Skip to main content
SpringerLink
Log in

A hybrid bio-inspired computing approach for buzz detection in social media

  • Research Paper
  • Published:
Evolutionary Intelligence Aims and scope Submit manuscript

Abstract

Social media forums such as Twitter can be used as instruments for understanding the way users behave and engage with other users online. Analysis of data related to material shared by users assists in mining useful information for assessing content for virality. This study proposes a methodology to predict which tweets are likely to become viral and generate a lot of conversations over the Internet, termed as buzz discussions, by considering such discussions as outliers, using bio-inspired algorithms integrated with k-Nearest Neighbors classification. Performances of three bio-inspired optimization algorithms, namely Grey Wolf Optimization, Chicken Swarm Optimization and, Artificial Bee Colony, have also been evaluated based on the efficacy of the proposed hybrid models for mining outliers on a supervised learning data-set containing 11 primary features and 140,707 instances. Among the three algorithms used for this outlier detection problem, Chicken Swarm Optimization shows better performance, overall, in terms of evaluation parameters, including accuracy, precision, recall, specificity, F1-measure and convergence.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Aci C, İnan C, Avci M (2010) A hybrid classification method of k nearest neighbor, Bayesian methods and genetic algorithm. Expert Syst Appl 37:5061–5067. https://doi.org/10.1016/j.eswa.2009.12.004

    Article  Google Scholar 

  2. Ahmed K, Hassanien AE, Ezzat E, Tsai PW (2016) An adaptive approach for community detection based on chicken swarm optimization algorithm. In: ICGEC, pp. 1–8

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

    Article  Google Scholar 

  4. Amer M, Goldstein M (2012) Nearest-neighbor and clustering based anomaly detection algorithms for rapidminer. In: Proceedings of the 3rd rapid miner community meeting and conferernce, pp 1–12. https://doi.org/10.5455/ijavms.141

  5. Amer M, Goldstein M, Abdennadher S (2013) Enhancing one-class support vector machines for unsupervised anomaly detection. In: Proceedings of the ACM SIGKDD workshop on outlier detection and description, ODD 2013, pp 8–15. https://doi.org/10.1145/2500853.2500857

  6. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: Principles of data mining and knowledge discovery, pp 15–27

  7. Angiulli F, Pizzuti C (2002) Fast outlier detection in high dimensional spaces. In: Proceedings of the 6th European conference on principles of data mining and knowledge discovery, pp 15–26

  8. Angiulli F, Pizzuti C (2005) Outlier mining in large high-dimensional data sets. IEEE Trans Knowl Data Eng 17:203–215

    Article  Google Scholar 

  9. Aswani R, Ghrera S, Kar A, Chandra S (2017) Identifying buzz in social media: a hybrid approach using artificial bee colony and k-nearest neighbors for outlier detection. Soc Netw Anal Min 7:1–10. https://doi.org/10.1007/s13278-017-0461-2

    Article  Google Scholar 

  10. 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 9:1–8

    Article  Google Scholar 

  11. Aswani R, Ghrera SP, Chandra S, Kar AK (2017) 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, vol 10595, pp 251–260. https://doi.org/10.1007/978-3-319-68557-1_23

  12. Backstrom S, Haslum J (2016) Detecting trends on twitter. In: Degree project in technology, First cycle, 15 Credits Stockholm, Sweden, pp 4–39

  13. Beheshti Z, Shamsuddin SM (2013) A review of population-based meta-heuristic algorithm. Int J Adv Soft Comput Appl 5:1–35

    Google Scholar 

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

    Google Scholar 

  15. Berthon P, Pitt L, Plangger K, Shapiro D (2012) Marketing meets web 2.0, social media, and creative consumers: implications for international marketing strategy. Bus Horiz 55:261–271. https://doi.org/10.1016/j.bushor.2012.01.007

    Article  Google Scholar 

  16. Bhattacharya S, Gaurav K, Ghosh S (2019) Viral marketing on social networks: an epidemiological perspective. Physica A 525:478–490. https://doi.org/10.1016/j.physa.2019.03.008

    Article  MathSciNet  Google Scholar 

  17. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2:1–8. https://doi.org/10.1016/j.jocs.2010.12.007

