Identification and role of opinion leaders in information diffusion for online discussion network

Social networks are playing a vibrant role in spreading information among its users. With the ongoing boom in technology, social networks are becoming very popular nowadays and are made up of a multitude of users. Some of these users may have a strong influence on the other network users depending on their uncommon elevated values of betweenness centrality (BC). In the online discussion network such as Twitter, the extremely important users are called Opinion Leaders, who play a vital role in the spread of information in an efficient and fast way and keep the isolates interested in the online discussion network. One of the most significant problems in the associated sector is the identification of opinion leaders. In this paper, opinion leaders are chosen based on various centrality measures. The central users are identified based on their in-degree and out-degree links and are ranked within the network by their BC values. Furthermore, we analyze community evolution by using the standard Louvain algorithm. The experiment is performed on publicly available Higgs Boson data from Twitter. The conversation starter and influencer have been observed as an opinion leader for each network. These users have been observed to play a crucial part in the dissemination of information in an online discussion network.

[1]  Won-Hwa Hong,et al.  Exploiting Small World Problems in a SIoT Environment , 2018, Energies.

[2]  Sona Mardikyan,et al.  User Roles and Contribution Patterns in Online Communities: A Managerial Perspective , 2018 .

[3]  M. De Domenico,et al.  The Anatomy of a Scientific Rumor , 2013, Scientific Reports.

[4]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[5]  Eugene Ch'ng,et al.  The bottom-up formation and maintenance of a Twitter community: Analysis of the #FreeJahar Twitter community , 2015, Ind. Manag. Data Syst..

[6]  M. Jacomy,et al.  ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software , 2014, PloS one.

[7]  Amparo Baviera-Puig,et al.  Identifying Opinion Leaders on Twitter during Sporting Events: Lessons from a Case Study , 2019, Social Sciences.

[8]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[9]  Sona Mardikyan,et al.  User Roles in Online Communities and Their Moderating Effect on Online Community Usage Intention: An Integrated Approach , 2019, Int. J. Hum. Comput. Interact..

[10]  Mario Cataldi,et al.  Estimating domain-based user influence in social networks , 2013, SAC '13.

[11]  Quratulain Rajput,et al.  Identification of opinion leaders in social network , 2015, 2015 International Conference on Information and Communication Technologies (ICICT).

[12]  Eunyoung Moon,et al.  A Qualitative Method to Find Influencers Using Similarity-based Approach in the Blogosphere , 2010, 2010 IEEE Second International Conference on Social Computing.

[13]  Young Min Kim,et al.  Longitudinal analysis of online community dynamics , 2015, Ind. Manag. Data Syst..

[14]  Wei Liu,et al.  CORRIGENDUM: Biodegradation-inspired bioproduction of methylacetoin and 2-methyl-2,3-butanediol , 2013, Scientific Reports.

[15]  Marc Smith,et al.  Mapping a Twitter scholarly communication network: a case of the association of internet researchers’ conference , 2017, Scientometrics.

[16]  Gyu Sang Choi,et al.  An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks , 2019, Sensors.

[17]  Lewis Mitchell,et al.  A data-driven model for influenza transmission incorporating media effects , 2016, Royal Society Open Science.

[18]  Nitin Agarwal.  A study of communities and influence in blogosphere , 2008, IDAR '08.

[19]  Gayle S. Stever,et al.  Twitter as a way for celebrities to communicate with fans: Implications for the study of parasocial interaction. , 2013 .

[20]  Renaud Lambiotte,et al.  Communities, knowledge creation, and information diffusion , 2009, J. Informetrics.

[21]  Martin G. Everett,et al.  Analyzing social networks , 2013 .

[22]  Claudia Canali,et al.  A quantitative methodology based on component analysis to identify key users in social networks , 2012, Int. J. Soc. Netw. Min..

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

[24]  Yang Feng.  Are you connected? Evaluating information cascades in online discussion about the #RaceTogether campaign , 2016, Comput. Hum. Behav..

[25]  R. Burt.  The Social Capital of Opinion Leaders , 1999 .

[26]  Martijn Meeter,et al.  Factors Influencing Academic Motivation of Ethnic Minority Students: A Review , 2018 .

[27]  Zhaohui Yuan,et al.  Motor Imagery EEG Signals Classification Based on Mode Amplitude and Frequency Components Using Empirical Wavelet Transform , 2019, IEEE Access.

[28]  Cheree James,et al.  Autonomic markers of emotional processing: skin sympathetic nerve activity in humans during exposure to emotionally charged images , 2012, Front. Physio..

[29]  Aimin Jiang,et al.  Weighted Based Trustworthiness Ranking in Social Internet of Things by using Soft Set Theory , 2019, 2019 IEEE 5th International Conference on Computer and Communications (ICCC).

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

[31]  Eric Fleury,et al.  Tracking Clinical Staff Behaviors in an Operating Room , 2019, Sensors.

[32]  J. Berry.  The Influentials: One American in Ten Tells the Other Nine How to Vote, Where to Eat, and What to Buy , 2003 .

[33]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

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

[35]  Masoud Rahgozar,et al.  OLFinder: Finding opinion leaders in online social networks , 2016, J. Inf. Sci..

[36]  Mohsin Adalat,et al.  Variations in power of opinion leaders in online communication networks , 2018, Royal Society Open Science.

[37]  Seyed Mojtaba Hosseini Bamakan,et al.  Opinion leader detection: A methodological review , 2019, Expert Syst. Appl..

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

[39]  G. Khan,et al.  Social media communication strategies of government agencies: Twitter use in Korea and the USA , 2014 .

[40]  Kenneth E. Clow,et al.  Integrated Advertising, Promotion and Marketing Communications , 2002 .

[41]  Jalel Akaichi,et al.  Detection of Opinion Leaders in Social Networks: A Survey , 2018, IIMSS.

[42]  James P. Gleeson,et al.  Integrating sentiment and social structure to determine preference alignments: the Irish Marriage Referendum , 2017, Royal Society Open Science.

[43]  Hayder Radha,et al.  Identifying Leaders and Followers in Online Social Networks , 2013, IEEE Journal on Selected Areas in Communications.

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

[45]  Han Woo Park,et al.  Longitudinal dynamics of the cultural diffusion of Kpop on YouTube , 2017 .

[46]  Narisa Zhao,et al.  Modeling information diffusion in time-varying community networks. , 2017, Chaos.

[47]  David Liben-Nowell,et al.  The link-prediction problem for social networks , 2007 .

[48]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[49]  Alexandre Arenas,et al.  Characterizing interactions in online social networks during exceptional events , 2015, Front. Phys..

[50]  Michael Trusov,et al.  Determining Influential Users in Internet Social Networks , 2010 .

[51]  Barry Wellman,et al.  Geography of Twitter networks , 2012, Soc. Networks.

[52]  E. Rogers.  Diffusion of Innovations , 1962 .

[53]  Igor Mozetic,et al.  Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities , 2016, PloS one.

[54]  Timothy W. Finin,et al.  Why we twitter: understanding microblogging usage and communities , 2007, WebKDD/SNA-KDD '07.

[55]  Kasturi Dewi Varathan,et al.  Identification of influential spreaders in online social networks using interaction weighted K-core decomposition method , 2017 .