Skip to main content
SpringerLink
Log in

User Identification on Social Networks Through Text Mining Techniques: A Systematic Literature Review

  • Conference paper
  • First Online:
Information Science and Applications 2018 (ICISA 2018)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 514))

Included in the following conference series:

Abstract

Social connection between the set of people is known as social network analysis. People keep numerous identities on various online social sites. User-related network data has distinctive information which shows user interests, behavioral patterns, and political views. By using these behaviors individually and collectively are of great help to recognize users across social networks. SLR (Systematic Literature Review) has been performed to distinguish 31 papers published during 2010–2018. The idea is to determine user identification categories that are used to classify users. Furthermore, to identify algorithms, models, methods, and tools that has been suggested since 2010 for user characterization. We have identified 10 algorithms, 19 models, 5 methods and 8 tools that have proposed for 5 user identification categories. Finally, we empirically evaluated that text mining techniques are promising approaches for the identification of users on online social networks.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Gao H, Hu J, Wilson C, Li Z, Chen Y, Zhao BY (2010) Detecting and characterizing social spam campaigns. In: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, Nov 2010. ACM, pp 35–47

    Google Scholar 

  2. Tuna T, Akbas E, Aksoy A, Canbaz MA, Karabiyik U, Gonen B, Aygun R (2016) User characterization for online social networks. Soc Netw Anal Mining 6(1):104

    Article  Google Scholar 

  3. Perikos I, Hatzilygeroudis I (2016) Recognizing emotions in text using ensemble of classifiers. Eng Appl Artif Intell 51:191–201

    Article  Google Scholar 

  4. Sboev A, Litvinova T, Gudovskikh D, Rybka R, Moloshnikov I (2016) Machine learning models of text categorization by author gender using topic-independent features. Proc Comput Sci 101:135–142

    Article  Google Scholar 

  5. Kitchenham B (2004) Procedures for performing systematic reviews, Keele, UK, Keele University, vol 33, no 2004, pp 1–26

    Google Scholar 

  6. Poria S, Cambria E, Gelbukh A, Bisio F, Hussain A (2015) Sentiment data flow analysis by means of dynamic linguistic patterns. IEEE Comput Intell Mag 10(4):26–36

    Article  Google Scholar 

  7. Qian X, Feng H, Zhao G, Mei T (2014) Personalized recommendation combining user interest and social circle. IEEE Trans Knowl Data Eng 26(7):1763–1777

    Article  Google Scholar 

  8. Murkute AM, Gadge J (2015) Framework for user identification using writeprint approach. In: 2015 international conference on technologies for sustainable development (ICTSD), Feb. IEEE, pp 1–5

    Google Scholar 

  9. Amuchi F, Al-Nemrat A, Alazab M, Layton R (2012) Identifying cyber predators through forensic authorship analysis of chat logs. In: 2012 third cybercrime and trustworthy computing workshop (CTC), Oct. IEEE, pp 28–37

    Google Scholar 

  10. Wang J, Liu Z, Zhao H (2014) Group recommendation using topic identification in social networks. In: 2014 sixth international conference on intelligent human-machine systems and cybernetics (IHMSC), vol 1, Aug. IEEE, pp 355–358

    Google Scholar 

  11. Yin C, Xiang J, Zhang H, Wang J, Yin Z, Kim JU (2015) A new SVM method for short text classification based on semi-supervised learning. In: 2015 4th international conference on advanced information technology and sensor application (AITS), Aug. IEEE, pp 100–103

    Google Scholar 

  12. Meda C, Ragusa E, Gianoglio C, Zunino R, Ottaviano A, Scillia E, Surlinelli R (2016) Spam detection of Twitter traffic: a framework based on random forests and non-uniform feature sampling. In: 2016 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), Aug. IEEE, pp 811–817

    Google Scholar 

  13. Guo H, Chen Y (2016) User interest detecting by text mining technology for microblog platform. Arab J Sci Eng 41(8):3177–3186

    Article  Google Scholar 

  14. Zhang Y, He J, Xu J (2018) A new anti-spam model based on e-mail address concealment technique. Wuhan Univ J Nat Sci 23(1):79–83

    Article  Google Scholar 

  15. Ding Y, Meng X, Chai G, Tang Y (2011) User identification for instant messages. In: Neural information processing. Springer Berlin/Heidelberg, pp 113–120

    Chapter  Google Scholar 

  16. Ma J, Teng G, Chang S, Zhang X, Xiao K (2011) Social network analysis based on authorship identification for cybercrime investigation. Intell Secur Inf 27–35

    Google Scholar 

  17. Frommholz I, Al-Khateeb HM, Potthast M, Ghasem Z, Shukla M, Short E (2016) On textual analysis and machine learning for cyberstalking detection. Datenbank-Spektrum 16(2):127–135

