Detecting Social Media Hidden Communities Using Dynamic Stochastic Blockmodel with Temporal Dirichlet Process

2014 ◽  
Vol 5 (2) ◽  
pp. 1-21 ◽  
Author(s):  
Xuning Tang ◽  
Christopher C. Yang
Keyword(s):  
Social Media ◽  
Dirichlet Process ◽  
Stochastic Blockmodel ◽  
Dynamic Stochastic

scholarly journals A Distance-Dependent Chinese Restaurant Process Based Method for Event Detection on Social Media

Inventions ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 80 ◽  
Author(s):  
Georgios Palaiokrassas ◽  
Athanasios Voulodimos ◽  
Antonios Litke ◽  
Athanasios Papaoikonomou ◽  
Theodora Varvarigou
Keyword(s):  
Social Media ◽  
Event Detection ◽  
Dirichlet Process ◽  
Chinese Restaurant Process ◽  
Chinese Restaurant ◽  
Social Events ◽  
The Social ◽  
Social Event Detection ◽  
Clustering Approach ◽  
Processing Steps

In this paper, we propose a method for event detection on social media, which aims at clustering media items into groups of events based on their textural information as well as available metadata. Our approach is based on distance-dependent Chinese Restaurant Process (ddCRP), a clustering approach resembling Dirichlet process algorithm. Furthermore, we scrutinize the effectiveness of a series of pre-processing steps in improving the detection performance. We experimentally evaluated our method using the Social Event Detection (SED) dataset of MediaEval 2013 benchmarking workshop, which pertains to the discovery of social events and their grouping in event-specific clusters. The obtained results indicate that the proposed method attains very good performance rates compared to existing approaches.

scholarly journals Dynamic degree-corrected blockmodels for social networks: A nonparametric approach

2018 ◽  
Vol 19 (4) ◽  
pp. 386-411
Author(s):  
Linda SL Tan ◽  
Maria De Iorio
Keyword(s):  
Social Networks ◽  
Dirichlet Process ◽  
Dynamic Networks ◽  
Static Model ◽  
Probit Regression ◽  
Dirichlet Process Prior ◽  
Nonparametric Approach ◽  
Mcmc Algorithms ◽  
Posterior Inference ◽  
Stochastic Blockmodel

A nonparametric approach to the modelling of social networks using degree-corrected stochastic blockmodels is proposed. The model for static network consists of a stochastic blockmodel using a probit regression formulation, and popularity parameters are incorporated to account for degree heterogeneity. We specify a Dirichlet process prior to detect community structure as well as to induce clustering in the popularity parameters. This approach is flexible yet parsimonious as it allows the appropriate number of communities and popularity clusters to be determined automatically by the data. We further discuss and implement extensions of the static model to dynamic networks. In a Bayesian framework, we perform posterior inference through MCMC algorithms. The models are illustrated using several real-world benchmark social networks.

scholarly journals A Density-based Nonparametric Model for Online Event Discovery from the Social Media Data

2017 ◽  
Author(s):  
Jinjin Guo ◽  
Zhiguo Gong
Keyword(s):  
Social Media ◽  
Dirichlet Process ◽  
Sequential Monte Carlo ◽  
Temporal Dynamics ◽  
Nonparametric Model ◽  
Parameter Inference ◽  
Social Media Data ◽  
Proposed Model ◽  
The Social ◽  
Media Data

In this paper, we propose a novel online event discovery model DP-density to capture various events from the social media data. The proposed model can flexibly accommodate the incremental arriving of the social documents in an online manner by leveraging Dirichlet Process, and a density based technique is exploited to deduce the temporal dynamics of events. The spatial patterns of events are also incorporated in the model by a mixture of Gaussians. To remove the bias caused by the streaming process of the documents, Sequential Monte Carlo is used for the parameter inference. Our extensive experiments over two different real datasets show that the proposed model is capable to extract interpretable events effectively in terms of perplexity and coherence.

scholarly journals Modeling the social media relationships of Irish politicians using a generalized latent space stochastic blockmodel

2021 ◽  
Vol 15 (4) ◽  
Author(s):  
Tin Lok James Ng ◽  
Thomas Brendan Murphy ◽  
Ted Westling ◽  
Tyler H. McCormick ◽  
Bailey Fosdick
Keyword(s):  
Social Media ◽  
Latent Space ◽  
The Social ◽  
Stochastic Blockmodel

Where Social Media Meets AAC

ASHA Leader ◽  
2015 ◽  
Vol 20 (7) ◽  
Author(s):  
Vicki Clarke
Keyword(s):  
Social Media

Making Twitter Work For You

ASHA Leader ◽  
2013 ◽  
Vol 18 (5) ◽  
Keyword(s):  
Social Media ◽  
Public Relations ◽  
Speech Language Pathologists

As professionals who recognize and value the power and important of communications, audiologists and speech-language pathologists are perfectly positioned to leverage social media for public relations.

Doctors employ social media

2013 ◽  
Vol 44 (1) ◽  
pp. 4
Author(s):  
Jane Anderson
Keyword(s):  
Social Media
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