This work discusses the mechanisms of popularity generation on the Internet. What we propose here is a model that replicates the statistical distribution profile of popularity. It is a probabilistic model of the number of individuals who read, hear or see, and then replicate a message, and parameterizes an individual’s preference for either new or older messages. Messages can gain in popularity according to a process of paying attention and the resulting popularity distribution has a stretched lognormal configuration. The stretch depends on the degree of attention paid to new messages versus that paid to older messages. We considered three sets of data to test the fit of the model: the American singers/songwriters listed on Wikipedia, videos on YouTube belonging to two different categories, and the number of visits to Wikipedia pages on music albums and film categories. Our main results adjust, with good approximation, to this experimental data. In each of the three case studies, the fit produced by the model is better adjusted to the data than the lognormal standard function.
BorgholY.MitraS.ArdonS.CarlssonN.EagerD.MahantiA. (2011). Characterizing and modelling popularity of user-generated videos. Performance Evaluation, 68, 1037–1055.
4.
BuzzellR. D. (1981). Are there” natural” market structures?The Journal of Marketing, 45, 42–51.
5.
ChaM.KwakH.RodriguezP.AhnY.-Y.MoonS. (2007). I tube, you tube, everybody tubes: Analyzing the world’s largest user generated content video system. Proceedings of the IMC ‘07–7th ACM SIGCOMM Conference on Internet Measurement (pp. 1–14). ACM: New York, USA.
6.
ChaM.KwakH.RodriguezP.AhnY.-Y.MoonS. (2009). Analyzing the video popularity characteristics of large-scale user generated content systems. IEEE/ACM Transactions on Networking (TON), 17, 1357–1370.
7.
ChengX.DaleC.LiuJ. (2007). Understanding the characteristics of internet short video sharing: YouTube as a case study. arXiv0707.3670.
8.
ChengX.DaleC.LiuJ. (2008). Statistics and social network of YouTube videos. Proceedings of the IWQoS 2008 16th International Workshop on Quality of Service (pp. 229–238). Enschede, The Netherlands: IEEE.
9.
CiampagliaG. L.FlamminiA.MenczerF. (2015). The production of information in the attention economy. Scientific Reports, 5, 9452.
10.
ClausetA.ShaliziC. R.NewmanM. E. (2009). Power-law distributions in empirical data. SIAM Review, 51, 661–703.
11.
DeschatresF.SornetteD. (2005). Dynamics of book sales: Endogenous versus exogenous shocks in complex networks. Physical Review E, 72, 016112.
12.
FamaeyJ.IterbekeF.WautersT.De TurckF. (2013). Towards a predictive cache replacement strategy for multimedia content. Journal of Network and Computer Applications, 36, 219–227.
13.
FuD.PammolliF.BuldyrevS. V.RiccaboniM.MatiaK.YamasakiK.StanleyH. E. (2005). The growth of business firms: Theoretical framework and empirical evidence. Proceedings of the National Academy of Sciences of the United States of America, 102, 18801–18806.
14.
GibratR. (1931). Les inégalités économiques. Paris, France: Recueil Sirey.
GleesonJ. P.CellaiD.OnnelaJ.PorterM. A.Reed-TsochasF. (2014). A simple generative model of collective online behavior. Proceedings of the National Academy of Sciences, 111, 10411–10415.
17.
GoodhardtG. J.EhrenbergA. S.ChatfieldC. (1984). The Dirichlet: A comprehensive model of buying behaviour. Journal of the Royal Statistical Society. Series A (General), 147, 621–655.
18.
GrowiecJ.PammolliF.RiccaboniM.StanleyH. E. (2008). On the size distribution of business firms. Economics Letters, 98, 207–212.
19.
GuptaM.GaoJ.ZhaiC.HanJ. (2012). Predicting future popularity trend of events in microblogging platforms. Proceedings of the American Society for Information Science and Technology, 49, 1–10.
20.
HillB. M. (1974). The rank-frequency form of Zipf’s law. Journal of the American Statistical Association, 69, 1017–1026.
21.
HongL.DanO.DavisonB. D. (2011). Predicting popular messages in twitter. Proceedings of the 20th International Conference Companion on World Wide Web Conference (pp. 57–58). Hyderabad: ACM.
22.
KohliR.SahR. (2004). Market shares: Some power law results and observations. Chicago, IL: Irving B. Harris Graduate School of Public Policy Studies, University of Chicago.
23.
KupavskiiA.UmnovA.GusevG.SerdyukovP. (2013). Predicting the audience size of a tweet. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (pp. 693–696). Cambridge, Massachusetts: AAAI Publication.
24.
LermanK.GhoshR. (2010). Information contagion: An empirical study of the spread of news on Digg and Twitter social networks. International Conference on Web and Social Media, 10, 90–97.
25.
LinY.-R.KeeganB.MargolinD.LazerD. (2014). Rising tides or rising stars? Dynamics of shared attention on twitter during media events. PLoS One, 9, e94093.
26.
LinY.-R.MargolinD.KeeganB.BaronchelliA.LazerD. (2013). #Bigbirds never die: Understanding social dynamics of emergent hashtag. arXiv1303.7144.
27.
LiuY. (2006). Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing, 70, 74–89.
28.
MaH.QianW.XiaF.HeX.XuJ.ZhouA. (2013). Towards modeling popularity of microblogs. Frontiers of Computer Science, 7, 171–184.
29.
MatsubaraY.SakuraiY.PrakashB. A.LiL.FaloutsosC. (2012). Rise and fall patterns of information diffusion: Model and implications. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 6–14). Beijing: ACM.
30.
MestyánM.YasseriT.KertészJ. (2013). Early prediction of movie box office success based on Wikipedia activity big data. PLoS One, 8, e71226.
31.
MishneG.GlanceN. (2006). Leave a reply: An analysis of weblog comments. Third annual workshop on the Weblogging ecosystem, Edinburgh, Scotland.
ReedW. J.HughesB. D. (2004). A model explaining the size distribution of gene and protein families. Mathematical Biosciences, 189, 97–102.
34.
ShammaD.KennedyL.ChurchillE. (2010. Tweetgeist: Can the twitter timeline reveal the structure of broadcast events. CSCW 2010 ACM Conference on Computer Supported Cooperative Work. Savannah, Georgia: ACM.
35.
SlimaneS. B. (2001). Bounds on the distribution of a sum of independent lognormal random variables. IEEE Transactions on Communications, 49, 975–978.
36.
SuttonJ. (1997). Gibrat’s legacy. Journal of Economic Literature, 35, 40–59.
37.
SzaboG.HubermanB. A. (2010). Predicting the popularity of online content. Communications of the ACM, 53, 80–88.
38.
TatarA.AntoniadisP.De AmorimM. D.FdidaS. (2012). Ranking news articles based on popularity prediction. Proceedings of the 2012 ASONAM International Conference on Advances in Social Networks Analysis and Mining (pp. 106–110). Istanbul: IEEE.
39.
TatarA.de AmorimM. D.FdidaS.AntoniadisP. (2014). A survey on predicting the popularity of web content. Journal of Internet Services and Applications, 5, 1–20.
40.
TsagkiasM.WeerkampW.De RijkeM. (2010). News comments: Exploring, modeling, and online prediction. Proceedings of the 32nd European Conference on IR Research (pp. 191–203). Milton Keynes: Springer-Verlag Berlin.
Van MieghemP.BlennN.DoerrC. (2011). Lognormal distribution in the Digg online social network. The European Physical Journal B-Condensed Matter and Complex Systems, 83, 251–261.
43.
WangC.YeM.HubermanB. A. (2012). From user comments to on-line conversations. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 244–252). Beijing: ACM.
44.
WengL.FlamminiA.VespignaniA.MenczerF. (2012). Competition among memes in a world with limited attention. Scientific Reports, 2, 335.
45.
WuF.HubermanB. A. (2007). Novelty and collective attention. Proceedings of the National Academy of Sciences, 104, 17599–17601.
46.
YamasakiK.MatiaK.BuldyrevS. V.FuD.PammolliF.RiccaboniM.StanleyH. E. (2006). Preferential attachment and growth dynamics in complex systems. Physical Review E, 74, 035103.
47.
YasseriT.BrightJ. (2014). Can electoral popularity be predicted using socially generated big data?IT-Information Technology, 56, 246–253.
48.
YasseriT.HaleS. A.MargettsH. (2013). Modeling the rise in internet-based petitions. arXiv1308.0239.
