图 书 馆
论社会结构与主题结构的相互作用On the Interplay between Social and Topical Structure |
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| 课程网址: | http://videolectures.net/icwsm2013_tan_topical_structure/ |
| 主讲教师: | Chenhao Tan |
| 开课单位: | 康奈尔大学 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  人们的利益和人们的社会关系在直觉上是相互联系的，但是了解他们之间的相互作用以及他们是否可以帮助彼此预测仍然是一个悬而未决的问题。我们研究了构成在线社交媒体主干的两个决定性结构的接口：与谁连接的社交网络的图结构以及对什么感兴趣的主题从属关系的集合结构。在研究此界面时，我们确定了关键关系，从而可以从另一个角度理解这些结构中的每个结构。我们进行分析的上下文是Twitter，这是一个既包含关注者关系又包含交流关系的复杂社交网络。在Twitter上，“标签”用于标记对话主题，并且我们在这些社交结构旁边检查了标签的使用情况。我们发现用户采用的主题标签可以预测他们的社交关系，并且主题标签的最初采用者之间的社交关系可以预测该主题标签的未来流行度。通过研究加权的社会关系，我们观察到，虽然从标签结构上最容易预测强往复关系，但它们在预测标签受欢迎度方面也比弱定向关系要少得多。重要的是，我们表明，计算简单的结构行列式可以在两个任务中提供出色的性能。虽然我们的分析集中在Twitter上，但我们认为我们的发现广泛适用于各种背景下的主题联盟和社会关系，包括人们观看的电影，人们喜欢的品牌或人们经常光顾的地方。 |
| 课程简介: | People’s interests and people’s social relationships are intuitively connected, but understanding their interplay and whether they can help predict each other has remained an open question. We examine the interface of two decisive structures forming the backbone of online social media: the graph structure of social networks - who connects with whom - and the set structure of topical affiliations - who is interested in what. In studying this interface, we identify key relationships whereby each of these structures can be understood in terms of the other. The context for our analysis is Twitter, a complex social network of both follower relationships and communication relationships. On Twitter, “hashtags” are used to label conversation topics, and we examine hashtag usage alongside these social structures. We find that the hashtags that users adopt can predict their social relationships, and also that the social relationships between the initial adopters of a hashtag can predict the future popularity of that hashtag. By studying weighted social relationships, we observe that while strong reciprocated ties are the easiest to predict from hashtag structure, they are also much less useful than weak directed ties for predicting hashtag popularity. Importantly, we show that computationally simple structural determinants can provide remarkable performance in both tasks. While our analyses focus on Twitter, we view our findings as broadly applicable to topical affiliations and social relationships in a host of diverse contexts, including the movies people watch, the brands people like, or the locations people frequent. |
| 关 键 词: | Twitter; 主题标签; 社交网络图结构; 在线社交媒体 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-05-27：zyk |
| 最后编审: | 2021-05-27：zyk |
| 阅读次数: | 56 |