Skip to main content

Posting Topics \(\ne \) Reading Topics: On Discovering Posting and Reading Topics in Social Media

  • Conference paper
  • First Online:
Advances in Network Science (NetSci-X 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9564))

Included in the following conference series:

Abstract

Social media users make decisions about what content to post and read. As posted content is often visible to others, users are likely to impose self-censorship when deciding what content to post. On the other hand, such a concern may not apply to reading social media content. As a result, the topics of content that a user posted and read can be different and this has major implications to the applications that require personalization. To better determine and profile social media users’ topic interests, we conduct a user survey in Twitter. In this survey, participants chose the topics they like to post (posting topics) and the topics they like to read (reading topics). We observe that users’ posting topics differ from their reading topics significantly. We find that some topics such as “Religion”, “Business” and “Politics” attract much more users to read than to post. With the ground truth data obtained from the survey, we further explore the discovery of users’ posting and reading topics separately using features derived from their posted content, received content and social networks.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Twitter accounts are considered as personal identifiable information, so we can not use Amazon Mechanical Turk (AMT) for conducting this survey. The restrictions of using AMT: https://requester.mturk.com/help/faq#restrictions_use_mturk.

References

  1. Balasubramanian, S., Mahajan, V.: The economic leverage of the virtual community. Int. J. Electron. Commer. 5(3), 103–138 (2001)

    Google Scholar 

  2. Benevenuto, F., Rodrigues, T., Cha, M., Almeida, V.: Characterizing user behavior in online social networks. In: IMC (2009)

    Google Scholar 

  3. Bhattacharya, P., Zafar, M.B., Ganguly, N., Ghosh, S., Gummadi, K.P.: Inferring user interests in the twitter social network. In: RecSys (2014)

    Google Scholar 

  4. Chen, J., Hsieh, G., Mahmud, J.U., Nichols, J.: Understanding individuals’ personal values from social media word use. In: CSCW (2014)

    Google Scholar 

  5. Das, S., Kramer, A.: Self-censorship on Facebook. In: ICWSM (2013)

    Google Scholar 

  6. Davidson, J., Liebald, B., Liu, J., Nandy, P., Van Vleet, T., Gargi, U., Gupta, S., He, Y., Lambert, M., Livingston, B., Sampath, D.: The youtube video recommendation system. In: RecSys (2010)

    Google Scholar 

  7. Diao, Q., Jiang, J.: A unified model for topics, events and users on Twitter. In: EMNLP (2013)

    Google Scholar 

  8. Ghosh, S., Sharma, N., Benevenuto, F., Ganguly, N., Gummadi, K.: Cognos: crowdsourcing search for topic experts in microblogs. In: SIGIR (2012)

    Google Scholar 

  9. Gong, W., Lim, E.P., Zhu, F.: Characterizing silent users in social media communities. In: ICWSM (2015)

    Google Scholar 

  10. Hampton, K., Rainie, L., Lu, W., Dwyer, M., Shin, I., Purcell, K.: Social media and the “spiral of silence”. Pew Research Internet Project (2014). http://www.pewinternet.org/2014/08/26/social-media-and-the-spiral-of-silence/

  11. Hong, L., Bekkerman, R., Adler, J., Davison, B.D.: Learning to rank social update streams. In: SIGIR (2012)

    Google Scholar 

  12. Hsieh, G., Chen, J., Mahmud, J.U., Nichols, J.: You read what you value: understanding personal values and reading interests. In: CHI (2014)

    Google Scholar 

  13. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. PNAS 110(15), 5802–5850 (2013)

    Article  Google Scholar 

  14. Kouloumpis, E., Wilson, T., Moore, J.: Twitter sentiment analysis: the good the bad and the OMG! In: ICWSM (2011)

    Google Scholar 

  15. Kulshrestha, J., Zafar, M.B., Noboa, L.E., Gummadi, K.P., Ghosh, S.: Characterizing information diets of social media users. In: ICWSM (2015)

    Google Scholar 

  16. Lebanon, G., Lafferty, J.D.: Cranking: combining rankings using conditional probability models on permutations. In: ICML (2002)

    Google Scholar 

  17. Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: WWW (2008)

    Google Scholar 

  18. Linden, G., Smith, B., York, J.: Amazon. com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  19. Michelson, M., Macskassy, S.A.: Discovering users’ topics of interest on twitter: a first look. In: AND (2010)

    Google Scholar 

  20. Nonnecke, B., Preece, J.: Lurker demographics: counting the silent. In: CHI (2000)

    Google Scholar 

  21. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)

    Google Scholar 

  22. Sleeper, M., Balebako, R., Das, S., McConahy, A.L., Wiese, J., Cranor, L.F.: The post that wasn’t: exploring self-censorship on facebook. In: CSCW (2013)

    Google Scholar 

  23. Spasojevic, N., Yan, J., Rao, A., Bhattacharyya, P.: Lasta: large scale topic assignment on multiple social networks. In: KDD (2014)

    Google Scholar 

  24. Tagarelli, A., Interdonato, R.: “Who’s out there?”: identifying and ranking lurkers in social networks. In: ASONAM (2013)

    Google Scholar 

  25. Wang, G., Konolige, T., Wilson, C., Wang, X., Zheng, H., Zhao, B.Y.: You are how you click: clickstream analysis for Sybil detection. In: SEC (2013)

    Google Scholar 

  26. Xu, Z., Ru, L., Xiang, L., Yang, Q.: Discovering user interest on twitter with a modified author-topic model. In: WI-IAT (2011)

    Google Scholar 

  27. Yang, Z., Xu, J., Li, X.: Data selection for user topic model in twitter-like service. In: ICPADS (2011)

    Google Scholar 

  28. Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing Twitter and traditional media using topic models. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 338–349. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

Download references

Acknowledgments

The authors gratefully thank Ingmar Weber for the inspiring discussion. This research is supported by the Singapore National Research Foundation under its International Research Centre @ Singapore Funding Initiative and administered by the IDM Programme Office, Media Development Authority (MDA).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Gong .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Gong, W., Lim, EP., Zhu, F. (2016). Posting Topics \(\ne \) Reading Topics: On Discovering Posting and Reading Topics in Social Media. In: Wierzbicki, A., Brandes, U., Schweitzer, F., Pedreschi, D. (eds) Advances in Network Science. NetSci-X 2016. Lecture Notes in Computer Science(), vol 9564. Springer, Cham. https://doi.org/10.1007/978-3-319-28361-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28361-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28360-9

  • Online ISBN: 978-3-319-28361-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics