Skip to main content

Measuring the effects of repeated and diversified influence mechanism for information adoption on Twitter

Social Network Analysis and Mining volume 12, Article number: 16 (2022) Cite this article

Abstract

People can adopt information disseminated in online social networks whenever they receive it frequently from friends or others. Studying this social influence dynamic is crucial to understanding social interactions and users’ behavior regarding online information spread. Quantifying social influence is challenging in online social systems where the interactions and communication content can be closely followed. Here, we study the effects of repeated and diversified influence mechanisms exploring the concepts of Information susceptibility and Adoption thresholds of Twitter users. We consider hashtag and retweet adoptions on different aggregation levels: items, users, and topic groups and study the adoption characterized by diversified and repeated influence stimuli. We address this challenge by tracking the timeline order of potential influence and adopting hashtags and retweets in a specific dataset collected from Twitter, which contains the posts’ dynamics of thousands of seed users and their entire followee networks. We show that users adopt retweets easier than hashtags, and we find both metrics to be heterogeneously distributed, correlated, and dependent on the topics and aggregation level of social influence. We find that new influencing neighbors can effectively trigger adoptions, particularly for topics where a new adopter friend triggers ~ 50% of adoptions. Our results may inform better models of adoption processes leading to a deeper empirical understanding of simple and complex contagion in online social networks.

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

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Notes

  1. The maximum number of collected tweets for a user is 3200, which covers their timeline to the past for a varying length depending on their tweeting activity. However, even the most active users may post less than this number over a year, thus setting a cap on this period assures the collection of all tweets for most user over 13 months.

References

  • Adamic L et al (2015) The diffusion of support in an online social movement: evidence from the adoption of equal-sign profile pictures, pp 1741–1750

  • Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74(1):47

    MathSciNet  Article  Google Scholar 

  • Aral S, Walker D (2012) Identifying influential and susceptible members of social networks. Science 337(6092): 337–341

    MathSciNet  Article  Google Scholar 

  • Bakshy E, Hofman JM, Mason WA, Watts DJ (2011a) Everyone's an influencer: quantifying influence on Twitter, pp 65–74

  • Bakshy E, Hofman JM, Mason WA, Watts DJ (2011b) Identifying influencers on Twitter

  • Baños RA, Borge-Holthoefer J, Moreno Y (2013) The role of hidden influentials in the diffusion of online information cascades. EPJ Data Sci 2(1):6

    Article  Google Scholar 

  • Barash V (2011) The dynamics of social contagion. Ph.D. thesis, Cornell University, Ithaca, NY, USA, aAI3485091

  • Bass FM (1969) A new product growth for model consumer durables. Manage Sci 15(5):215–227

    Article  Google Scholar 

  • Bearden WO, Netemeyer RG, Teel JE (1989) Measurement of consumer susceptibility to interpersonal influence. J Consum Res 15(4):473–481

    Article  Google Scholar 

  • Bruning PF, Alge BJ, Lin HC (2018) The embedding forces of network commitment: an examination of the psychological processes linking advice centrality and susceptibility to social influence. Organ Behav Hum Decis Process 148:54–69

    Article  Google Scholar 

  • Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? pp 925–936

  • Comito C, Forestiero A, Pizzuti C (2019) Word embedding based clustering to detect topics in social media, pp 192–199

  • Comito C (2020) Next: a framework for next-place prediction on location based social networks. Knowl-Based Syst 204(106):205

    Google Scholar 

  • Comito C (2021) How covid-19 information spread in us the role of Twitter as early indicator of epidemics. IEEE Trans Serv Comput https://doi.org/10.1109/TSC.2021.3091281

    Article  Google Scholar 

  • Cox S, Horadam K, Rao A (2016) The spread of ideas in a weighted threshold network, pp 437–447

  • Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Mach Intell 1(2):224–227

    Article  Google Scholar 

  • de Oliveira JF, Marques-Neto HT, Karsai M (2020) Information adoption via repeated or diversified social influence on Twitter

  • de Vries DA, Kühne R (2015) Facebook and self-perception: Individual susceptibility to negative social comparison on Facebook. Personal Individ Differ 86:217–221

    Article  Google Scholar 

  • Dow PA, Adamic LA, Friggeri A (2013) The anatomy of large Facebook cascades. ICWSM 1(2):12.

    Google Scholar 

  • Gleeson JP, Cahalane DJ (2007) Seed size strongly affects cascades on random networks. Phys Rev E 75(5):056103

    Article  Google Scholar 

  • Granovetter M (1978) Threshold models of collective behavior. Am J Sociol 83(6):1420–1443.

