Detecting Influencers in Social Media using information from their followers

  1. Javier Rodríguez-Vidal
  2. Laura Plaza
  3. Julio Gonzalo
Journal:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Year of publication: 2020

Issue: 64

Pages: 21-28

Type: Article

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Abstract

Given the task of finding influencers of a given domain (i.e. banking) in a social network, in this paper we investigate (i) the importance of characterizing followers for the automatic detection of influencers; (ii) the most effective way to combine signals obtained from followers and from the main profiles for the automatic detection of influencers. In this work, we have modeled the discourse used in two domains, banking and automotive, as well as the language used by the influencers in such domains and by their followers, and used these Language Models to estimate the probability of being influencer. Our most remarkable finding is that influencers not only depend on their expertise on the domain but also on that of their followers, so that the more knowledge and number of experts among their followers, the more probability of being influencer a profile has.

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Funding information

This research was supported by the Span ish Ministry of Science and Innovation (Ve-modalen Project, TIN2015-71785-R).

Funders

    • TIN2015-71785-R

Bibliographic References

  • AleAhmad, A., P. Karisani, M. Rahgozar, y F. Oroumchian. 2014. University of tehran at replab 2014. En Conference and Labs of the Evaluation Forum (Working Notes), páginas 1528–1536.
  • Amigó, E., J. Carrillo-de Albornoz, I. Chugur, A. Corujo, J. Gonzalo, E. Meij, M. de Rijke, y D. Spina. 2014. Overview of replab 2014: author profiling and reputation dimensions for online reputation management. En International Conference of the Cross-Language Evaluation Forum for European Languages, páginas 307–322. Springer.
  • Aral, S. y D. Walker. 2012. Identifying influential and susceptible members of social networks. Science, 337(6092):337–341.
  • CLEF. 2014. Replab 2014 campaign @online. http://nlp.uned.es/replab2014/.
  • Cossu, J.-V., K. Janod, E. Ferreira, J. Gaillard, y M. El-Bèze. 2014. Lia@ replab 2014: 10 methods for 3 tasks. En 4th International Conference of the Conference and Labs of the Evaluation Forum initiative. Citeseer.
  • Cossu, J.-V., V. Labatut, y N. Dugué. 2016. A review of features for the discrimination of twitter users: application to the prediction of offline influence. Social Network Analysis and Mining, 6(1):1–23.
  • Dang, V. 2012. The lemur projectwiki-ranklib. Lemur Project,[Online]. Available: http://sourceforge.net/p/lemur/wiki/RankLib
  • Friedman, J. H. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics, páginas 1189– 1232.
  • Jefferson-Henrique. 2016. Getoldtweets-java @online. https://github.com/JeffersonHenrique/GetOldTweets-java,
  • Abril. Liu, T.-Y. 2009. Learning to rank for information retrieval. Found. Trends Inf. Retr., 3(3):225–331, Marzo.
  • Lomena, J. J. M. y F. L. Ostenero. 2014. Uned at clef replab 2014: Author profiling task. En Conference and Labs of the Evaluation Forum (Working Notes), páginas 1537–1546.
  • Madden, M. y A. Smith. 2010. Reputation management and social media. pewresearch.org,[Online]. Available: pewresearch .org/ internet/ 2010/ 05/ 26/ reputation-management-and - social-media/ .
  • Manning, C. D., P. Raghavan, y H. Schütze. 2008. Introduction to Information Retrieval. Cambridge University Press, New York, NY, USA.
  • Nebot, V., F. Rangel, R. Berlanga, y P. Rosso. 2018. Identifying and classifying influencers in twitter only with textual information. En International Conference on Applications of Natural Language to Information Systems, páginas 28–39. Springer.
  • Rodríguez-Vidal, J., J. Gonzalo, L. Plaza, y H. A. Sánchez. 2019. Automatic detection of influencers in social networks: Authority versus domain signals. Journal of the Association for Information Science and Technology.
  • Saari, D. G. 1999. Explaining all threealternative voting outcomes. Journal of Economic Theory, 87:313–355.
  • Vilares, D., M. Hermo, M. A. Alonso, C. Gómez-Rodríguez, y J. Vilares. 2014. Lys at clef replab 2014: Creating the state of the art in author influence ranking and reputation classification on twitter. En Conference and Labs of the Evaluation Forum (Working Notes), páginas 1468–1478.
  • Villatoro-Tello, E., G. Ramírez-de-la Rosa, C. Sánchez-Sánchez, H. Jiménez-Salazar, W. A. Luna-Ramírez, y C. Rodríguez Lucatero. 2014.
  • Uamclyr at replab 2014: Author profiling task. En Conference and Labs of the Evaluation Forum (Working Notes), páginas 1547–1558.
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