(T-ToCODE): A Framework for Trendy Topic Detection and Community Detection for Information Diffusion in Social Network

The increased use of social network generates a huge amount of data. Extracting useful information from this huge data available is the need of today. Study and analysis of this data generated provide insight into the behavior of the customers or users and thus will be beneficial to increase the sales of products or understand customers. To achieve the same, we propose a novel framework which will extract trendy topics, identify communities related to these trendy, topics, and also identify influential or seed nodes in communities. The framework intends to find the list of topics which are popular, second, find trend-driven communities, and from these trend-driven communities find nodes which act as seed nodes and thus dominate the spread of information in the community. Analysis of real-world data is done and results are compared with baseline approaches.

[1]  Yong Jae Lee,et al.  Who Will Share My Image?: Predicting the Content Diffusion Path in Online Social Networks , 2018, WSDM.

[2]  Adwitiya Sinha,et al.  Network diffusion for information propagation in online social communities , 2017, 2017 Tenth International Conference on Contemporary Computing (IC3).

[3]  Gang Chen,et al.  Determinants of users' information dissemination behavior on social networking sites: An elaboration likelihood model perspective , 2018, Internet Res..

[4]  Francesco Bonchi.  Influence Propagation in Social Networks: A Data Mining Perspective , 2011, Web Intelligence.

[5]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[6]  Mehrdad Agha Mohammad Ali Kermani,et al.  A hybrid algorithm based on community detection and multi attribute decision making for influence maximization , 2018, Comput. Ind. Eng..

[7]  Masahiro Kimura,et al.  Prediction of Information Diffusion Probabilities for Independent Cascade Model , 2008, KES.

[8]  Jean-Loup Guillaume,et al.  Fast unfolding of communities in large networks , 2008, 0803.0476.

[9]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[10]  Roberto Frias,et al.  Twitter event detection: combining wavelet analysis and topic inference summarization , 2011 .

[11]  Bo Qu,et al.  Modeling of Information Diffusion on Social Networks with Applications to WeChat , 2017, ArXiv.

[12]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[13]  Jay Lee,et al.  Open Source Social Network Simulator Focusing on Spatial Meme Diffusion , 2018 .

[14]  Chao Liu,et al.  Information Diffusion and Opinion Leader Mathematical Modeling Based on Microblog , 2018, IEEE Access.

[15]  Bin Wu,et al.  Event Evolution Model Based on Random Walk Model with Hot Topic Extraction , 2016, ADMA.

[16]  Kerk F. Kee,et al.  Developing and validating the A-B-C framework of information diffusion on social media , 2018, New Media Soc..

[17]  Li Li,et al.  An Improved Latent Dirichlet Allocation Model for Hot Topic Extraction , 2014, 2014 IEEE Fourth International Conference on Big Data and Cloud Computing.

[18]  Makarand Hastak,et al.  Social network analysis: Characteristics of online social networks after a disaster , 2018, Int. J. Inf. Manag..

[19]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

[20]  Hong Tuyet Tu,et al.  Competitive Information Diffusion Model in Social Network with Negative Information Propagation , 2018, ACIIDS.

[21]  Daniel M. Romero,et al.  Influence and Passivity in Social Media , 2011, ECML/PKDD.

[22]  Jure Leskovec,et al.  Defining and evaluating network communities based on ground-truth , 2012, Knowledge and Information Systems.

[23]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[24]  Ghassan Beydoun,et al.  Disaster Management and Information Systems: Insights to Emerging Challenges , 2018, Information Systems Frontiers.

[25]  Daniel Barbará,et al.  On-line LDA: Adaptive Topic Models for Mining Text Streams with Applications to Topic Detection and Tracking , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[26]  Vasileios Karyotis,et al.  Temporal Dynamics of Information Diffusion in Twitter: Modeling and Experimentation , 2018, IEEE Transactions on Computational Social Systems.

[27]  Scott Counts,et al.  Identifying topical authorities in microblogs , 2011, WSDM '11.

[28]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[29]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[30]  K. J. Ray Liu,et al.  Graphical Evolutionary Game for Information Diffusion Over Social Networks , 2013, IEEE Journal of Selected Topics in Signal Processing.

[31]  Hong Tuyet Tu,et al.  Differential Information Diffusion Model in Social Network , 2018, ACIIDS.