Skip to main content
Log in

Characterization of cross-posting activity for professional users across Facebook, Twitter and Google+

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript


Professional players in social media (e.g., big companies, politician, athletes, celebrities, etc) are intensively using Online Social Networks (OSNs) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation improvement, etc). Hence, due to the large catalog of existing OSNs, professional players usually count with OSN accounts in different systems. In this context, an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. This paper aims at characterizing the cross-posting activity of professional users across three major OSNs, Facebook, Twitter and Google+. To this end, we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs. Then we characterize the phenomenon of cross-posting and analyse the behavioural patterns based on the identified characteristics.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Similar content being viewed by others


  1. This is an extended version of a published paper at Asonam’15 conference Farahbakhsh et al. (2015).

  2. Through this paper, the term “professional users” stand for users in social media that behind their accounts there are entities with a clear business plan that are utilizing social media for their business interest.

  3. It must be noted that for this analysis we do not take into account where the post appears first, but only consider whether it is unique in an OSN or it appears in 2 or 3 of them.

  4. This is the nomenclature employed in FB. A like is associated to a +1 in G+ and to a favourite in TW. A share is associated to reshare in G+ and a retweet in TW.

  5. Usually a linear regression is represented as y = ax + b, but in the figure we just use y = ax, since we are interested in the slope, but not in the offset.

  6. TW usually employs short urls. Hence, to obtain the website behind the short urls, we had to reverse the process and obtain the original urls from the short urls using “Expand url portal” (





  • Backstrom L, Boldi P, Rosa M, Ugander J, Vigna S (2011) Four degrees of separation, CoRR, vol. abs/1111.4570, 2011

  • Cha M, Haddadi H, Benevenuto F, Gummadi K (2010) Measuring user influence in twitter: the million follower fallacy, in AAAI ICWSM, 2010

  • Chun B, Culler D, Roscoe T, Bavier A, Peterson L, Wawrzoniak M, Bowman M (2003) Planetlab: an overlay testbed for broad-coverage services. ACM SIGCOMM Comput Commun Rev 33:3–12

  • Crymble A (2010) An analysis of Twitter and Facebook use by the archival community. Archivaria 70:125–151

    Google Scholar 

  • Farahbakhsh R, Cuevas A, Crespi N (2015) Characterization of cross-posting activity for professional users across major osns. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining, ser. ASONAM, 2015

  • Gjoka M, Kurant M, Butts C, Markopoulou A (2010) Walking in facebook: a case study of unbiased sampling of osns, in IEEE INFOCOM, 2010

  • Gonzalez R, Cuevas R, Motamedi R, Rejaie R, Cuevas A (2013) Google+ or google? dissecting the evolution of the new osn in its first year, in WWW, 2013

  • Hughesa DJ, Rowe M, Batey M, Lee M (2011) A tale of two sites: Twitter vs. Facebook and the personality predictors of social media usage. Comput Human Behav 28:561–569

    Article  Google Scholar 

  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news Media? in WWW, 2010

  • Lim BH, Lu D, Chen T, Kan M (2015) #mytweet via instagram: Exploring user behaviour across multiple social networks. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining, ASONAM, 2015

  • MacQueen J (1967) Some methods for classification and analysis of multivariate observations, Berkeley symposium on mathematical statistics and probability

  • Magno G, Comarela G, Saez-Trumper D, Cha M, Almeida V (2012) New kid on the block: exploring the google+ social graph, in ACM IMC, 2012

  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks, in ACM IMC, 2007

  • Motamedi R, Gonzalez R, Farahbakhsh R, Cuevas A, Cuevas R, Rejaie R (2014) Characterizing group-level user behavior in major online social networks, Technical Report,

  • NLTK, NLTK madules for similarity (2014)

  • Ottoni R, Casas D, Pesce J, Meira W, Wilson C, Mislove A, Almeida V (2014) Of Pins and Tweets: investigating how users behave across image- and text-based social networks, in ICWSM, 2014

  • PageData, Pagedata Portal (2014)

  • Rejaie R, Torkjazi M, Valafar M, Willinger W (2010) Sizing up online social networks, IEEE Network, 2010

  • Schiöberg D, Schneider F, Schiöberg H, Schmid S, Uhlig S, Feldmann A (2012) Tracing the birth of an osn: social graph and profile analysis in google, in ACM WebSci, 2012

  • Singhal A (2001) Modern information retrieval: a brief overview. IEEE Data Eng Bull 24:35–43

    Google Scholar 

  • Socialbakers, Socialbakers Portal (2014)

  • Ugander J, Karrer B, Backstrom L, Marlow C (2011) The anatomy of the facebook social graph, CoRR, vol. abs/1111.4503, 2011

  • Yang QX, Sung SY, Chun L, Zhao L, Peng S (2003) Faster algorithm of string comparison. Pattern Anal Appl 70:125–151

    Google Scholar 

  • Zhong C, Salehi M, Shah S, Cobzarenco M, Sastry N, Cha M, (2014) Social bootstrapping: How pinterest and social communities benefit by borrowing links from facebook. In: Proceedings of the 23rd international conference on world wide web, ser. WWW ’14, 2014, pp. 305–314

Download references


This work is partially supported by the European Celtic-Plus project CONVINcE and ITEA3 CAP. as well as the Ministerio de Economia y Competitividad of SPAIN through the project BigDatAAM (FIS2013-47532-C3-3-P) and Horizon 2020 Programme (H2020-DS-2014-1) under Grant Agreement number 653449. We would like thank Reza Motamedi, Reza Rejaie, Roberto Gonzlez and Ruben Cuevas for providing Twitter and Google+ dataset to be used in this study.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Reza Farahbakhsh.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Farahbakhsh, R., Cuevas, Á. & Crespi, N. Characterization of cross-posting activity for professional users across Facebook, Twitter and Google+. Soc. Netw. Anal. Min. 6, 33 (2016).

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: