Skip to main content
SpringerLink
Log in

Utilizing Twitter Data for Identifying and Resolving Runtime Business Process Disruptions

  • Conference paper
  • First Online:
On the Move to Meaningful Internet Systems. OTM 2018 Conferences (OTM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11229))

Abstract

The advent of web 2.0 technologies represents a paradigm shift in how individuals collaborate in their businesses and daily lives. Web 2.0 opens new opportunities for businesses to reconsider their strategies and operating models by taking a customer-centric approach, which creates a competitive advantage. Business Process Management (BPM) is taking advantage from this phenomenon (aka social business processes or business processes 2.0), embracing ‘social’ and embed it through different stages of the BP lifecycle. This paper contributes by a novel framework for the real-time monitoring and improvement of business processes by analyzing the huge amounts of social data, providing visibility and control, which leads to informed decision making and immediate corrective actions. Thus, the proposed framework bridges in the gap between the social and business worlds. The applicability, efficiency and utility of the proposed approach is validated through its application on a real-life case study of a leading telecommunication company.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    https://www.techopedia.com/definition/29695/sentiment-analysis.

  2. 2.

    https://www.cs.waikato.ac.nz/ml/weka/.

  3. 3.

    https://dev.twitter.com/overview/api.

  4. 4.

    “Other” means that the person is tweeting by un-meaningful words or not a related tweet indicating a potential problem.

References

  1. Almeida, F.: Web 2. 0 technologies and social networking security fears in enterprises. Int. J. Adv. Comput. Sci. Appl. 3, 152–156 (2012)

    Google Scholar 

  2. Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing. ACM Trans. Web. 1, 39 (2007)

    Article  Google Scholar 

  3. Kumar, M., Bala, A.: Analyzing Twitter sentiments through big data. In: 3rd International Conference on Computing for Sustainable Global Development (INDIACom). IEEE (2016)

    Google Scholar 

  4. Troussas, C., Virvou, M., Espinosa, K.J., Llaguno, K., Caro, J.: Sentiment analysis of Facebook statuses using Naive Bayes classifier for language learning. In: 2013 Fourth International Information, Intelligence, Systems and Applications (IISA) (2013)

    Google Scholar 

  5. Isah, H., Trundle, P., Neagu, D.: Social media analysis for product safety using text mining and sentiment analysis. In: 2014 14th UK Workshop on Computational Intelligence (UKCI). IEEE (2014)

    Google Scholar 

  6. Wang, T., Chen, Y.: The power of comments: fostering social interactions in microblog networks. Front. Comput. Sci. 10, 889–907 (2016)

    Article  Google Scholar 

  7. He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manag. 33, 464–472 (2013)

    Article  Google Scholar 

  8. Moseley, N., Alm, C.O., Rege, M.: Toward inferring the age of Twitter users with their use of nonstandard abbreviations and lexicon. In: IEEE 15th International Conference on Information Reuse and Integration, IRI 2014, pp. 219–226 (2014)

    Google Scholar 

  9. Liu, B.: Sentiment Analysis. Cambridge University Press, Cambridge (2015)

    Book  Google Scholar 

  10. Shad Manaman, H., Jamali, S., Aleahmad, A.: Online reputation measurement of companies based on user-generated content in online social networks. Comput. Hum. Behav. 54, 94–100 (2016)

    Article  Google Scholar 

  11. Salampasis, M., Paltoglou, G., Giachanou, A.: Using social media for continuous monitoring and mining of consumer behaviour. Int. J. Electron. Bus. 11, 85 (2013)

    Article  Google Scholar 

  12. Younis, E.: Sentiment analysis and text mining for social media microblogs using open source tools: an empirical study. Int. J. Comput. Appl. 112, 44–48 (2015)

    Google Scholar 

  13. Gürsoy, U.T., Bulut, D., Yiğit, C.: Social media mining and sentiment analysis for brand management. Glob. J. Emerg. Trends e-Business, Mark. Consum. Psychol. Online Int. Res. J. 3, 497–511 (2017)

