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

Abstract

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.

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Notes

  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” (http://expandurl.appspot.com/expand?url=http).

  7. http://www.argylesocial.com/.

  8. https://www.dlvr.it/.

  9. https://www.bufferapp.com/.

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Acknowledgments

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.

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Correspondence to Reza Farahbakhsh.

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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). https://doi.org/10.1007/s13278-016-0336-y

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  • DOI: https://doi.org/10.1007/s13278-016-0336-y

Keywords

  • Strong Preference
  • Online Social Network
  • Cosine Similarity
  • Regular User
  • Initial Source