A multilayer network analysis of hashtags in twitter via co-occurrence and semantic links

Complex network studies, as an interdisciplinary framework, span a large variety of subjects including social media. In social networks, several mechanisms generate miscellaneous structures like friendship networks, mention networks, tag networks, etc. Focusing on tag networks (namely, hashtags in twitter), we made a two-layer analysis of tag networks from a massive dataset of Twitter entries. The first layer is constructed by converting the co-occurrences of these tags in a single entry (tweet) into links, while the second layer is constructed converting the semantic relations of the tags into links. We observed that the universal properties of the real networks like small-world property, clustering and power-law distributions in various network parameters are also evident in the multilayer network of hashtags. Moreover, we outlined that co-occurrences of hashtags in tweets are mostly coupled with semantic relations, whereas a small number of semantically unrelated, therefore random links reduce node separation and network diameter in the co-occurrence network layer. Together with the degree distributions, the power-law consistencies of degree difference, edge weight and cosine similarity distributions in both layers are also appealing forms of Zipf’s law evident in nature.

[1]  M. Newman,et al.  Mixing patterns in networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[2]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[3]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[4]  Dror Y. Kenett,et al.  Networks of networks – An introduction , 2015 .

[5]  Long Sheng,et al.  English and Chinese languages as weighted complex networks , 2009 .

[6]  Marina L. Gavrilova,et al.  Mining Social Behavioral Biometrics in Twitter , 2014, 2014 International Conference on Cyberworlds.

[7]  A. Barabasi,et al.  Evolution of the social network of scientific collaborations , 2001, cond-mat/0104162.

[8]  Scott A. Golder,et al.  Structural Predictors of Tie Formation in Twitter: Transitivity and Mutuality , 2010, 2010 IEEE Second International Conference on Social Computing.

[9]  Daniel D. Suthers,et al.  Networked Solidarity: An Exploratory Network Perspective on Twitter Activity Related to #illridewithyou , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[10]  Hideaki Takeda,et al.  Hashtag Popularity on Twitter: Analyzing Co-occurrence of Multiple Hashtags , 2015, HCI.

[11]  Markus Strohmaier,et al.  Meaning as collective use: predicting semantic hashtag categories on twitter , 2013, WWW.

[12]  M. Newman Spread of epidemic disease on networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[13]  Beom Jun Kim,et al.  Growing scale-free networks with tunable clustering. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Lada A. Adamic,et al.  Zipf's law and the Internet , 2002, Glottometrics.

[15]  Ricardo A. Baeza-Yates,et al.  Extracting semantic relations from query logs , 2007, KDD '07.

[16]  Krzysztof Suchecki,et al.  Networks of companies and branches in Poland , 2007 .

[17]  Albert-László Barabási,et al.  Universality in network dynamics , 2013, Nature Physics.

[18]  M E J Newman,et al.  Random graphs with clustering. , 2009, Physical review letters.

[19]  Marina L. Gavrilova,et al.  Social Behavioral Biometrics: An Emerging Trend , 2015, Int. J. Pattern Recognit. Artif. Intell..

[20]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Matjaz Perc,et al.  The Matthew effect in empirical data , 2014, Journal of The Royal Society Interface.

[22]  Clara Pizzuti,et al.  Analysis of the Italian Tweet Political Sentiment in 2014 European Elections , 2015, 2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).

[23]  Huanwen Tang,et al.  EVOLVING SCALE-FREE NETWORK MODEL WITH TUNABLE CLUSTERING , 2005 .

[24]  Qi Gao,et al.  Semantic Enrichment of Twitter Posts for User Profile Construction on the Social Web , 2011, ESWC.

[25]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[26]  Mason A. Porter,et al.  Multilayer networks , 2013, J. Complex Networks.

[27]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[28]  Pinar Senkul,et al.  Semantic Expansion of Hashtags for Enhanced Event Detection in Twitter , 2012 .

[29]  B. Chae,et al.  Insights from hashtag #supplychain and Twitter Analytics: Considering Twitter and Twitter data for supply chain practice and research , 2015 .

[30]  A. Çavusoglu,et al.  Detailing the co-authorship networks in degree coupling, edge weight and academic age perspective , 2016 .

[31]  Mark Newman,et al.  Networks: An Introduction , 2010 .

[32]  İlker Türker,et al.  Scientific collaboration network of Turkey , 2013 .

[33]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[34]  Ben Shneiderman,et al.  The Structures of Twitter Crowds and Conversations , 2015 .

[35]  Yogesh Virkar,et al.  Power-law distributions in binned empirical data , 2012, 1208.3524.

[36]  Ciro Cattuto,et al.  Dynamical classes of collective attention in twitter , 2011, WWW.

[37]  Sanda Martinčić-Ipšić,et al.  Link prediction on Twitter , 2017, PloS one.

[38]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[39]  Grigory Begelman,et al.  Automated Tag Clustering: Improving search and exploration in the tag space , 2006 .

[40]  Jari Saramäki,et al.  Temporal Networks , 2011, Encyclopedia of Social Network Analysis and Mining.

[41]  Mehran Sahami,et al.  A web-based kernel function for measuring the similarity of short text snippets , 2006, WWW '06.

[42]  Emrullah Demiral,et al.  Uncovering the differences in linguistic network dynamics of book and social media texts , 2016, SpringerPlus.

[43]  José Garcia Vivas Miranda,et al.  Complex Semantic Networks , 2010 .

[44]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[45]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[46]  Simone Paolo Ponzetto,et al.  BabelNet: Building a Very Large Multilingual Semantic Network , 2010, ACL.

[47]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[48]  M E J Newman Assortative mixing in networks. , 2002, Physical review letters.

[49]  V. Latora,et al.  Complex networks: Structure and dynamics , 2006 .

[50]  Mark E. J. Newman,et al.  Power-Law Distributions in Empirical Data , 2007, SIAM Rev..

[51]  A. Barabasi,et al.  Scale-free characteristics of random networks: the topology of the world-wide web , 2000 .

[52]  Z. Wang,et al.  The structure and dynamics of multilayer networks , 2014, Physics Reports.