用户可以预测她的追随者想要什么吗？
Can A User Anticipate What Her Followers Want?
摘要：
每当社交媒体用户决定分享一个故事时，她通常很高兴收到喜欢，评论，分享，或者更普遍的是，来自她的追随者的反馈。因此，她可能会感到被迫使用她收到的反馈来（重新）估计她的追随者的偏好，并决定接下来分享哪些故事以获得更多（积极）反馈。在什么条件下，她能成功？在这项工作中，我们首先从理论的角度来研究这个问题，然后提供一套实用的算法来识别和描述社交媒体中的这种行为。更具体地说，我们解决上述问题的顺序决策和效用最大化的观点。对于各种各样的效用函数，我们首先表明，要成功，用户需要积极权衡剥削-分享故事，导致更多的（积极的）反馈-和探索-分享故事，以了解她的追随者的偏好。然而，如果用户除了利用她接收的反馈之外还利用她的追随者提供给其他用户的反馈，则探索是不必要的。然后，我们开发了一个效用估计框架的观察数据，它依赖于统计假设检验，以确定用户是否利用她从她的每个追随者收到的反馈来决定下一步要发布什么。合成数据上的实验说明了我们的理论研究结果，并表明我们的估计框架能够准确地恢复用户的潜在效用函数。在从Twitter和Reddit收集的几个真实数据集上进行的实验表明，在我们的数据集中，高达82%（43%）的Twitter（Reddit）用户确实使用他们收到的反馈来决定下一步发布什么。
英文摘要：
Whenever a social media user decides to share a story, she is typically pleased to receive likes, comments, shares, or, more generally, feedback from her followers. As a result, she may feel compelled to use the feedback she receives to (re-)estimate her followers' preferences and decides which stories to share next to receive more (positive) feedback. Under which conditions can she succeed? In this work, we first look into this problem from a theoretical perspective and then provide a set of practical algorithms to identify and characterize such behavior in social media. More specifically, we address the above problem from the viewpoint of sequential decision making and utility maximization. For a wide variety of utility functions, we first show that, to succeed, a user needs to actively trade off exploitation-- sharing stories which lead to more (positive) feedback--and exploration-- sharing stories to learn about her followers' preferences. However, exploration is not necessary if a user utilizes the feedback her followers provide to other users in addition to the feedback she receives. Then, we develop a utility estimation framework for observation data, which relies on statistical hypothesis testing to determine whether a user utilizes the feedback she receives from each of her followers to decide what to post next. Experiments on synthetic data illustrate our theoretical findings and show that our estimation framework is able to accurately recover users' underlying utility functions. Experiments on several real datasets gathered from Twitter and Reddit reveal that up to 82% (43%) of the Twitter (Reddit) users in our datasets do use the feedback they receive to decide what to post next.
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。