    Article  Google Scholar 

  18. Chavoshi N, Hamooni H, Mueen A (2016) Identifying correlated bots in twitter. In: International Conference on Social Informatics, Nov. Springer International Publishing, pp 14–21

    Google Scholar 

  19. Santos I, Minambres-Marcos I, Laorden C, Galán-García P, Santamaría-Ibirika A, Bringas PG (2014) Twitter content-based spam filtering. In: International joint conference SOCO’13-CISIS’13-ICEUTE’13. Springer, Cham, pp 449–458

    Google Scholar 

  20. Zhou X, Wu B, Jin Q (2017) User role identification based on social behavior and networking analysis for information dissemination. Future Gener Comput Syst

    Google Scholar 

  21. Qiu Z, Shen H (2017) User clustering in a dynamic social network topic model for short text streams. Inf Sci 414:102–116

    Article  Google Scholar 

  22. Sharef NM, Martin T (2015) Evolving fuzzy grammar for crime texts categorization. Appl Soft Comput 28:175–187

    Article  Google Scholar 

  23. Zaeem RN, Manoharan M, Yang Y, Barber KS (2017) Modeling and analysis of identity threat behaviors through text mining of identity theft stories. Comput Secur 65:50–63

    Article  Google Scholar 

  24. Liang J, Liu P, Tan J, Bai S (2014) Sentiment classification based on AS-LDA model. Proc Comput Sci 31:511–516

    Article  Google Scholar 

  25. Chelmis C, Prasanna VK (2013) Social link prediction in online social tagging systems. ACM Trans Inf Syst (TOIS) 31(4):20

    Article  Google Scholar 

  26. Manne S, Fatima SS (2012) An extensive empirical study of feature terms selection for text summarization and categorization. In: Proceedings of the second international conference on computational science, engineering and information technology, Oct. ACM, pp 606–613

    Google Scholar 

  27. Chakraborti S (2015) Multi-document text summarization for competitor intelligence: a methodology based on topic identification and artificial bee colony optimization. In: Proceedings of the 30th annual ACM symposium on applied computing, Apr. ACM, pp 1110–1111

    Google Scholar 

  28. Choi D, Han J, Chung T, Ahn YY, Chun BG, Kwon TT (2015) Characterizing conversation patterns in Reddit: from the perspectives of content properties and user participation behaviors. In: Proceedings of the 2015 ACM on conference on online social networks, Nov. ACM, pp 233–243

    Google Scholar 

  29. Inches G, Crestani F (2011) Online conversation mining for author characterization and topic identification. In: Proceedings of the 4th workshop on workshop for Ph.D. students in information & knowledge management, Oct. ACM, pp 19–26

    Google Scholar 

  30. Zhao Y, Liang S, Ren Z, Ma J, Yilmaz E, de Rijke M (2016) Explainable user clustering in short text streams. In: Proceedings of the 39th international ACM SIGIR conference on research and development in information retrieval, July. ACM, pp 155–164

    Google Scholar 

  31. O’Riordan S, Feller J, Nagle T (2016) A categorisation framework for a feature-level analysis of social network sites. J Decis Syst 25(3):244–262

    Article  Google Scholar 

  32. Son JE, Lee SH, Cho EY, Kim HW (2016) Examining online citizenship behaviours in social network sites: a social capital perspective. Behav Inf Technol 35(9):730–747

    Article  Google Scholar 

  33. Riedl C, Köbler F, Goswami S, Krcmar H (2013) Tweeting to feel connected: a model for social connectedness in online social networks. Int J Hum-Comput Interact 29(10):670–687

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, College of E&ME, National University of Sciences and Technology (NUST), Islamabad, 12, Pakistan

    Kinza Zahra, Farooque Azam, Wasi Haider Butt & Fauqia Ilyas

Authors
  1. Kinza Zahra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Farooque Azam
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wasi Haider Butt
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fauqia Ilyas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kinza Zahra .

Editor information

Editors and Affiliations

  1. iCatse, Seongnam, Gyeonggi, Korea (Republic of)

    Kuinam J. Kim

  2. School of Computer Science and Engineering, Kyungpook National University, Daegu, Korea (Republic of)

    Nakhoon Baek

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zahra, K., Azam, F., Butt, W.H., Ilyas, F. (2019). User Identification on Social Networks Through Text Mining Techniques: A Systematic Literature Review. In: Kim, K., Baek, N. (eds) Information Science and Applications 2018. ICISA 2018. Lecture Notes in Electrical Engineering, vol 514. Springer, Singapore. https://doi.org/10.1007/978-981-13-1056-0_49

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1056-0_49

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1055-3

  • Online ISBN: 978-981-13-1056-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation