Combining advanced computational social science and graph theoretic techniques to reveal adversarial information operations

Abstract Social media has influenced socio-political aspects of many societies around the world. It is an effortless way for people to enhance their communication, connect with like-minded people, and share ideas. Online social networks (OSNs) can be used for noble causes by bringing together communities with common shared interests and to promote awareness of various causes. However, there is a dark side to the use of OSNs. OSNs can also be used as a coordination and amplification platform for attacks. For instance, adversaries can increase the impact of an attack by causing panic in an area by promoting attacks using OSNs. Public data can help adversaries to determine the best timing for attacks, scheduling attacks, and then using OSNs to coordinate attacks on networks or physical locations. This convergence of the cyber and physical worlds is known as cybernetics. In this paper, we introduce an integrated method to identify malicious behavior and the actors responsible for propagating this behavior via online social networks. Throughout history we have used surveillance techniques to monitor negative behavior, activities, and information. Quantitative socio-technical methods such as deviant cyber flash mob (DCFM) detection and focal structure analysis (FSA) can provide reconnaissance capabilities that enable cities and governments to look beyond internal data and identify threats based on active events. Groups of powerful hackers can be identified through FSA which is an integrated model that uses a betweenness centrality method at the node-level and spectral modularity at group-level to identify a hidden malicious and powerful focal structure (a subset of the network). Assessment of groups using DCFM methods can help to identify powerful actors and prevent attacks. In this study, we examine multiple data sets integrating the DCFM and FSA models to help cybersecurity experts see a better picture of the threat which will help to plan a better response.

[1]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[2]  Ronaldo Menezes,et al.  Extracting Social Structures from Conversations in Twitter: A Case Study on Health-Related Posts , 2016, HT.

[3]  Nitin Agarwal,et al.  Focal structures analysis: identifying influential sets of individuals in a social network , 2016, Social Network Analysis and Mining.

[4]  Nitin Agarwal,et al.  Examining Intensive Groups in YouTube Commenter Networks , 2019, SBP-BRiMS.

[5]  Keisuke Sato,et al.  An enhanced MILP-based branch-and-price approach to modularity density maximization on graphs , 2018, Comput. Oper. Res..

[6]  J. Liu,et al.  A multi-agent genetic algorithm for community detection in complex networks , 2016 .

[7]  Jianbin Huang,et al.  Towards Online Multiresolution Community Detection in Large-Scale Networks , 2011, PloS one.

[8]  Christos Faloutsos,et al.  Patterns of Cascading Behavior in Large Blog Graphs , 2007, SDM.

[9]  Nitin Agarwal,et al.  Modeling flash mobs in cybernetic space: evaluating threats of emerging socio-technical behaviors to human security , 2014, 2014 IEEE Joint Intelligence and Security Informatics Conference.

[10]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[11]  Philip S. Yu,et al.  Identifying the influential bloggers in a community , 2008, WSDM '08.

[12]  Saeed Parsa,et al.  An evolutionary method for community detection using a novel local search strategy , 2019, Physica A: Statistical Mechanics and its Applications.

[13]  Reza Zafarani,et al.  Social Media Mining: An Introduction , 2014 .

[14]  Jimmy J. Lin,et al.  Information network or social network?: the structure of the twitter follow graph , 2014, WWW.

[15]  Chen-Kun Tsung,et al.  A Spectral Clustering Approach Based on Modularity Maximization for Community Detection Problem , 2016, 2016 International Computer Symposium (ICS).

[16]  Sille Obelitz Søe Algorithmic detection of misinformation and disinformation: Gricean perspectives , 2017, J. Documentation.

[17]  Nitin Agarwal,et al.  Finding Fake News Key Spreaders in Complex Social Networks by Using Bi-Level Decomposition Optimization Method , 2019, Communications in Computer and Information Science.

[18]  Eamonn O'Neill,et al.  Feasibility of structural network clustering for group-based privacy control in social networks , 2010, SOUPS.

