Community Detection on Social Networks With Sentimental Interaction

Community Detection on Social Networks With Sentimental Interaction

Bingdao Feng, Fangyu Cheng, Yanfei Liu, Xinglong Chang, Xiaobao Wang, Di Jin
Copyright: © 2024 |Pages: 23
DOI: 10.4018/IJSWIS.341232
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Many studies on community detection are mainly based on the similarity in friendship between users. Recent studies have started to explore node contents to identify semantically meaningful communities. However, the sentimental interaction information which plays an important role in community detection is often ignored. By analyzing and utilizing the abundant sentimental interaction information, one can not only more precisely identify the communities, but also discover the interesting interactions and conflicts between these communities. Based on this concept, the authors propose a new Community Sentiment Diffusion Detection Model (CSDD), which utilizes sentimental information embedded in forward posts. Furthermore, the authors present an efficient variational algorithm for model inference. The community detection results have been verified on two large Twitter datasets. It is experimentally demonstrated that we can provide a fine-grained view of sentimental interaction between communities and discover the mechanism of sentiment diffusion between communities.
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Relevant Work

Many methods of community detection have been proposed in recent years. The primary theories and techniques include the following: modularity-based methods (Yang et al., 2016; Newman, 2016; Zhang & Moore, 2014), hierarchical clustering methods (Li et al., 2015), spectral algorithms (Jia et al., 2015; Ma et al., 2018), dynamic algorithms (Jiang et al., 2018), statistical inference-based methods (Xie et al., 2018), etc. Please refer to the survey by Fortunato and Hric (2016). There are also some ways to use the metadata of the node to improve community detection results; for instance, Peel (2011, 2012) extended the stochastic block model for jointly modelling relational and class label information. Peel used the Supervised Blockmodel to achieve strong link-structure-based classification results and also provided a clear summary of network interactions to aid in the comprehension of the data.

Community Detection Based on Semantics

Many traditional community detection methods ignore node content attributes in favor of network topology information alone. Nevertheless, node content is crucial and helpful for community discovery, because it divides the community in a way that is more in line with its actual circumstances and makes a bigger contribution to the community. Node characteristics are based on the inherent structure of individuals, which is another way to express the nature of the community.

The topological information and content information complement each other, thus producing a more accurate result. Another advantage is that even if a single source of information is lost, a relatively stable community structure can still be built with another source of information, e.g., content analysis and personality characteristics. The idea of using a topic model-based method to unify the model and then estimating each parameter to find the community distribution was proposed by Balasubramanyan and Cohen (2011) and Yang et al. (2009). Zhu et al. (2013) combined classic ideas in topic modeling with a variant of the mixed-membership block model recently developed in the statistical physics community; their model can also be used for detecting generalized communities. Block-LDA (Balasubramanyan & Cohen, 2011) combines aspects of mixed membership stochastic block models and topic models to improve entity link modeling by jointly modeling links and text about the entities that are linked. Yang et al. (2009) proposed a discriminative methodology for detecting communities by integrating link and content analysis. For content analysis, they also developed a conditional model and a discriminative model. Neville et al. (2003) proposed a weighted adjacency matrix to allow a content similarity measure. The weight of each edge was defined as the number of attribute values shared by the two end vertices. They then applied three existing graph clustering algorithms to the weighted adjacency matrix to perform network clustering. Furthermore, since semantic structures are related to each other, TCCD (Wang et al., 2019) can learn the community structure and semantic interpretation of each community and search for the correlations of different topics in community detection.

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