    Article  Google Scholar 

  • Granovetter M, Soong R (1983) Threshold models of diffusion and collective behavior. J Math Sociol 9(3):165–179

    Article  Google Scholar 

  • Hoang TA, Lim EP (2012) Virality and susceptibility in information diffusions

  • Hoang TA, Lim EP (2013) Retweeting: an act of viral users, susceptible users, or viral topics? pp 569–577

  • Hoang TA, Lim EP (2016) Tracking virality and susceptibility in social media, pp 1059–1068

  • Hurd TR, Gleeson JP (2013) On Watts' cascade model with random link weights. J Complex Netw 1(1):25–43

    Article  Google Scholar 

  • Karampourniotis PD, Sreenivasan S, Szymanski BK, Korniss G (2015) The impact of heterogeneous thresholds on social contagion with multiple initiators. PLoS ONE 10(11):56

    Article  Google Scholar 

  • Karimi F, Holme P (2013) Threshold model of cascades in empirical temporal networks. Physica A: Stat Mech Appl 392(16):3476–3483

    Article  Google Scholar 

  • Karsai M, Iñiguez G, Kikas R, Kaski K, Kertész J (2016) Local cascades induced global contagion: how heterogeneous thresholds, exogenous effects, and unconcerned behaviour govern online adoption spreading. Sci Rep 6(27):178

    Google Scholar 

  • Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, Makse HA (2010) Identification of influential spreaders in complex networks. Nat Phys 6(11):888.

    Article  Google Scholar 

  • Lee RKW, Lim EP (2015) Measuring user influence, susceptibility and cynicalness in sentiment diffusion, pp 411–422

  • Mensah H, Xiao L, Soundarajan S (2019) Characterizing susceptible users on Reddit's change my view

  • Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space.arXiv:13013781

  • Minatel D, Ferreira V, de Andrade LA (2021) Local-entity resolution for building location-based social networks by using stay points. Theor Comput Sci 851:62–76

    MathSciNet  Article  Google Scholar 

  • Rogers EM (2010) Diffusion of innovations. Simon and Schuster, New York

    Google Scholar 

  • Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65

    Article  Google Scholar 

  • Ruan Z, Iniguez G, Karsai M, Kertész J (2015) Kinetics of social contagion. Phys Rev Lett 115(21):218702

    Article  Google Scholar 

  • Schelling TC (1969) Models of segregation. Am Econ Rev 59(2):488–493

    Google Scholar 

  • Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    MathSciNet  Article  Google Scholar 

  • Twitter I (2020) Twitter developer. https://developer.twitter.com

  • Tussyadiah SP, Kausar DR, Soesilo PK (2018) The effect of engagement in online social network on susceptibility to influence. J Hosp Tour 42(2):201–223

    Article  Google Scholar 

  • Unicomb S, Iñiguez G, Karsai M (2018) Threshold driven contagion on weighted networks. Sci Rep 8(1):3094

    Article  Google Scholar 

  • Unicomb S, Iñiguez G, Kertész J, Karsai M (2019) Reentrant phase transitions in threshold driven contagion on multiplex networks. Phys Rev E 100:040301. https://doi.org/10.1103/PhysRevE.100.040301

    Article  Google Scholar 

  • Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9(11):2579–2605

    MATH  Google Scholar 

  • Wald R, Khoshgoftaar TM, Napolitano A, Sumner C (2013) Predicting susceptibility to social bots on Twitter, pp 6–13

  • Wagner C, Mitter S, Körner C, Strohmaier M (2012) When social bots attack: modeling susceptibility of users in online social networks. In: # MSM, pp 41–48

  • Wang W, Tang M, Shu P, Wang Z (2016) Dynamics of social contagions with heterogeneous adoption thresholds: crossover phenomena in phase transition. New J Phys 18(1):013029

    Article  Google Scholar 

  • Wasserman S, Faust K (1994) Social network analysis: methods and applications, vol 8. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Watts DJ (2002) A simple model of global cascades on random networks. Proc Natl Acad Sci 99(9):5766–5771

    MathSciNet  Article  Google Scholar 

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

    Article  Google Scholar 

  • Weeks BE (2015) Emotions, partisanship, and misperceptions: how anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. J Commun 65(4):699–719

    Article  Google Scholar 

  • Yağan O, Gligor V (2012) Analysis of complex contagions in random multiplex networks. Phys Rev E 86(3):036103

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by CAPES, FAPEMIG (PPM-00253-18), and the STIC-AmSud Program (Project 18-STIC-07). MK was supported by the DataRedux (ANR-19-CE46-0008) and SoSweet (ANR-15-CE38-0011) ANR projects and the SoBigData++ (H2020-871042) project.

Author information

Authors and Affiliations

  1. Department of Computer Science, PUC Minas, Belo Horizonte, Brazil

    Jaqueline Faria de Oliveira & Humberto Torres Marques-Neto

  2. Department of Network and Data Science, Central European University, Vienna, Austria

    Márton Karsai

  3. ENS de Lyon, Inria, CNRS, UCB Lyon 1, LIP UMR 5668, IXXI, Univ Lyon, 69342, Lyon, France

    Jaqueline Faria de Oliveira & Márton Karsai

Authors
  1. Jaqueline Faria de Oliveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Humberto Torres Marques-Neto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Márton Karsai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jaqueline Faria de Oliveira.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Oliveira, J.F., Marques-Neto, H.T. & Karsai, M. Measuring the effects of repeated and diversified influence mechanism for information adoption on Twitter. Soc. Netw. Anal. Min. 12, 16 (2022). https://doi.org/10.1007/s13278-021-00844-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-021-00844-x

Keywords

  • Susceptibility
  • Threshold
  • Social contagion
  • Adoption