[19]  Robert E. Tarjan,et al.  Depth-First Search and Linear Graph Algorithms , 1972, SIAM J. Comput..

[20]  L. Freeman Centrality in social networks conceptual clarification , 1978 .

[21]  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.

[22]  Nitin Agarwal,et al.  Analyzing Disinformation and Crowd Manipulation Tactics on YouTube , 2018, 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[23]  Yi Shen,et al.  Short communication A measure of centrality based on modularity matrix , 2008 .

[24]  Suhang Wang,et al.  Fake News Detection on Social Media: A Data Mining Perspective , 2017, SKDD.

[25]  Ali A. Ghorbani,et al.  An overview of online fake news: Characterization, detection, and discussion , 2020, Inf. Process. Manag..

[26]  Timothy W. Finin,et al.  Detecting Commmunities via Simultaneous Clustering of Graphs and Folksonomies , 2008, WebKDD 2008.

[27]  Martin G. Everett,et al.  A Graph-theoretic perspective on centrality , 2006, Soc. Networks.

[28]  Amrit Lal Sangal,et al.  Community detection in social networks based on fire propagation , 2019, Swarm Evol. Comput..

[29]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[30]  Matthew Richardson,et al.  Mining knowledge-sharing sites for viral marketing , 2002, KDD.

[31]  Tat-Seng Chua The Multimedia Challenges in Social Media Analytics , 2014, SAM '14.

[32]  Andrew B. Kahng,et al.  New spectral methods for ratio cut partitioning and clustering , 1991, IEEE Trans. Comput. Aided Des. Integr. Circuits Syst..

[33]  Haggai Roitman,et al.  An author-reader influence model for detecting topic-based influencers in social media , 2014, HT.

[34]  Darren Scott Appling,et al.  Determining credibility from social network structure , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[35]  Christos Faloutsos,et al.  Cascading Behavior in Large Blog Graphs , 2007 .

[36]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[37]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[38]  Mark Newman,et al.  Detecting community structure in networks , 2004 .

[39]  Renquan Lu,et al.  Inverse modelling-based multi-objective evolutionary algorithm with decomposition for community detection in complex networks , 2019, Physica A: Statistical Mechanics and its Applications.

[40]  M E J Newman,et al.  Fast algorithm for detecting community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[41]  Chao Li,et al.  Identification of influential spreaders based on classified neighbors in real-world complex networks , 2018, Appl. Math. Comput..

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

[43]  Shuming Zhou,et al.  Communities detection in social network based on local edge centrality , 2019, Physica A: Statistical Mechanics and its Applications.

[44]  Nitin Agarwal,et al.  Modeling blogger influence in a community , 2011, Social Network Analysis and Mining.

[45]  John Scott What is social network analysis , 2010 .

[46]  Stephen P. Borgatti,et al.  Centrality and network flow , 2005, Soc. Networks.

[47]  Yun Liu,et al.  Detecting communities in social networks using label propagation with information entropy , 2017 .

[48]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[49]  Yu Lei,et al.  Overlapping communities detection of social network based on hybrid C-means clustering algorithm , 2019, Sustainable Cities and Society.

[50]  Ugo Merlone,et al.  Comparing operational terrorist networks , 2020 .

[51]  W. Zachary,et al.  An Information Flow Model for Conflict and Fission in Small Groups , 1977, Journal of Anthropological Research.

[52]  Vijayalakshmi Atluri,et al.  Community based emergency response , 2013, DG.O.

[53]  M. Newman,et al.  Finding community structure in very large networks. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[54]  Mor Naaman,et al.  The impact of network structure on breaking ties in online social networks: unfollowing on twitter , 2011, CHI.

[55]  Vladimir Batagelj,et al.  Centrality in Social Networks , 1993 .

[56]  Robert Dale,et al.  NLP in a post-truth world , 2017, Natural Language Engineering.

[57]  Nitin Agarwal,et al.  Deviance in Social Media and Social Cyber Forensics , 2019, SpringerBriefs in Cybersecurity.