如何请求帮助：利他请求成功的案例研究
How to Ask for a Favor: A Case Study on the Success of Altruistic Requests
摘要：
请求是许多社交媒体系统的核心，如问答网站和在线慈善社区。虽然这些请求的成功对于社区的成功至关重要，但导致社区成员满足请求的因素在很大程度上是未知的。一个请求的成功取决于一些因素，比如谁在请求，他们是如何请求的，他们什么时候请求的，最重要的是请求的内容，从小的帮助到大量的金钱捐赠。我们提出了一个案例研究的利他主义的请求在一个在线社区，所有的请求要求非常相同的贡献，并没有提供任何有形的回报，让我们从文本和社会因素的要求。从社会心理学文献中，我们提取高层次的社会功能，从文本中操作接收者和捐助者之间的社会关系，并证明这些提取的关系是成功的预测。更具体地说，我们发现，通过叙述清楚地传达需求是必不可少的，感谢，证据和广义互惠的语言指示，以及高地位的要求者进一步增加成功的可能性。基于这种理解，我们开发了一个模型，可以预测未知请求的成功，在几个基线上有显着改进。我们将这些发现与心理学中关于帮助行为的研究联系起来，为进一步分析社交媒体系统的成功提供了基础。
英文摘要：
Requests are at the core of many social media systems such as question & answer sites and online philanthropy communities. While the success of such requests is critical to the success of the community, the factors that lead community members to satisfy a request are largely unknown. Success of a request depends on factors like who is asking, how they are asking, when are they asking, and most critically what is being requested, ranging from small favors to substantial monetary donations. We present a case study of altruistic requests in an online community where all requests ask for the very same contribution and do not offer anything tangible in return, allowing us to disentangle what is requested from textual and social factors. Drawing from social psychology literature, we extract high-level social features from text that operationalize social relations between recipient and donor and demonstrate that these extracted relations are predictive of success. More specifically, we find that clearly communicating need through the narrative is essential and that that linguistic indications of gratitude, evidentiality, and generalized reciprocity, as well as high status of the asker further increase the likelihood of success. Building on this understanding, we develop a model that can predict the success of unseen requests, significantly improving over several baselines. We link these findings to research in psychology on helping behavior, providing a basis for further analysis of success in social media systems.
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。
