Characterization of topic-based online communities by combining network data and user generated content
Characterization of topic-based online communities by combining network data and user generated content
复制标题
通过结合网络数据和用户生成的内容来表征基于主题的在线社区
DOI：
10.1007/s11222-020-09947-5
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发表时间：
2020
影响因子：
2.2
通讯作者：
Mirai Igarashi and Nobuhiko Terui
中科院分区：
文献类型：
--
作者：
五十嵐未来;照井伸彦;Mirai Igarashi and Nobuhiko Terui
本研究提出了一个模型，通过结合两种类型的数据：网络数据和用户生成的内容（UGC）的特征在线社区。 现有的网络社团结构检测模型只利用网络信息。然而，并非所有连接在网络中的人都有相同的兴趣。 例如，即使学生属于同一个社区的“学校”，他们可能有各种爱好，如音乐，书籍或体育。因此，公司根据他们在社交媒体上的通信所发现的兴趣来识别社区是更现实和有益的。此外，人们可能属于多个社区，如家庭、工作和在线朋友。我们的模型探索多个重叠的社区，根据他们的主题确定使用两种类型的数据联合。通过验证所提出的模型的主要特点，我们的模拟研究表明，该模型正确地识别社区结构，不能同时考虑网络数据和UGC发现。此外，使用Twitter数据进行的实证分析表明，我们的模型可以从大型在线网络中找到现实和有意义的社区结构，并具有良好的预测性能。
This study proposes a model for characterizing online communities by combining two types of data: network data and user-generated-content (UGC). The existing models for detecting the community structure of a network employ only network information. However, not all people connected in a network share the same interests. For instance, even if students belong to the same community of “school,” they may have various hobbies such as music, books, or sports. Hence, it is more realistic and beneficial for companies to identify communities according to their interests uncovered by their communications on social media. In addition, people may belong to multiple communities such as family, work, and online friends. Our model explores multiple overlapping communities according to their topics identified using two types of data jointly. By way of validating the main features of the proposed model, our simulation study shows that the model correctly identifies the community structure that could not be found without considering both network data and UGC. Furthermore, an empirical analysis using Twitter data clarifies that our model can find realistic and meaningful community structures from large online networks and has a good predictive performance.
DOI：
10.1073/pnas.0307752101
发表时间：
2004-04-06
影响因子：
11.1
作者：
Griffiths, TL;Steyvers, M
通讯作者：
Steyvers, M
影响因子：
2
作者：
HUBERT, L;ARABIE, P
通讯作者：
ARABIE, P
影响因子：
2.4
作者：
Echebarria, Blas;Roeder, Georg;Baer, Markus
通讯作者：
Baer, Markus