    Article  Google Scholar 

  18. Borges-Tiago MT, Tiago F, Cosme C (2019) Exploring users’ motivations to participate in viral communication on social media. J Bus Res 101:574–582. https://doi.org/10.1016/j.jbusres.2018.11.011

    Article  Google Scholar 

  19. Cai L, Yu Y, Zhang S, Song Y, Xiong Z, Zhou T (2020) A sample-rebalanced outlier-rejected \(k\)-nearest neighbor regression model for short-term traffic flow forecasting. IEEE Access 8:22686–22696

    Article  Google Scholar 

  20. Cenni D, Nesi P, Pantaleo G, Zaza I (2017) Twitter vigilance: a multi-user platform for cross-domain twitter data analytics, NLP and sentiment analysis. In: IEEE international conference on smart city and innovation, San Francisco, California (USA), pp 1–8. https://doi.org/10.1109/UIC-ATC.2017.8397589

  21. Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring user influence in twitter: the million follower fallacy. In: Proceedings of the 4th international AAAI conference on weblogs and social media (ICWSM), pp 2–8

  22. Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. In: Modeling and optimization in science and technologies, pp 475–494. https://doi.org/10.1007/978-3-319-50920-4_19

  23. Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41:1–58. https://doi.org/10.1145/1541880.1541882

    Article  Google Scholar 

  24. Chen S, Yang R, Yang R, Yang L, Yang X, Xu C, Xu B, Zhang H, Lu Y, Liu W (2016) A parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimization. Discrete Dyn Nat Soc. https://doi.org/10.1155/2016/3795961

    Article  Google Scholar 

  25. Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Modeling wine preferences by data mining from physicochemical properties. Decis Support Syst 47(4):547–553. https://doi.org/10.1016/j.dss.2009.05.016

    Article  Google Scholar 

  26. Cortez P, Cerdeira A, Almeida F, Matos T, Reis J (2009) Wine quality data set—UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/wine+quality

  27. Darwish A (2018) Bio-inspired computing: algorithms review, deep analysis, and the scope of applications. Future Comput Inform J 3:231–246. https://doi.org/10.1016/j.fcij.2018.06.001

    Article  Google Scholar 

  28. Dimopoulos C, Zalzala A (2000) Recent developments in evolutionary computation for manufacturing optimization: problems, solutions and comparisons. IEEE Trans Evol Comput 4:93–113. https://doi.org/10.1109/4235.850651

    Article  Google Scholar 

  29. Dwivedi YK, Kapoor KK, Chen H (2015) Social media marketing and advertising. Mark Rev 15:289–309. https://doi.org/10.1362/146934715X14441363377999

    Article  Google Scholar 

  30. Emary E, Yamany W, Hassanien AE, Snasel V (2015) Multi-objective gray-wolf optimization for attribute reduction. Proc Comput Sci 65:623–632. https://doi.org/10.1016/j.procs.2015.09.006

    Article  Google Scholar 

  31. Emary E, Zawbaa H, Grosan C, Hassanien AE (2014) Feature subset selection approach by gray-wolf optimization. Adv Intell Syst Comput 334:1–13. https://doi.org/10.1007/978-3-319-13572-4_1

    Article  Google Scholar 

  32. Garibay I (2010) Dario floreano and claudio mattiussi (eds): bio-inspired artificial intelligence: theories, methods, and technologies. Genet Program Evol Mach 11:441–443. https://doi.org/10.1007/s10710-010-9104-3

    Article  Google Scholar 

  33. Ghanem TF, Elkilani WS, Abdul-Kader HM (2015) A hybrid approach for efficient anomaly detection using metaheuristic methods. J Adv Res 6:609–619. https://doi.org/10.1016/j.jare.2014.02.009

    Article  Google Scholar 

  34. Gogna A, Tayal A (2013) Metaheuristics: review and application. J Exp Theor Artif Intell 25:503–526. https://doi.org/10.1080/0952813X.2013.782347

    Article  Google Scholar 

  35. Grover P, Kar AK, Dwivedi YK, Janssen M (2018) Polarization and acculturation in US election 2016 outcomes: can twitter analytics predict changes in voting preferences. J Technol Forecast Soc Change 145:438–460

