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The 10 million follower fallacy: audience size does not prove domain-influence on Twitter

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Abstract

With the advent of social networks and micro-blogging systems, the way of communicating with other people and spreading information has changed substantially. Persons with different backgrounds, age and education exchange information and opinions, spanning various domains and topics, and have now the possibility to directly interact with popular users and authoritative information sources usually unreachable before the advent of these environments. As a result, the mechanism of information propagation changed deeply, the study of which is indispensable for the sake of understanding the evolution of information networks. To cope up with this intention, in this paper, we propose a novel model which enables to delve into the spread of information over a social network along with the change in the user relationships with respect to the domain of discussion. For this, considering Twitter as a case study, we aim at analyzing the multiple paths the information follows over the network with the goal of understanding the dynamics of the information contagion with respect to the change of the topic of discussion. We then provide a method for estimating the influence among users by evaluating the nature of the relationship among them with respect to the topic of discussion they share. Using a vast sample of the Twitter network, we then present various experiments that illustrate our proposal and show the efficacy of the proposed approach in modeling this information spread.

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Notes

  1. http://www.twitter.com.

  2. http://www.tumblr.com.

  3. http://www.facebook.com.

  4. http://plus.google.com/.

  5. A divulgative article expressing the same high-level idea can be found at http://www.nytimes.com/external/readwriteweb/2010/03/19/19readwriteweb-the-million-follower-fallacy-audience-size-d-3203.html.

  6. www.facebook.com/.

  7. http://secondlife.com/.

  8. www.informatik.uni-trier.de/~ley/db/.

  9. Please notice that, as pointed out in literature, the inverse document frequency factor cannot be positively applied in our work. In fact, it diminishes the weight of terms that occur very frequently in the corpus, while increasing the weight of terms that occur rarely. In our case, we believe that this is not a suitable weighting scheme. Terms that appear in most of the documents in the corpus are likely to be highly relevant for the domain. Please also notice that, in order to exclude common function words (such as conjunctions and articles), we have removed stop-words with common techniques, and we have only considered nouns in our computation.

  10. An interesting observation is possible: the highest ranked \(n\)-grams are mostly uni-grams and simply reflect the distribution of the letters of the alphabet in the language of the document. In other words, the most frequent \(n\)-grams are most of the time correlated to the language. Thus, considering that the most frequent \(n\)-grams for the considered topic profiles resulted to be very similar due to this fact (while they start differing consistently in the lowest part of ranked \(n\)-grams list), we excluded from our analysis the uni-grams.

  11. The sampling rate of the used Twitter account is 10 % over an average of 200 millions per day. More information are available at http://apiwiki.Twitter.com.

  12. http://www.weibo.com.

  13. http://www.facebook.com.

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Authors and Affiliations

  1. LIASD - Université Paris 8, Saint-Denis, France

    Mario Cataldi

  2. Mas Laboratory - Ecole Centrale Paris, Châtenay-Malabry, France

    Marie-Aude Aufaure

Authors
  1. Mario Cataldi
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  2. Marie-Aude Aufaure
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Correspondence to Mario Cataldi.

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Cataldi, M., Aufaure, MA. The 10 million follower fallacy: audience size does not prove domain-influence on Twitter. Knowl Inf Syst 44, 559–580 (2015). https://doi.org/10.1007/s10115-014-0773-8

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