Skip to main content

Conversation Graphs in Online Social Media

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12706)

Abstract

In online social media platforms, users can express their ideas by posting original content or by adding comments and responses to existing posts, thus generating virtual discussions and conversations. Studying these conversations is essential for understanding the online communication behavior of users. This study proposes a novel approach to retrieve popular patterns on online conversations using network-based analysis. The analysis consists of two main stages: intent analysis and network generation. Users’ intention is detected using keyword-based categorization of posts and comments, integrated with classification through Naïve Bayes and Support Vector Machine algorithms for uncategorized comments. A continuous human-in-the-loop approach further improves the keyword-based classification. To build and understand communication patterns among the users, we build conversation graphs starting from the hierarchical structure of posts and comments, using a directed multigraph network. The experiments categorize 90% comments with 98% accuracy on a real social media dataset. The model then identifies relevant patterns in terms of shape and content; and finally determines the relevance and frequency of the patterns. Results show that the most popular online discussion patterns obtained from conversation graphs resemble real-life interactions and communication.

Keywords

  • Network analysis
  • Conversation graph
  • Intent analysis
  • Social media
  • Instagram
  • Discourse analysis
  • Online conversation

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-74296-6_8
  • Chapter length: 16 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   89.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-74296-6
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   119.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

Notes

  1. 1.

    http://www.socialmediaexpo2015.com/yourexpo/.

References

  1. Al-Atabi, M., DeBoer, J.: Teaching entrepreneurship using massive open online course (MOOC). Technovation 34(4), 261–264 (2014)

    CrossRef  Google Scholar 

  2. Aragón, P., Gómez, V., Kaltenbrunner, A.: To thread or not to thread: the impact of conversation threading on online discussion. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 11 (2017)

    Google Scholar 

  3. Aumayr, E., Chan, J., Hayes, C.: Reconstruction of threaded conversations in online discussion forums. ICWSM 11, 26–33 (2011)

    Google Scholar 

  4. Balduini, M., et al.: Models and practices in urban data science at scale. Big Data Res. 17, 66–84 (2019)

    Google Scholar 

  5. Bastian, M., Heymann, S., Jacomy, M.: Gephi: an open source software for exploring and manipulating networks. In: Proceedings of 3rd ICWSM (2009)

    Google Scholar 

  6. Brambilla, M., Sabet, A.J., Hosseini, M.: The role of social media in long-running live events: the case of the big four fashion weeks dataset. Data Brief 35, 106840 (2021)

    CrossRef  Google Scholar 

  7. Brena, G., Brambilla, M., Ceri, S., Di Giovanni, M., Pierri, F., Ramponi, G.: News sharing user behaviour on Twitter: a comprehensive data collection of news articles and social interactions. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 592–597 (2019)

    Google Scholar 

  8. Buntain, C., Golbeck, J.: Identifying social roles in reddit using network structure. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 615–620 (2014)

    Google Scholar 

  9. Cha, Y., Kim, J., Park, S., Yi, M.Y., Lee, U.: Complex and ambiguous: understanding sticker misinterpretations in instant messaging. In: Proceedings of the ACM on Human-Computer Interaction, vol. 2(CSCW), November 2018

    Google Scholar 

  10. Chakraborty, K., Bhattacharyya, S., Bag, R.: A survey of sentiment analysis from social media data. IEEE Trans. CSS 7(2), 450–464 (2020)

    Google Scholar 

  11. Cogan, P., Andrews, M., Bradonjic, M., Kennedy, W.S., Sala, A., Tucci, G. Reconstruction and analysis of twitter conversation graphs. In: Proceedings of the 1st ACM International Workshop on HotSocial, pp. 25–31 (2012)

    Google Scholar 

  12. Dillahunt, T.R., Mankoff, J. Understanding factors of successful engagement around energy consumption between and among households. In: Proceedings of the 17th ACM Conference on CSCW, pp. 1246–1257 (2014)

    Google Scholar 

  13. Dong, J.Q., Wu, W.: Business value of social media technologies: Evidence from online user innovation communities. J. Strat. Inf. Sys. 24(2), 113–127 (2015)