    Article  Google Scholar 

  36. Hassan R, Cohanim B, de Weck O (2005) A comparison of particle swarm optimization and the genetic algorithm. In: Proceedings of the 46th AIAA/ASME/ASCE/AHS/ASC structures, structural dynamics and material conference, vol 2, 1–13. https://doi.org/10.2514/6.2005-1897

  37. Hausmann A (2012) Creating ‘buzz’: opportunities and limitations of social media for arts institutions and their viral marketing. Int J Nonprofit Volunt Sect Mark 17:173–182. https://doi.org/10.1002/nvsm.1420

    Article  Google Scholar 

  38. Ilavarasan V, Rathore A (2018) Social media and business practices. Encycl Inf Sci Technol. https://doi.org/10.4018/978-1-5225-7601-3.ch042

    Article  Google Scholar 

  39. Irsalinda N, Thobirin A, Wijayanti DE (2017) Chicken swarm as a multi step algorithm for global optimization. Int J Eng Sci Invent 6:1–7

    Google Scholar 

  40. Irsalinda N, Yanto ITR, Chiroma H, Herawan T (2017) A framework of clustering based on chicken swarm optimization. Adv Intell Syst Comput. https://doi.org/10.1007/978-3-319-51281-5_34

    Article  Google Scholar 

  41. Jenders M, Kasneci G, Naumann F (2013) Analyzing and predicting viral tweets. In: Proceedings of the 22nd international conference on world wide web, pp 657–664. https://doi.org/10.1145/2487788.2488017

  42. Kapoor KK, Tamilmani K, Rana NP, Patil P, Dwivedi YK, Nerur S (2017) Advances in social media research: past, present and future. Inf Syst Front 20:1–28. https://doi.org/10.1007/s10796-017-9810-y

    Article  Google Scholar 

  43. Kar AK (2016) Bio inspired computing: a review of algorithms and scope of applications. Expert Syst Appl 59:2–50. https://doi.org/10.1016/j.eswa.2016.04.018

    Article  Google Scholar 

  44. Kar AK, Chakraborty A (2016) A review of bio inspired computing methods and potential applications. In: International conference on signal, networks, computing, and systems, vol 396, pp 2–7. https://doi.org/10.1007/978-81-322-3589-7_16

  45. Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report TR06. Technical Report, Erciyes University pp 1–10

  46. Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132. https://doi.org/10.1016/j.amc.2009.03.090

    Article  MathSciNet  MATH  Google Scholar 

  47. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (abc) algorithm. J Glob Optim 39:459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  48. Karaboga D, Basturk B (2008) On the performance of artificial bee colony (abc) algorithm. Appl Soft Comput 8(1):687–697. https://doi.org/10.1016/j.asoc.2007.05.007

    Article  Google Scholar 

  49. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2012) A comprehensive survey: artificial bee colony (abc) algorithm and applications. Artif Intell Rev 42:21–57. https://doi.org/10.1007/s10462-012-9328-0

    Article  Google Scholar 

  50. Kawala F, Douzal A, Gaussier E, Dimert E (2013) Buzz in social media data set: UCI machine learning repository. https://archive.ics.uci.edu/ml/datasets/Buzz+in+social+media+

  51. Khaja S, Banu M, Pappula P (2016) A novel approach for k-NN on unsupervised distance-based outlier detection. Int J Technol Res Eng 4:2347–4718

    Google Scholar 

  52. Khandelwal A, Bhargava A, Sharma A (2019) Voltage stability constrained transmission network expansion planning using fast convergent grey wolf optimization algorithm. Evol Intell. https://doi.org/10.1007/s12065-019-00200-1

    Article  Google Scholar 

  53. 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, WWW ’10, vol 19, 591–600. https://doi.org/10.1145/1772690.1772751

  54. Lamrini B, Gjini A, Daudin S, Armando F, Pratmarty P, Travé-Massuyès L (2018) Anomaly detection using similarity-based one-class SVM for network traffic characterization. In: 29th international workshop on principles of diagnosis, pp 1–8

