User communities in social networks are usually identified by considering explicit structural social connections between users. While such communities can reveal important information about their members such as family or friendship ties and geographical proximity, they do not necessarily succeed at pulling like-minded users that share the same interests together. In this paper, we are interested in identifying communities of users that share similar topical interests over time, regardless of whether they are explicitly connected to each other on the social network. More specifically, we tackle the problem of identifying temporal topic-based communities from Twitter, i.e., communities of users who have similar temporal inclination towards the current emerging topics on Twitter. We model each topic as a collection of highly correlated semantic concepts observed in tweets and identify them by clustering the time-series based representation of each concept built based on each concept's observation frequency over time. Based on the identified emerging topics in a given time period, we utilize multivariate time series analysis to model the contributions of each user towards the identified topics, which allows us to detect latent user communities. Through our experiments on Twitter data, we demonstrate i) the effectiveness of our topic detection method to detect real world topics and ii) the effectiveness of our approach compared to well-established approaches for community detection.
Most of these works have proposed a probabilistic model to detect topic-based user communities based on textual content or jointly with social connections @cite_26 @cite_6 @cite_7 @cite_27 @cite_16 . For example, @cite_26 have identified users' topics of interest and extract latent communities based on the topics utilizing Gaussian Restricted Boltzmann Machine. , @cite_23 have integrated community discovery with topic modeling in a unified generative model to detect communities of users who are coherent in both structural relationships and latent topics. In their framework, a community can be formed around multiple topics and a topic can be shared between multiple communities. , @cite_24 have proposed probabilistic schemes that incorporate users' posts, social connections and interaction types to discover latent user communities in social networks. In their paper, they have considered three types of interactions: a conventional tweet, a reply tweet and a re-tweet. Other authors have also proposed variations of Latent Dirichlet Allocation (LDA), for example, Author-Topic model @cite_29 and Community-User-Topic model @cite_4 , to identify latent communities.
@cite_26 @cite_4 @cite_7 @cite_29 @cite_6 @cite_24 @cite_27 @cite_23 @cite_16
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In this work, we discover the daily location-driven routines that are contained in a massive real-life human dataset collected by mobile phones. Our goal is the discovery and analysis of human routines that characterize both individual and group behaviors in terms of location patterns. We develop an unsupervised methodology based on two differing probabilistic topic models and apply them to the daily life of 97 mobile phone users over a 16-month period to achieve these goals. Topic models are probabilistic generative models for documents that identify the latent structure that underlies a set of words. Routines dominating the entire group's activities, identified with a methodology based on the Latent Dirichlet Allocation topic model, include “going to work late”, “going home early”, “working nonstop” and “having no reception (phone off)” at different times over varying time-intervals. We also detect routines which are characteristic of users, with a methodology based on the Author-Topic model. With the routines discovered, and the two methods of characterizing days and users, we can then perform various tasks. We use the routines discovered to determine behavioral patterns of users and groups of users. For example, we can find individuals that display specific daily routines, such as “going to work early” or “turning off the mobile (or having no reception) in the evenings”. We are also able to characterize daily patterns by determining the topic structure of days in addition to determining whether certain routines occur dominantly on weekends or weekdays. Furthermore, the routines discovered can be used to rank users or find subgroups of users who display certain routines. We can also characterize users based on their entropy. We compare our method to one based on clustering using K-means. Finally, we analyze an individual's routines over time to determine regions with high variations, which may correspond to specific events. Recently community detection has attracted much interest in social media to understand the collective behaviours of users and allow individuals to be modeled in the context of the group. Most existing approaches for community detection exploit either users' social links or their published content, aiming at discovering groups of densely connected or highly similar users. They often fail to find effective communities due to excessive noise in content, sparsity in links, and heterogenous behaviours of users in social media. Further, they are unable to provide insights and rationales behind the formation of the group and the collective behaviours of the users. To tackle these challenges, we propose to discover communities in a low- dimensional latent space in which we simultaneously learn the representation of users and communities. In particular, we integrated different social views of the network into a low-dimensional latent space in which we sought dense clusters of users as communities. By imposing a Laplacian regularizer into affiliation matrix, we further incorporated prior knowledge into the community discovery process. Finally community profiles were computed by a linear operator integrating the profiles of members. Taking the wellness domain as an example, we conducted experiments on a large scale real world dataset of users tweeting about diabetes and its related concepts, which demonstrate the effectiveness of our approach in discovering and profiling user communities. We propose a method for discovering the dependency relationships between the topics of documents shared in social networks using the latent social interactions, attempting to answer the question: given a seemingly new topic, from where does this topic evolve? In particular, we seek to discover the pair-wise probabilistic dependency in topics of documents which associate social actors from a latent social network, where these documents are being shared. By viewing the evolution of topics as a Markov chain, we estimate a Markov transition matrix of topics by leveraging social interactions and topic semantics. Metastable states in a Markov chain are applied to the clustering of topics. Applied to the CiteSeer dataset, a collection of documents in academia, we show the trends of research topics, how research topics are related and which are stable. We also show how certain social actors, authors, impact these topics and propose new ways for evaluating author impact. This article studies the problem of latent community topic analysis in text-associated graphs. With the development of social media, a lot of user-generated content is available with user networks. Along with rich information in networks, user graphs can be extended with text information associated with nodes. Topic modeling is a classic problem in text mining and it is interesting to discover the latent topics in text-associated graphs. Different from traditional topic modeling methods considering links, we incorporate community discovery into topic analysis in text-associated graphs to guarantee the topical coherence in the communities so that users in the same community are closely linked to each other and share common latent topics. We handle topic modeling and community discovery in the same framework. In our model we separate the concepts of community and topic, so one community can correspond to multiple topics and multiple communities can share the same topic. We compare different methods and perform extensive experiments on two real datasets. The results confirm our hypothesis that topics could help understand community structure, while community structure could help model topics.