400步。
• 社交影响解释了55%的行为改变，其余45%归因于内在动机的提升。
• 后续关系的建立导致每日步数出现短暂且递减的增加，效果随时间衰减。
• 整合了人口统计、连接前行为及关系属性的预测模型，在预测活动增加方面AUC超过78%。
• 预测能力因关系类型与发起方而异，其中由关注者发起的关系预测能力最强，AUC达0.847。