Skip to main content
SpringerLink
Log in

Factors dominating individual information disseminating behavior on social networking sites

  • Published:
Information Technology and Management Aims and scope Submit manuscript

Abstract

Identifying dominating features that affect individual information retweeting behavior on social networking sites (SNSs) is crucial to understanding individual retweeting behaivor and developing effective marketing strategies on SNS. However, there is little agreement on what factors are dominating individual information disseminating behavior on SNS, and what’s worse, more and more factors are added into the prediction model, without examining the relevance of them and even why these factors are added is rarely discussed. This leads to undesirable outcomes such as increasing the cost of measuring and computing irrelevant/redundant features. Most importantly, it hinders us from understanding what discriminative features are affecting individual information disseminating behavior. Using a unique real-life Twitter data set consisting of 55,575 twitterers and 9,440,321 tweets, the authors examine what discriminative features are dominating individual information disseminating behavior. The results indicate that topic distance is the most discriminative factor, highlighting that self-presentation motives play an important role in information disseminating decisions. Besides, the amount of information, social relationship and the popularity of the tweet also contribute to individual information disseminating decisions. Experiments demonstrate that adopting only dominating factors can improve prediction performance in terms of various indicators, compared with adopting the full features set. Finally, we conclude the paper by discussing theoretical and practical implications of our findings.

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.

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
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. https://www.wired.com/2013/02/oreo-twitter-super-bowl/

  2. http://textblob.readthedocs.org/en/dev/

  3. 0.00–0.19: very weak; 0.20–0.39: weak; 0.40–0.59: moderate; 0.60–0.79: strong; 0.80–1.00: very strong.

References

  1. Allsop DT, Bassett BR, Hoskins JA (2007) Word-of-mouth research: principles and applications. J Adv Res 47(4):398–411

    Article  Google Scholar 

  2. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) Everyone’s an influencer: quantifying influence on twitter. In: Gesundheitceedings of the fourth ACM international conference on Web search Bakshy and data mining. ACM, pp 65–74

  3. Berger J, Milkman KL (2012) What makes online content viral? J Mark Res 49(2):192–205

    Article  Google Scholar 

  4. Cheung CMK, Thadani DR (2012) The impact of electronic word-of-mouth communication: a literature analysis and integrative model. Decis Support Syst 54(1):461–470

    Article  Google Scholar 

  5. Cohen J (2013) Statistical power analysis for the behavioral sciences. Academic Press, New York

    Google Scholar 

  6. Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340

    Article  Google Scholar 

  7. Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407

    Article  Google Scholar 

  8. Deutsch M, Gerard HB (1955) A study of normative and informational social influences upon individual judgment. J Abnorm Soc Psychol 51(3):629

    Article  Google Scholar 

  9. Evans JD (1996) Straightforward statistics for the behavioral sciences. Brooks/Cole, Pacific Grove

    Google Scholar 

  10. Feng W, Wang J (2013) Retweet or not? Personalized tweet re-ranking. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM, pp 577–586

  11. Flint JT (1997) Toward transformation of social knowledge. Int Stud Philos 29(4):115–116

    Article  Google Scholar 

  12. Goldhaber MH (2006) The value of openness in an attention economy. First Mon 11(6). http://firstmonday.org/issues/issue11_6/goldhaber/index.html

  13. Gupta P, Harris J (2010) How e-WOM recommendations influence product consideration and quality of choice: a motivation to process information perspective. J Bus Res 63(9):1041–1049

    Article  Google Scholar 

  14. Hong L, Davison BD (2010) Empirical study of topic modeling in twitter. In: Proceedings of the first workshop on social media analytics. ACM, pp 80–88

  15. Hong L, Dan O, Davison BD (2011) Predicting popular messages in twitter. In: Proceedings of the 20th international conference companion on world wide web. ACM, pp 57–58

  16. Ivakhnenko AG, Ivakhnenko GA (1995) The review of problems solvable by algorithms of the group method of data handling (GMDH). Pattern Recogn Image Anal C/C Raspozn Obrazov I Analiz Izobrazhenii 5:527–535

    Google Scholar 

  17. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York

    Book  Google Scholar 

  18. Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the 19th international conference on world wide web. ACM, pp 591–600

  19. Patalano C (2008) Punk marketing: get off your ass and join the revolution. JAME 13(1):87

  20. Leavitt A, Burchard E, Fisher D, Gilbert S (2009) The influentials: new approaches for analyzing influence on twitter. Web Ecol Proj 4(2):1–18

