Skip to main content

Advertisement

SpringerLink
Log in

Study of collective user behaviour in Twitter: a fuzzy approach

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

The study of collective user behaviours in social networking sites has become an increasing important topic in social media mining. Understanding such behaviours has its potential to extract actionable patterns that can be beneficial to develop effective marketing strategies, optimise user experiences and maximise website revenues. With the rapid development of micro-blogging, Twitter has become a richer source of intelligence that can be used to study collective user behaviour, due to its efficient and meaningful user-to-user interactions. However, the classical statistical methods have some drawbacks in bridging the gap between user-generated data and human analysts who mostly use linguistic terms to analyse data and model/summarise knowledge learned. To address this gap, this work proposes a new approach, which employs the mass assignment theory-based fuzzy association rules algorithm (MASS-FARM), for the first time, to extract useful interaction behaviour of Twitter users. The influential factors (including activity time, number of friends/followers and the number of tweets) are represented as fuzzy granules, and the associations amongst are studied by employing MASS-FARM. The collective user behaviours are analysed in the Reply category and the Non-Reply category, respectively. The applicability and usefulness of the proposed method are demonstrated via an empirical study on a collected Twitter data set. The derived results are also discussed and compared with existing works.

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
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. A fuzzy subset of the universe corresponds to a granule in this paper.

References

  1. Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: SIGMOD ’93: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, ACM, New York, pp 207–216

  2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large Data Bases, VLDB

  3. Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: Membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 44–54

  4. Backstrom L, Leskovec J (2011) Supervised random walks: Predicting and recommending links in social networks. In: Proceedings of the fourth ACM international conference on Web Search and data mining, pp 635–644

  5. Baldwin J, Lawry J, Martin TP (1996) Efficient algorithms for semantic unification. In: Information processing and the management of uncertainty

  6. Benevenuto F, Rodrigues T, Cha M, Almeida VAF (2009) Characterizing user behavior in online social networks. In: Internet measurement conference, pp 49–62

  7. Bosc P, Pivert O (2001) On some fuzzy extensions of association rules. In: IFSA World Congress and 20th NAFIPS international conference, 2001. Joint 9th, vol 2, pp 1104–1109

  8. Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J Comput Sci 2(1):1–8

    Article  Google Scholar 

  9. Brunelli M, Fedrizzi M (2009) A fuzzy approach to social network analysis. In: ASONAM ’09: Proceedings of the 2009 international conference on advances in social network analysis and mining. IEEE Computer Society, Washington, DC, pp 225–230

  10. Cagman N, Citak F, Aktas H (2012) Soft int-group and its applications to group theory. Neural Comput Appl 21(1 Supplement):151–158

    Article  Google Scholar 

  11. Chu Z, Gianvecchio S, Wang H, Jajodia S (2010) Who is tweeting on twitter: Human, bot, or cyborg? In: Proceedings of the 26th annual computer security applications conference, pp 21–30

  12. Delgado M, Marn N, Snchez D, amparo Vila M (2003) Fuzzy association rules: general model and applications. IEEE Trans Fuzzy Syst 11:214–225

    Article  Google Scholar 

  13. Dodds P, Harris K, Kloumann I, Bliss C, Danforth C (2011) Temporal patterns of happiness and information in a global social network: Hedonometrics and twitter. PLoS ONE 6(12):e26752. doi:10.1371/journal.pone.0026752

  14. Dubois D, Hllermeier E, Prade H (2006) A systematic approach to the assessment of fuzzy association rules. Data Min Knowl Disc 13(2):167–192

    Article  Google Scholar 

  15. Fu X, Shen Q (2010) Fuzzy compositional modelling. IEEE Trans Fuzzy Syst 18(4):823–840

    Article  Google Scholar 

  16. Fu X, Shen Q (2011) Fuzzy complex numbers and their application for classifiers performance evaluation. Pattern Recogn 44(7):1403–1417

    Article  MATH  Google Scholar 

  17. Golder SA, Macy M (2011) Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science 333:1878–1881

    Article  Google Scholar 

  18. Gundecha P, Liu H (2012) Mining social media: a brief introduction. In: Tutorials in operations research—new directions in informatics, optimization, logistics, and production (INFORMS), pp 1–17

  19. Ikeda K, Hattori G, Ono C, Asoh H, Higashino T (2013) Twitter user profiling based on text and community mining for market analysis. Knowl Based Syst 51:35–47

    Article  Google Scholar 

  20. Java A, Song X, Finin T, Tseng B (2007) Why we twitter: understanding microblogging usage and communities. In: WebKDD/SNA-KDD ’07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis. ACM, New York, NY, pp 56–65

  21. Kacprzyk J, Zadrozny S (2003) Linguistic summarization of data sets using association rules. In: Fuzzy Systems, 2003. The 12th IEEE international conference on FUZZ ’03, vol 1, pp 702–707

  22. Khan FH, Bashir S, Qamar U (2013) Tom: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems (0)

  23. Konstas I, Stathopoulos V, Jose JM (2009) On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval, pp 195–202

  24. Kontopoulos E, Berberidis C, Dergiades T, Bassiliades N (2013) Ontology-based sentiment analysis of twitter posts. Expert Syst Appl 40(10):4065–4074

    Article  Google Scholar 

  25. Krajci S, Krajciova J (2007) Social network and one-sided fuzzy concept lattices. In. In Proceedings of the 16th international conference on Fuzzy systems, pp 1–6

  26. Krishnamurthy B, Gill P, Arlitt M (2008) A few chirps about twitter. In: WOSP ’08: Proceedings of the first workshop on Online social networks. ACM, New York, NY, pp 19–24

  27. Martin T, Shen Y, Majidian A (2010) Discovery of time-varying relations using fuzzy formal concept analysis and associations. J Intell Syst 25(12):1217–1248

    Article  MATH  Google Scholar 

  28. Martin TP, Shen Y, Azvine B (2008) Incremental evolution of fuzzy grammar fragments to enhance instance matching and text mining. IEEE Trans Fuzzy Syst 16(6):1425–1438

    Article  Google Scholar 

  29. Newman M (2006) Finding community structure in networks using the eigenvectors of matrices. Phys Rev E 74(3):1–22

    Article  Google Scholar 

  30. Tang L, Liu H (2009) Scalable learning of collective behavior based on sparse social dimensions. In: CIKM ’09: Proceeding of the 18th ACM conference on information and knowledge management. ACM, pp 1107–1116

  31. Tang L, Liu H (2010) Towards predicting collective behaviour via social dimension extraction. IEEE Intell Syst 25:19–25

    Article  Google Scholar 

  32. Yager RR (1988) On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Trans Syst Man Cybern 18(1):183–190

    Article  MATH  MathSciNet  Google Scholar 

  33. Yager RR (2008) Intelligent social network analysis using granular computing. Int J Intell Syst 23(11):1196–1219

    Google Scholar 

  34. Yardi S, Romero D, Schoenebeck G, Boyd D (2009) Detecting spam in a twitter network. First Monday 15(1):1–4

    Article  Google Scholar 

  35. Zadeh LA (1965) Fuzzy sets. Inform Contl 8:338–353

    Article  MATH  MathSciNet  Google Scholar 

  36. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inform Sci 9(1):43–80

    Article  MATH  MathSciNet  Google Scholar 

  37. Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

    Article  MATH  MathSciNet  Google Scholar 

  38. Zadeh LA (2000) From computing with numbers to computing with words—from manipulation of measurements to manipulation of perceptions. In: Intelligent systems and soft computing, pp 3–40

  39. Zhang S, Wang R, Zhang X (2007) Identification of overlapping community structure in complex networks using fuzzy cc-means clustering. Phys A 374(1):483–490

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Nature Science Foundation of China (No. 71301133, 71371159) and Humanity and Social Science Youth foundation of Ministry of Education, China (Grant No. 13YJC630033). The authors are also grateful to the referees for their invaluable and insightful comments that have helped significantly to improve this work.

Author information

Authors and Affiliations

  1. Department of Management Sciences, School of Management, Xiamen University, Xiamen, 361005, China

    Xin Fu

  2. Symantec Research Labs, Ballycoolin Business Park, Dublin 15, Ireland

    Yun Shen

Authors
  1. Xin Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yun Shen.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, X., Shen, Y. Study of collective user behaviour in Twitter: a fuzzy approach. Neural Comput & Applic 25, 1603–1614 (2014). https://doi.org/10.1007/s00521-014-1642-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1642-9

Keywords

Search

Navigation