New research methods & algorithms in social network analysis

Abstract The exponential growth of social media and online social networks (e.g., Facebook, Twitter, Instagram, and TikTok) has changed the daily lives of millions of people. The ease to accessing, gathering and processing available data and the high societal and industrial interest in such data have attracted the interest of a large of research disciplines. This special issue has been focused mainly on Data Science and Artificial Intelligence techniques, and their application to social network analysis. The issue provides a total of 12 selected papers (out of 65) that represent latest advances and developments in these areas.

[1]  Tinghuai Ma,et al.  LGIEM: Global and local node influence based community detection , 2020, Future Gener. Comput. Syst..

[2]  Francisco Herrera,et al.  Sentiment Analysis in TripAdvisor , 2017, IEEE Intelligent Systems.

[3]  Diego Reforgiato Recupero,et al.  Multi-domain sentiment analysis with mimicked and polarized word embeddings for human-robot interaction , 2020, Future Gener. Comput. Syst..

[4]  Francisco Herrera,et al.  Enhancing the classification of social media opinions by optimizing the structural information , 2020, Future Gener. Comput. Syst..

[5]  Tao Wang,et al.  Sparsity estimation matching pursuit algorithm based on restricted isometry property for signal reconstruction , 2017, Future Gener. Comput. Syst..

[6]  Carmen Zarco,et al.  Marketing analysis of wineries using social collective behavior from users' temporal activity on Twitter , 2020, Inf. Process. Manag..

[7]  Kevin Chen-Chuan Chang,et al.  Embedding Both Finite and Infinite Communities on Graphs [Application Notes] , 2019, IEEE Comput. Intell. Mag..

[8]  Francisco Herrera,et al.  Inconsistencies on TripAdvisor reviews: A unified index between users and Sentiment Analysis Methods , 2019, Neurocomputing.

[9]  Yufeng Wang,et al.  A novel ITÖ Algorithm for influence maximization in the large-scale social networks , 2018, Future Gener. Comput. Syst..

[10]  Erik Cambria,et al.  Natural language based financial forecasting: a survey , 2017, Artificial Intelligence Review.

[11]  Mourad Oussalah,et al.  On the use of distributed semantics of tweet metadata for user age prediction , 2020, Future Gener. Comput. Syst..

[12]  Jesús Sánchez-Oro,et al.  Iterated Greedy algorithm for performing community detection in social networks , 2018, Future Gener. Comput. Syst..

[13]  Rebeca P. Díaz Redondo,et al.  A hybrid analysis of LBSN data to early detect anomalies in crowd dynamics , 2020, Future Gener. Comput. Syst..

[14]  Francesco Orciuoli,et al.  Understanding the composition and evolution of terrorist group networks: A rough set approach , 2019, Future Gener. Comput. Syst..

[15]  Erik Cambria,et al.  The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools , 2020, Inf. Fusion.

[16]  Jason J. Jung,et al.  Social big data: Recent achievements and new challenges , 2015, Information Fusion.

[17]  Hao Lu,et al.  Wide-grained capsule network with sentence-level feature to detect meteorological event in social network , 2020, Future Gener. Comput. Syst..

[18]  Roberto Munoz,et al.  Automated classification of social network messages into Smart Cities dimensions , 2020, Future Gener. Comput. Syst..

[19]  David Camacho,et al.  A Multi-Objective Genetic Algorithm for detecting dynamic communities using a local search driven immigrant's scheme , 2020, Future Gener. Comput. Syst..

[20]  Sancho Salcedo-Sanz,et al.  A Multi-Objective Genetic Algorithm for overlapping community detection based on edge encoding , 2018, Inf. Sci..

[21]  Quan Pan,et al.  Learning binary codes with neural collaborative filtering for efficient recommendation systems , 2019, Knowl. Based Syst..

[22]  Stefan Stieglitz,et al.  Social Media Analytics , 2014, Business & Information Systems Engineering.

[23]  Erik Cambria,et al.  Sentic Computing for social media marketing , 2012, Multimedia Tools and Applications.

[24]  Erik Cambria,et al.  Semi-supervised learning for big social data analysis , 2018, Neurocomputing.