    CrossRef  Google Scholar 

  14. Farzan, R., Dabbish, L.A., Kraut, R.E., Postmes, T.: Increasing commitment to online communities by designing for social presence. In: Proceedings of the ACM 2011 Conference on Computer Supported Cooperative Work, pp. 321–330 (2011)

    Google Scholar 

  15. Gasparini, M., Ramponi, G., Brambilla, M., Ceri, S.: Assigning users to domains of interest based on content and network similarity with champion instances. In: Proceedings of the IEEE/ACM Conference on ASONAM, pp. 589–592 (2019)

    Google Scholar 

  16. Sabet, A.J.: Social media posts popularity prediction during long-running live events. a case study on fashion week (2019)

    Google Scholar 

  17. Kumar, R., Mahdian, M., McGlohon, M.: Dynamics of conversations. In: Proceedings of the 16th ACM SIGKDD, pp. 553–562 (2010)

    Google Scholar 

  18. Lai, L.S.L., To, W.M.: Content analysis of social media: a grounded theory approach. J. Electron. Commer. Res. 16(2), 138 (2015)

    Google Scholar 

  19. Leskovec, J., Sosič, R.: Snap: a general-purpose network analysis and graph-mining library. ACM TIST 8(1), 1–20 (2016)

    CrossRef  Google Scholar 

  20. Mcauley, J., Leskovec, J.: Discovering social circles in ego networks. ACM Trans. Knowl. Discovery Data (TKDD) 8(1), 1–28 (2014)

    CrossRef  Google Scholar 

  21. Myers, S.A., Sharma, A., Gupta, P., Lin, J.: Information network or social network? the structure of the twitter follow graph. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 493–498 (2014)

    Google Scholar 

  22. Ning, K., Li, N., Zhang, L.-J.: Using graph analysis approach to support question & answer on enterprise social network. In: IEEE APSCC (2012)

    Google Scholar 

  23. Qualman, E.: Socialnomics: How social media transforms the way we live and do business. Wiley (2012)

    Google Scholar 

  24. Rao , B., Mitra, A.: A new approach for detection of common communities in a social network using graph mining techniques. In: ICHPCA (2014)

    Google Scholar 

  25. Schreck, T., Keim, D.: Visual analysis of social media data. Computer 46(5), 68–75 (2012)

    CrossRef  Google Scholar 

  26. Vasilescu, B., Serebrenik, A., Devanbu, P., Filkov, V.: How social q&a sites are changing knowledge sharing in open source software communities. In: Proceedings of the 17th ACM conference on CSCW, pp. 342–354 (2014)

    Google Scholar 

  27. Baoxun, X., Guo, X., Ye, Y., Cheng, J.: An improved random forest classifier for text categorization. JCP 7(12), 2913–2920 (2012)

    Google Scholar 

  28. Yang, J., McAuley, J., Leskovec, J.: Community detection in networks with node attributes. In: 2013 IEEE 13th ICDM, pp. 1151–1156 (2013)

    Google Scholar 

  29. Zayats, V., Ostendorf, M.: Conversation modeling on reddit using a graph-structured LSTM. Trans. ACL 6, 121–132 (2018)

    Google Scholar 

  30. Zhao, Z., Wei, F., Zhou, M., Ng, W.: Cold-start expert finding in community question answering via graph regularization. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M.A. (eds.) DASFAA 2015. LNCS, vol. 9049, pp. 21–38. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18120-2_2

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marco Brambilla .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Brambilla, M., Javadian, A., Sulistiawati, A.E. (2021). Conversation Graphs in Online Social Media. In: Brambilla, M., Chbeir, R., Frasincar, F., Manolescu, I. (eds) Web Engineering. ICWE 2021. Lecture Notes in Computer Science(), vol 12706. Springer, Cham. https://doi.org/10.1007/978-3-030-74296-6_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-74296-6_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-74295-9

  • Online ISBN: 978-3-030-74296-6

  • eBook Packages: Computer ScienceComputer Science (R0)