    Google Scholar 

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

    Article  Google Scholar 

  15. Ruba, K.V., Venkatesan, D.: Building a custom sentiment analysis tool based on an ontology for Twitter posts. Indian J. Sci. Technol. 8, 1–5 (2015)

    Google Scholar 

  16. Ali, F., Kwak, D., Khan, P., Islam, S.M.R., Kim, K.H., Kwak, K.S.: Fuzzy ontology-based sentiment analysis of transportation and city feature reviews for safe traveling. Transp. Res. Part C Emerg. Technol. 77, 33–48 (2017)

    Article  Google Scholar 

  17. Zehra, S., Wasi, S., Jami, I., Nazir, A., Khan, A., Waheed, N.: Ontology-based sentiment analysis model for recommendation systems. In: 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KEOD 2017), pp. 155–160 (2017)

    Google Scholar 

  18. Ryota, K., Tomoharu, N.: Stock market prediction based on interrelated time series data. In: IEEE Symposium on Computers and Informatics, ISCI 2012, pp. 17–21 (2012)

    Google Scholar 

  19. Pagolu, V.S., Challa, K.N.R., Panda, G., Majhi, B.: Sentiment analysis of Twitter data for predicting stock market movements. In: International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1–6 (2016)

    Google Scholar 

  20. Pimprikar, R., Ramachandran, S., Senthilkumar, K.: Use of machine learning algorithms and twitter sentiment analysis for stock market prediction. Int. J Pure Appl. Math. 115, 521–526 (2017)

    Google Scholar 

  21. Gaikar, D.D., Marakarkandy, B., Dasgupta, C.: Using Twitter data to predict the performance of Bollywood movies. Ind. Manag. Data Syst. 115, 1604–1621 (2015)

    Article  Google Scholar 

  22. Sumner, C., Byers, A., Boochever, R., Park, G.J.: Predicting dark triad personality traits from Twitter usage and a linguistic analysis of tweets. In: 11th International Conference on Machine Learning and Applications. IEEE (2012)

    Google Scholar 

  23. Golbeck, J., Robles, C., Turner, K.: Predicting personality with social media. In: CHI EA 2011 CHI 2011 Extended Abstracts on Human Factors in Computing Systems, pp. 253–262. ACM (2011)

    Google Scholar 

  24. Hauder, M.: Bridging the gap between social software and business process management : a research agenda. In: Seventh International Conference on Research Challenges in Information Science (RCIS), pp. 1–6. IEEE (2013)

    Google Scholar 

  25. Yunus, M., Moingeon, B., Lehmann-Ortega, L.: Building social business models: lessons from the Grameen experience. Long Range Plan. 43, 308–325 (2010)

    Article  Google Scholar 

  26. Maamar, Z., Sakr, S., Faci, N., Boukhebouze, M., Barnawi, A.: SUPER: social-based business process management framework. In: Toumani, F., et al. (eds.) ICSOC 2014. LNCS, vol. 8954, pp. 413–417. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22885-3_38

    Chapter  Google Scholar 

  27. Maamar, Z., Burégio, V., Sellami, M.: Collaborative enterprise applications based on business and social artifacts. In: 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises, pp. 150–155 (2015)

    Google Scholar 

  28. Brun, Y., et al.: Engineering self-adaptive systems through Feedback loops. In: Cheng, Betty H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-02161-9_3

    Chapter  Google Scholar 

  29. Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Comput. Soc. 36, 41–50 (2003). Home Community Technology Leaders

    Article  Google Scholar 

  30. IBM: An architectural blueprint for autonomic computing. IBM (2005)

    Google Scholar 

  31. Salehie, M., Tahvildari, L.: Self-adaptive software: landscape and research challenges. ACM Trans. Auton. Adapt. Syst. 4, Article no. 14 (2009)

    Article  Google Scholar 

  32. Luckham, D.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley Longman Publishing Co., Inc., Boston (2001)

    Google Scholar 

  33. Awad, A., Barnawi, A., Elgammal, A., Elshawi, R., Almalaise, A., Sakr, S.: Runtime detection of business process compliance violations: an approach based on anti patterns. In: Proceedings of the 30th Annual ACM Symposium on Applied Computing, pp. 1203–1210. ACM (2015)

    Google Scholar 

  34. Barnawi, A., Awad, A., Elgammal, A., El Shawi, R., Almalaise, A., Sakr, S.: Runtime self-monitoring approach of business process compliance in cloud environments. Clust. Comput. 18, 1503–1526 (2015)

    Article  Google Scholar 

  35. Zou, L., Song, W.W.: LDA-TM: a two-step approach to Twitter topic data clustering. In: IEEE International Conference on Cloud Computing and Big Data Analysis, ICCCBDA 2016, pp. 342–347 (2016)

    Google Scholar 

  36. Cortes, C., Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)

    MATH  Google Scholar 

  37. Louppe, G.: Understanding random forests from theory to practice (2014)

    Google Scholar 

  38. Metzger, A., et al.: Comparing and combining predictive business process monitoring techniques. IEEE Trans. Syst. Man Cybern. Syst. 45, 276–290 (2015)

    Article  Google Scholar 

  39. Xu, S., Li, Y., Wang, Z.: Bayesian multinomial Naïve Bayes classifier to text classification. In: Park, James J.(Jong Hyuk), Chen, S.-C., Raymond Choo, K.-K. (eds.) MUE/FutureTech -2017. LNEE, vol. 448, pp. 347–352. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-5041-1_57

    Chapter  Google Scholar 

  40. Costa, E.P., Lorena, A.C., Carvalho, A.C.P.L.F., Freitas, A.A.: A Review of performance evaluation measures for hierarchical classifiers. In: Evaluation Methods for Machine Learning II: Papers from AAAI-2007 Work, pp. 1–6 (2007)

    Google Scholar 

  41. Allahyari, M., Trippe, E.D., Gutierrez, J.B.: A brief survey of text mining : classification, clustering and extraction techniques. ArXiv: 1 (2017)

    Google Scholar 

  42. Ellison, N.B., Gibbs, J.L., Weber, M.S.: The use of enterprise social network sites for knowledge sharing in distributed organizations. Am. Behav. Sci. 59, 103–123 (2015)

    Article  Google Scholar 

Download references

Acknowledgements

We wish to thank Dr. Ahmed Awad, Institute of Computer Science, University of Tartu, Estonia, for providing the essentials of the case study in this paper and for the fruitful discussions and advices.

Author information

Authors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Cairo, Egypt

    Alia Ayoub & Amal Elgammal

Authors
  1. Alia Ayoub
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amal Elgammal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alia Ayoub .

Editor information

Editors and Affiliations

  1. CNRS, University of Lorraine, Vandoeuvre-les-Nancy, France

    Hervé Panetto

  2. Trinity College Dublin, Dublin, Ireland

    Christophe Debruyne

  3. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Henderik A. Proper

  4. Università degli Studi di Milano, Crema, Italy

    Claudio Agostino Ardagna

  5. SINTEF and University of Oslo, Oslo, Norway

    Dumitru Roman

  6. TU Graz, Graz, Austria

    Robert Meersman

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ayoub, A., Elgammal, A. (2018). Utilizing Twitter Data for Identifying and Resolving Runtime Business Process Disruptions. In: Panetto, H., Debruyne, C., Proper, H., Ardagna, C., Roman, D., Meersman, R. (eds) On the Move to Meaningful Internet Systems. OTM 2018 Conferences. OTM 2018. Lecture Notes in Computer Science(), vol 11229. Springer, Cham. https://doi.org/10.1007/978-3-030-02610-3_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-02610-3_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02609-7

  • Online ISBN: 978-3-030-02610-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation