使用个人资料信息预测Twitter上粉丝数量的上升
Predicting Rising Follower Counts on Twitter Using Profile Information
摘要：
当评估一个人在Twitter上受欢迎的原因时，有一件事被认为是主要的驱动力：许多推文。关于应该发布什么样的推文，人们存在争议，但除了推文之外就没什么了。特别令人感兴趣的是每个Twitter用户的个人资料页面提供的信息。其中一个特点是这些配置文件上的名字。对心理学和经济学的研究发现了名字与以下因素的相关性：一个人的学校成绩或在美国获得面试机会。因此，我们感兴趣的是这些配置文件信息对追随者数量的影响。我们通过分析大约600万Twitter用户的个人资料来解决这个问题。所有配置文件分为三组：在名称字段中有名字、英语单词或两者都没有的用户。假设是名称和单词影响用户的可重复性，并随后影响他/她的追随者数量。我们提出了一个分类器，标签用户将增加他们的追随者计数在一个月内，通过应用不同的模型，基于用户的组。用受试者操作曲线得分下的面积评估分类器，并获得高于0.800的得分。
英文摘要：
When evaluating the cause of one's popularity on Twitter, one thing is considered to be the main driver: Many tweets. There is debate about the kind of tweet one should publish, but little beyond tweets. Of particular interest is the information provided by each Twitter user's profile page. One of the features are the given names on those profiles. Studies on psychology and economics identified correlations of the first name to, e.g., one's school marks or chances of getting a job interview in the US. Therefore, we are interested in the influence of those profile information on the follower count. We addressed this question by analyzing the profiles of about 6 Million Twitter users. All profiles are separated into three groups: Users that have a first name, English words, or neither of both in their name field. The assumption is that names and words influence the discoverability of a user and subsequently his/her follower count. We propose a classifier that labels users who will increase their follower count within a month by applying different models based on the user's group. The classifiers are evaluated with the area under the receiver operator curve score and achieves a score above 0.800.
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。