    Google Scholar 

  21. Li Y-M, Lee Y-L, Lien N-J (2012) Online social advertising via influential endorsers. Int J Electron Commer 16(3):119–154

    Article  Google Scholar 

  22. Liu Z, Liu L, Li H (2012) Determinants of information retweeting in microblogging. Internet Res 22(4):443–466

    Article  Google Scholar 

  23. Macskassy SA, Michelson M (2011) Why do people retweet? Anti-homophily wins the day! In: ICWSM. Citeseer

  24. Petrovic S, Osborne M, Lavrenko V (2011) Rt to win! predicting message propagation in twitter. In: ICWSM

  25. Petty RE, Cacioppo JT (1986) The elaboration likelihood model of persuasion. Springer, New York

    Google Scholar 

  26. Petty RE, Cacioppo JT (1996) Attitudes and persuasion: classic and contemporary approaches. Westview Press, Boulder

    Google Scholar 

  27. Pezzoni F, An J, Passarella A, Crowcroft J, Conti M (2013) Why do i retweet it? An information propagation model for microblogs. In: Jatowt A, Lim E-P, Ding Y, Miura A, Tetzuka T, Dias G, Tanaka K, Flanagin A, Dai BT (eds) 5th international conference on social informatics (SocInfo 2013), vol 13, Kyoto, Japan, 25–27 November 2013. Springer, pp 360–369

  28. Romero DM, Meeder B, Kleinberg J (2011) Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, pp 695–704

  29. Sher PJ, Lee S-H (2009) Consumer skepticism and online reviews: an elaboration likelihood model perspective. Soc Behav Pers Int J 37(1):137–143

    Article  Google Scholar 

  30. Stieglitz S, Dang-Xuan L (2013) Emotions and information diffusion in social mediasentiment of microblogs and sharing behavior. J Manag Inf Syst 29(4):217–248

    Article  Google Scholar 

  31. Suarez FF (2005) Network effects revisited: the role of strong ties in technology selection. Acad Manag J 48(4):710–720

    Article  Google Scholar 

  32. Suh B, Hong L, Pirolli P, Chi EH (2010) Want to be retweeted? Large scale analytics on factors impacting retweet in twitter network. In: 2010 IEEE second international conference on social computing (socialcom). IEEE, pp 177–184

  33. Sun E, Rosenn I, Marlow C, Lento TM (2009) Gesundheit! modeling contagion through facebook news feed. In: ICWSM

  34. Tang X, Miao Q, Quan Y, Tang J, Deng K (2015) Predicting individual retweet behavior by user similarity: a multi-task learning approach. Knowl Based Syst 89:681–688

    Article  Google Scholar 

  35. Watts DJ, Dodds PS (2007) Influentials, networks, and public opinion formation. J Consum Res 34(4):441–458

    Article  Google Scholar 

  36. Weng J, Lim E-P, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of the third ACM international conference on web search and data mining. ACM, pp 261–270

  37. Wu S, Hofman JM, Mason WA, Watts DJ (2011) Who says what to whom on twitter. In: Proceedings of the 20th international conference on world wide web. ACM, pp 705–714

  38. Xu Z, Yang Q (2012) Analyzing user retweet behavior on twitter. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012). IEEE Computer Society, pp 46–50

  39. Zafarani R, Abbasi MA, Liu H (2014) Social media mining: an introduction. Cambridge University Press, Cambridge

    Book  Google Scholar 

  40. Zhang J, Tang J, Li J, Liu Y, Xing C (2014) Who influenced you? Predicting retweet via social influence locality. ACM Trans Knowl Discov Data TKDD 9(3):25

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, Xi’an Jiaotong University, Xi’an, China

    Juan Shi & Ping Hu

  2. College of Business, City University of Hong Kong, Hong Kong SAR, China

    Juan Shi & Kin Keung Lai

  3. International Business School, Shaanxi Normal University, Xi’an, China

    Kin Keung Lai

  4. The 705th Research Institute, China Shipbuilding Industry Corporation, Xi’an, 710077, China

    Gang Chen

Authors
  1. Juan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kin Keung Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ping Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Gang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juan Shi.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shi, J., Lai, K.K., Hu, P. et al. Factors dominating individual information disseminating behavior on social networking sites. Inf Technol Manag 19, 121–139 (2018). https://doi.org/10.1007/s10799-017-0278-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10799-017-0278-8

Keywords

Search

Navigation