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Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection

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Abstract

The exponential growth in the use of social media has not only impacted the way individuals communicate and interact but has also opened new avenues for various domains including health care, marketing, e-commerce, e-governance and politics to name a few. It has been further seen that such engagements result in huge amount of user-generated content (UGC) from both individuals and organizations combined. This UGC can be analyzed in multiple ways to mine useful information. One such popular domain that uses this information is content buzz/popularity. The content shared on social media platforms becomes popular and subsequently viral when shared and propagated by a larger audience at a faster pace. Organizations are leveraging this power of social media in the domain of content buzz and virality by employing various buzz monitoring techniques to boost the reach of their content. This study thus proposes a hybrid artificial bee colony approach integrated with k-nearest neighbors to identify and segregate buzz in Twitter. A set of metrics comprising of created discussions, increase in authors, attention level, burstiness level, contribution sparseness, author interaction, author count and average length of discussions are used to model the buzz. The proposed approach considers the buzz discussions as outliers deviating from the normal discussions and identifies the same using the proposed hybrid bio-inspired approach. Findings may be useful in domains like e-commerce, digital and influencer marketing to explore the factors that might create buzz along with the difference between the impact of buzz and normal discussions on the consumers.

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References

  • Adaniya MHAC, Abrao T, Proenca ML Jr (2013) Anomaly detection using metaheuristic firefly harmonic clustering. J Netw 8(1):82–91

    Google Scholar 

  • Aggrawal N, Ahluwalia A, Khurana P, Arora A (2017) Brand analysis framework for online marketing: ranking web pages and analyzing popularity of brands on social media. Soc Netw Anal Min 7(1):21

    Article  Google Scholar 

  • Akcora CG, Carminati B, Ferrari E, Kantarcioglu M (2014) Detecting anomalies in social network data consumption. Soc Netw Anal Min 4(1):231

    Article  Google Scholar 

  • Aswani R, Ghrera SP, Chandra S (2016) A novel approach to outlier detection using modified grey wolf optimization and k-nearest neighbors algorithm. Indian J Sci Technol 9(44):1–8

    Article  Google Scholar 

  • Aswani R, Ghrera SP, Chandra S, Kar AK (2017a) Outlier detection among influencer blogs based on off-site web analytics data. Forthcoming in Lecture Notes in Computer Science. In: Proceedings of 16th IFIP conference on e-business, e-services and e-society. Springer International Publishing (in press)

  • Aswani R, Kar AK, Aggarwal S, Ilavarasan PV (2017b) Exploring content virality in Facebook: a semantic based approach. Forthcoming in Lecture Notes in Computer Science. In: Proceedings of 16th IFIP conference on e-business, e-services and e-society. Springer International Publishing (in press)

  • Aswani R, Kar AK, Ilavarasan PV, Dwivedi YK (2017c) Search engine marketing is not all gold: insights from Twitter and SEOClerks. Forthcoming in International Journal of Information Management (in press)

  • Bell G, Hey T, Szalay A (2009) Beyond the data deluge. Science 323(5919):1297–1298

    Article  Google Scholar 

  • Berger J, Milkman K (2010) Virality: what gets shared and why. NA Adv Consum Res 37:118–121

    Google Scholar 

  • Berthon PR, Pitt LF, Plangger K, Shapiro D (2012) Marketing meets Web 2.0, social media, and creative consumers: implications for international marketing strategy. Bus Horiz 55(3):261–271

    Article  Google Scholar 

  • Carvalho LF, Rodrigues JJ, Barbon S, Proenca ML (2013) Using ant colony optimization metaheuristic and dynamic time warping for anomaly detection. In: 2013 21st international conference on software, telecommunications and computer networks (SoftCOM), IEEE. pp 1–5

  • Castronovo C, Huang L (2012) Social media in an alternative marketing communication model. J Market Dev Compet 6(1):117

    Google Scholar 

  • Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197

    Article  Google Scholar 

  • Chae BK (2015) Insights from hashtag# supplychain and Twitter Analytics: considering Twitter and Twitter data for supply chain practice and research. Int J Prod Econ 165:247–259

    Article  Google Scholar 

  • Chakraborty A, Kar AK (2016) A review of bio-inspired computing methods and potential applications. In: Proceedings of the international conference on signal, networks, computing, and systems. Springer India, pp 155–161

  • Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. Nature-inspired computing and optimization. Springer, Berlin, pp 475–494

    Chapter  Google Scholar 

  • Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv (CSUR) 41(3):15

    Article  Google Scholar 

  • Colliander J, Dahlén M (2011) Following the fashionable friend: the power of social media. J Advert Res 51(1):313–320

    Article  Google Scholar 

  • Cuvelier E, Aufaure MA (2011) A buzz and e-reputation monitoring tool for twitter based on galois lattices. In: Andrews S, Polovina S, Hill R, Akhgar B (eds) Conceptual Structures for Discovering Knowledge. ICCS 2011. Lecture Notes in Computer Science, vol 6828. Springer, Heidelberg, pp 91–103

    Google Scholar 

  • Das S, Biswas A, Dasgupta S, Abraham A (2009) Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications. Found Comput Intell 3:23–55

    Google Scholar 

  • Deza A, Parikh D (2015) Understanding image virality. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1818–1826

  • DiMaggio P, Hargittai E (2001) From the ‘digital divide’ to ‘digital inequality’: studying Internet use as penetration increases. Princeton: Center for Arts and Cultural Policy Studies, Woodrow Wilson School, Princeton University 4(1):4–2

  • Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theoret Comput Sci 344(2–3):243–278

    Article  MathSciNet  MATH  Google Scholar 

  • Edosomwan S, Prakasan SK, Kouame D, Watson J, Seymour T (2011) The history of social media and its impact on business. J Appl Manag Entrep 16(3):79

    Google Scholar 

  • Fan W, Bifet A (2013) Mining big data: current status, and forecast to the future. ACM SIGKDD Explor Newsl 14(2):1–5

    Article  Google Scholar 

  • Fire M, Kagan D, Elyashar A, Elovici Y (2014) Friend or foe? Fake profile identification in online social networks. Soc Netw Anal Min 4(1):194

    Article  Google Scholar 

  • Fisher T (2009) ROI in social media: a look at the arguments. J Database Market Cust Strategy Manag 16(3):189–195

    Article  Google Scholar 

  • Gendreau M, Potvin JY (2005) Metaheuristics in combinatorial optimization. Ann Oper Res 140(1):189–213

    Article  MathSciNet  MATH  Google Scholar 

  • Ghanem TF, Elkilani WS, Abdul-Kader HM (2015) A hybrid approach for efficient anomaly detection using metaheuristic methods. J Adv Res 6(4):609–619

    Article  Google Scholar 

  • Goh KY, Heng CS, Lin Z (2013) Social media brand community and consumer behavior: quantifying the relative impact of user-and marketer-generated content. Inf Syst Res 24(1):88–107

    Article  Google Scholar 

  • Goodchild MF (2007) In the World of Web 2.0. Int J 2:24–32

    Google Scholar 

  • Grossberg S (1988) Nonlinear neural networks: principles, mechanisms, and architectures. Neural Netw 1(1):17–61

    Article  Google Scholar 

  • Grover P, Kar AK (2017) Big data analytics: a review on theoretical contributions and tools used in literature. Glob J Flex Syst Manag 18(3):203–229

    Article  Google Scholar 

  • Guerini M, Strapparava C, Özbal G (2011) Exploring text virality in social networks. In: ICWSM

  • Guerini M, Staiano J, Albanese D (2013) Exploring image virality in Google plus. In: 2013 international conference on social computing (SocialCom), IEEE. pp 671–678

  • Hanna R, Rohm A, Crittenden VL (2011) We’re all connected: the power of the social media ecosystem. Bus Horiz 54(3):265–273

    Article  Google Scholar 

  • Harrigan N, Achananuparp P, Lim EP (2012) Influentials, novelty, and social contagion: the viral power of average friends, close communities, and old news. Soc Netw 34(4):470–480

    Article  Google Scholar 

  • Hausmann A (2012) Creating ‘buzz’: opportunities and limitations of social media for arts institutions and their viral marketing. Int J Nonprofit Volunt Sect Market 17(3):173–182

    Article  Google Scholar 

  • Hernández MA, Stolfo SJ (1998) Real-world data is dirty: data cleansing and the merge/purge problem. Data Min Knowl Discov 2(1):9–37

    Article  Google Scholar 

  • Hodge V, Austin J (2004) A survey of outlier detection methodologies. Artif Intell Rev 22(2):85–126

    Article  MATH  Google Scholar 

  • Hoffman DL, Fodor M (2010) Can you measure the ROI of your social media marketing? MIT Sloan Manag Rev 52(1):41

    Google Scholar 

  • Holland JH (1992) Genetic algorithms. Scientific Am 267(1):66–73  

    Article  Google Scholar 

  • Ienco D, Bonchi F, Castillo C (2010) The meme ranking problem: maximizing microblogging virality. In: 2010 IEEE international conference on data mining workshops (ICDMW), IEEE, pp 328–335

  • Jones DF, Mirrazavi SK, Tamiz M (2002) Multi-objective meta-heuristics: an overview of the current state-of-the-art. Eur J Oper Res 137(1):1–9

    Article  MATH  Google Scholar 

  • Kaplan AM, Haenlein M (2010) Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz 53(1):59–68

    Article  Google Scholar 

  • Kaplan AM, Haenlein M (2011) Two hearts in three-quarter time: how to waltz the social media/viral marketing dance. Bus Horiz 54(3):253–263

    Article  Google Scholar 

  • Kar AK (2016) Bio inspired computing—a review of algorithms and scope of applications. Expert Syst Appl 59:20–32

    Article  Google Scholar 

  • Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132

    MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2007a) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471

    Article  MathSciNet  MATH  Google Scholar 

  • Karaboga D, Basturk B (2007b) Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In: Melin P, Castillo O, Aguilar LT, Kacprzyk J, Pedrycz W (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science, vol 4529. Springer, Heidelberg, pp 789–798

    Google Scholar 

  • Karaboga D, Basturk B (2008) On the performance of artificial bee colony (ABC) algorithm. Appl Soft Comput 8(1):687–697

    Article  Google Scholar 

  • Karaboga D, Ozturk C (2011) A novel clustering approach: artificial Bee Colony (ABC) algorithm. Appl Soft Comput 11(1):652–657

    Article  Google Scholar 

  • Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42:21–57

    Article  Google Scholar 

  • Kawala F, Douzal-Chouakria A, Gaussier E, Dimert E (2013) Prédictions d’activité dans les réseaux sociaux en ligne. In 4ième Conférence sur les Modèles et l’Analyse des Réseaux: Approches Mathématiques et Informatiques, p 16

  • Kumar V, Mirchandani R (2012) Increasing the ROI of social media marketing. MIT Sloan Manag Rev 54(1):55

    Google Scholar 

  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media?. In: Proceedings of the 19th international conference on World wide web. ACM, pp 591–600

  • Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1(1):5

    Article  Google Scholar 

  • Li Y, Qian M, Jin D, Hui P, Vasilakos AV (2015) Revealing the efficiency of information diffusion in online social networks of microblog. Inf Sci 293:383–389

    Article  Google Scholar 

  • Luo X, Zhang J (2013) How do consumer buzz and traffic in social media marketing predict the value of the firm? J Manag Inf Syst 30(2):213–238

    Article  Google Scholar 

  • Mills AJ (2012) Virality in social media: the SPIN framework. J Publ Aff 12(2):162–169

    Article  Google Scholar 

  • Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61

    Article  Google Scholar 

  • Morales AJ, Borondo J, Losada JC, Benito RM (2014) Efficiency of human activity on information spreading on Twitter. Soc Netw 39:1–11

    Article  Google Scholar 

  • Murthy D (2015) Twitter and elections: are tweets, predictive, reactive, or a form of buzz? Inf Commun Soc 18(7):816–831

    Article  Google Scholar 

  • Murugesan S (2007) Understanding web 2.0. IT Prof 9(4):34–41

    Article  Google Scholar 

  • Ngai EWT, Hu Y, Wong YH, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: a classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  • O’Keeffe GS, Clarke-Pearson K (2011) The impact of social media on children, adolescents, and families. Pediatrics 127(4):800–804

    Article  Google Scholar 

  • Papasolomou I, Melanthiou Y (2012) Social media: marketing public relations’ new best friend. J Promot Manag 18(3):319–328

    Article  Google Scholar 

  • Popescu AM, Pennacchiotti M (2010) Detecting controversial events from twitter. In: Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, pp 1873–1876

  • Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: ACM Sigmod Record (vol 29, no 2). ACM, pp 427–438

  • Roelens I, Baecke P, Benoit DF (2016) Identifying influencers in a social network: the value of real referral data. Decis Support Syst 91:25

    Article  Google Scholar 

  • Sampaio LHD, de Paula Marques M, Adaniya MH, Abrão T, Jeszensky PJE (2013) Ant colony optimization for resource allocation and anomaly detection in communication networks. In: Search algorithms for engineering optimization. InTech

  • Shahreza ML, Moazzami D, Moshiri B, Delavar MR (2011) Anomaly detection using a self-organizing map and particle swarm optimization. Sci Iran 18(6):1460–1468

    Article  Google Scholar 

  • Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. Evolutionary programming VII. Springer, Berlin, pp 591–600

    Chapter  Google Scholar 

  • Thies F, Wessel M, Benlian A (2014) Understanding the dynamic interplay of social buzz and contribution behavior within and between online platforms–evidence from crowdfunding. In: International Conference on Information Systems (ICIS 2014), Auckland, New Zealand

  • Tsugawa S, Ohsaki H (2017) On the relation between message sentiment and its virality on social media. Soc Netw Anal Min 7(1):19

    Article  Google Scholar 

  • Wang G, Hao J, Ma J, Huang L (2010) A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering. Expert Syst Appl 37(9):6225–6232

    Article  Google Scholar 

  • Wang D, Irani D, Pu C (2014) Spade: a social-spam analytics and detection framework. Soc Netw Anal Min 4(1):189

    Article  Google Scholar 

  • Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Sci Rep 2:1–8

    Article  Google Scholar 

  • Weng L, Menczer F, Ahn YY (2013) Virality prediction and community structure in social networks. Sci Rep 3:1–6

    Google Scholar 

  • Weng L, Menczer F, Ahn YY (2014) Predicting successful memes using network and community structure. In:Proceedings of 8th AAAI international conference on weblogs and social media (ICWSM 2014)

  • Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82

    Article  Google Scholar 

  • Wu X, Zhu X, Wu GQ, Ding W (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107

    Article  Google Scholar 

  • Xing EP, Ho Q, Xie P, Wei D (2016) Strategies and principles of distributed machine learning on big data. Engineering 2(2):179–195

    Article  Google Scholar 

  • Yang XS (2009) Firefly algorithms for multimodal optimization. In: International symposium on stochastic algorithms. Springer, Berlin, pp 169-178

  • Yang XS (2010) A new metaheuristic bat-inspired algorithm. In: Nature inspired cooperative strategies for optimization (NICSO 2010), pp 65–74

  • Yang XS (2012) Flower pollination algorithm for global optimization. In :UCNC, pp 240–249

  • Yang XS, Deb S (2009) Cuckoo search via Lévy flights. In: World congress on nature and biologically inspired computing, 2009. NaBIC 2009. IEEE, pp 210–214

  • Yang XS, Karamanoglu M, He X (2014) Flower pollination algorithm: a novel approach for multiobjective optimization. Eng Optim 46(9):1222–1237

    Article  MathSciNet  Google Scholar 

  • Zhang Y, Meratnia N, Havinga P (2010) Outlier detection techniques for wireless sensor networks: a survey. IEEE Commun Surv Tutor 12(2):159–170

    Article  Google Scholar 

  • Zhang X, Fuehres H, Gloor P (2012) Predicting asset value through twitter buzz. Adv Collect Intell 2011:23–34

    Google Scholar 

  • Zubcsek PP, Chowdhury I, Katona Z (2014) Information communities: the network structure of communication. Soc Netw 38:50–62

    Article  Google Scholar 

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Authors and Affiliations

  1. Indian Institute of Technology, Delhi, Hauz Khas, New Delhi, 110016, India

    Reema Aswani & Arpan Kumar Kar

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    S. P. Ghrera

  3. Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India

    Satish Chandra

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  1. Reema Aswani
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  2. S. P. Ghrera
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  3. Arpan Kumar Kar
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  4. Satish Chandra
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Correspondence to Reema Aswani.

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Aswani, R., Ghrera, S.P., Kar, A.K. et al. Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc. Netw. Anal. Min. 7, 38 (2017). https://doi.org/10.1007/s13278-017-0461-2

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