From the User to the Medium: Neural Profiling Across Web Communities
From the User to the Medium: Neural Profiling Across Web Communities
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DOI：
10.1609/icwsm.v12i1.15063
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
Mohammad Akbari;Kunal Relia;Anas Elghafari;R. Chunara
中科院分区：
文献类型：
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作者：
Mohammad Akbari;Kunal Relia;Anas Elghafari;R. Chunara
在线社区为个人提供了一种独特的方式，可以从类似情况下的人那里获取信息，这对于需要日常和个性化管理的健康状况至关重要。由于这些群体和主题往往是有机地出现的，确定讨论的主题类型对于了解他们的需求是必要的。此外，这些社区和其中的人可以是相当多样化的，现有的社区检测方法还没有扩展到评估这些异质性。这是有限的，因为社区检测方法没有集中在社区检测的基础上的用户生成的内容的文本特征之间的语义关系。因此，在这里，我们开发了一种方法，NeuroCom，最佳地发现密集的用户群体作为社区在一个潜在的空间推断的神经表示的用户发布的内容。通过嵌入的话和消息，我们表明，NeuroCom展示了改进的聚类，并确定了更细致入微的讨论主题相比，其他常见的无监督学习方法。
Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.