  55. Leskovec J, Adamic LA, Huberman BA (2007) The dynamics of viral marketing. ACM Trans Web (TWEB) 1:1–39. https://doi.org/10.1145/1232722.1232727

    Article  Google Scholar 

  56. Liang J, Wang L, Ma M, Zhang J (2018) A fast sar image segmentation method based on improved chicken swarm optimization algorithm. Multimed Tools Appl 77:31787–31805. https://doi.org/10.1007/s11042-018-6119-x

    Article  Google Scholar 

  57. Liu Z, hua Guo J, Cao J, Wei Y, Huang W (2018) A hybrid short-term traffic flow forecasting method based on neural networks combined with k-nearest neighbor. Promet Traffic Transp 30:445–456

    Google Scholar 

  58. Lu S, Liu L, Li J, Le TD (2018) Effective outlier detection based on Bayesian network and proximity. In: 2018 IEEE international conference on big data (big data), pp 134–139

  59. Mahmoud HA, Hafez AI, Zawbaa HM, Emary E, Hassanien AE (2015) An innovative approach for feature selection based on chicken swarm optimization. In: International conference of soft computing and pattern recognition (SoCPaR), pp 1–6. https://doi.org/10.1109/SOCPAR.2015.7492775

  60. Marques-Toledo CA, Degener CM, Vinhal L, Coelho G, Meira W, Codeço CT, Teixeira MM (2017) Dengue prediction by the web: tweets are a useful tool for estimating and forecasting dengue at country and city level. PLOS Negl Trop Diseases 11:1–20. https://doi.org/10.1371/journal.pntd.0005729

    Article  Google Scholar 

  61. Meng XB, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization. Lect Notes Comput Sci 8794:86–94. https://doi.org/10.1007/978-3-319-11857-4_10

    Article  Google Scholar 

  62. Mezghani M, Washha M, Sèdes F (2018) Online social network phenomena: buzz, rumor and spam. In: How information systems can help in alarm/alert detection, pp 219–239. https://doi.org/10.1016/B978-1-78548-302-8.50008-3

  63. Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007

    Article  Google Scholar 

  64. Mirjalili SM (2014) How effective is the grey wolf optimizer in training multi-layer perceptrons. Appl Intell 43:150–161

    Article  Google Scholar 

  65. Mohemmed AW, Zhang M, Browne WN (2010) Particle swarm optimisation for outlier detection. In: Proceedings of GECCO, pp 1–2

  66. Murthy D (2015) Twitter and elections: are tweets, predictive, reactive, or a form of buzz? Inf Commun Soc 18:816–831. https://doi.org/10.1080/1369118X.2015.1006659

    Article  Google Scholar 

  67. Nesi P, Pantaleo G, Paoli I, Zaza I (2018) Assessing the retweet proneness of tweets: predictive models for retweeting. Multimed Tools Appl 77:26371–26396. https://doi.org/10.1007/s11042-018-5865-0

    Article  Google Scholar 

  68. Nguyen HT, Chaudhuri M (2019) Making new products go viral and succeed. Int J Res Mark 36(1):39–62. https://doi.org/10.1016/j.ijresmar.2018.09.007

    Article  Google Scholar 

  69. Rahmat G, Primartha R, Sukemi Wijaya A (2019) Comparative analysis of classification method for wart treatment method. J Phys Conf Ser 1196:1–7. https://doi.org/10.1088/1742-6596/1196/1/012012

    Article  Google Scholar 

  70. Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. ACM SIGMOD Record 29:427–438

    Article  Google Scholar 

  71. Rashidi L, Hashemi S, Hamzeh A (2011) Anomaly detection in categorical datasets using Bayesian networks. Artif Intell Comput Intell 7003:610–619. https://doi.org/10.1007/978-3-642-23887-1_78

    Article  Google Scholar 

  72. Reif M, Goldstein M, Stahl A, Breuel TM (2008) Anomaly detection by combining decision trees and parametric densities. In: 2008 19th international conference on pattern recognition, pp 1–4

  73. Roslina ZM, Yanto ITR, Hartama D (2016) A framework of training ANFIS using chicken swarm optimization for solving classification problems. In: International conference on informatics and computing (ICIC), pp 437–441. https://doi.org/10.1109/IAC.2016.7905759

  74. Sahana S (2019) Hybrid optimizer for the travelling salesman problem. Evol Intel 12:1–10. https://doi.org/10.1007/s12065-019-00208-7

    Article  Google Scholar 

  75. Saremi S, Mirjalili SZ, Mirjalili S (2015) Evolutionary population dynamics and grey wolf optimizer. Neural Comput Appl 26:1257–1263. https://doi.org/10.1007/s00521-014-1806-7

    Article  Google Scholar 

  76. Shekhawat S, Shringi S, Sharma H (2020) Twitter sentiment analysis using hybrid spider monkey optimization method. Evol Intell. https://doi.org/10.1007/s12065-019-00334-2

  77. Shukla AK, Kanungo S (2019) Automated face retrieval using bag-of-features and sigmoidal grey wolf optimization. Evol Intell

  78. Song H, Jiang Z, Men A, Yang B (2017) A hybrid semi-supervised anomaly detection model for high-dimensional data. Comput Intell Neurosci 2017:1–9. https://doi.org/10.1155/2017/8501683

    Article  Google Scholar 

  79. Syarif AR, Gata W (2017) Intrusion detection system using hybrid binary PSO and k-nearest neighborhood algorithm. In: 2017 11th international conference on information communication technology and system (ICTS), pp 181–186

  80. Tsai HC (2020) Artificial bee colony directive for continuous optimization. Appl Soft Comput 87:1–70. https://doi.org/10.1016/j.asoc.2019.105982

    Article  Google Scholar 

  81. han Wang X, Zhang Y, yan Sun X, li Wang Y, he Du C (2020) Multi-objective feature selection based on artificial bee colony: an acceleration approach with variable sample size. Appl Soft Comput 88:1–8. https://doi.org/10.1016/j.asoc.2019.106041

    Article  Google Scholar 

  82. Wang Y, Qian Y, Li Y, Gong M, Banzhaf W (2016) Artificial multi-bee-colony algorithm for k-nearest-neighbor fields search. Proc Genet Evol Comput Conf 2016:1037–1044. https://doi.org/10.1145/2908812.2908835

    Article  Google Scholar 

  83. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1:67–82. https://doi.org/10.1109/4235.585893

    Article  Google Scholar 

  84. Wong LI, Sulaiman MH, Mohamed MR, Hong MS (2014) Grey wolf optimizer for solving economic dispatch problems. In: IEEE international conference on power and energy (PECon), pp 150–154. https://doi.org/10.1109/PECON.2014.7062431

  85. Wu D, Kong F, Gao W, Shen Y, Ji Z (2015) Improved chicken swarm optimization. In: IEEE international conference on cyber technology in automation, control, and intelligent systems (CYBER), pp 681–686. https://doi.org/10.1109/CYBER.2015.7288023

  86. Zhou G, Moayedi H, Bahiraei M, Lyu Z (2020) Employing artificial bee colony and particle swarm techniques for optimizing a neural network in prediction of heating and cooling loads of residential buildings. J Clean Prod 254:1–24. https://doi.org/10.1016/j.jclepro.2020.120082

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Jaypee Institute of Information Technology, Noida, 201304, India

    Rupali Jain, Jai Batra & Vinay Anand Tikkiwal

  2. Indian Institute of Technology Delhi, Hauz Khas, Outer Ring Road, New Delhi, 110016, India

    Arpan Kumar Kar

  3. Department of Computer Science & Engineering and Information Technology, Jaypee Institute of Information Technology, Noida, 201304, India

    Himanshu Agrawal

Authors
  1. Rupali Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jai Batra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arpan Kumar Kar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Himanshu Agrawal
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vinay Anand Tikkiwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vinay Anand Tikkiwal.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jain, R., Batra, J., Kar, A.K. et al. A hybrid bio-inspired computing approach for buzz detection in social media. Evol. Intel. 15, 349–367 (2022). https://doi.org/10.1007/s12065-020-00512-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12065-020-00512-7

Keywords

Search

Navigation