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Facebook wall posts: a model of user behaviors

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How do people interact with their Facebook wall? At a high level, this question captures the essence of our work. While most prior efforts focus on Twitter, the much fewer Facebook studies focus on the friendship graph or are limited by the amount of users or the duration of the study. In this work, we model Facebook user behavior: we analyze the wall activities of users focusing on identifying common patterns and surprising phenomena. We conduct an extensive study of roughly 7k users over 3 years during 4-month intervals each year. We propose PowerWall, a lesser known heavy-tailed distribution to fit our data. Our key results can be summarized in the following points. First, we find that many wall activities, including number of posts, number of likes, number of posts of type photo, can be described by the PowerWall distribution. What is more surprising is that most of these distributions have similar slope, with a value close to 1! Second, we show how our patterns and metrics can help us spot surprising behaviors and anomalies. For example, we find a user posting every two days, exactly the same count of posts; another user posting at midnight, with no other activity before or after. Our work provides a solid step toward a systematic and quantitative wall-centric profiling of Facebook user activity.

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  • Acquisti A, Gross R (2006) Imagined communities: awareness, information sharing, and privacy on the facebook. In: Proceedings of the 6th international conference on privacy enhancing technologies, Springer, Berlin, PET’06, pp 36–58

  • Aiello W, Chung F, Lu L (2000) A random graph model for massive graphs. In: Proceedings of the thirty-second annual ACM symposium on theory of computing, ACM, New York, NY, USA, STOC ’00, pp 171–180. doi:10.1145/335305.335326

  • Arnaboldi V, Passarella A, Tesconi M, Gazzè D (2011) Towards a characterization of egocentric networks in online social networks. In: Proceedings of the 2011th confederated international conference on the move to meaningful internet systems, Springer, Berlin, OTM’11, pp 524–533

  • Backstrom L, Huttenlocher D, Kleinberg J, Lan X (2006) Group formation in large social networks: membership, growth, and evolution. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 44–54

  • Barabasi AL (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039):207–211

    Article  Google Scholar 

  • Barabási AL, Albert R (1999) Emergence of scaling in random networks. Science 286:509–512

    Article  MathSciNet  MATH  Google Scholar 

  • Boldi P, Vigna S (2012) Four degrees of separation, really. In: Proceedings of the 2012 international conference on advances in social networks analysis and mining (ASONAM 2012), IEEE Computer Society, Washington, DC, USA, ASONAM ’12, pp 1222–1227

  • Champernowne DG (1952) The graduation of income distributions. Econometrica 20(4):591–615

    Article  MATH  Google Scholar 

  • Cheng J, Adamic L, Dow PA, Kleinberg JM, Leskovec J (2014) Can cascades be predicted? In: Proceedings of the 23rd international conference on world wide web, ACM, New York, NY, USA, WWW ’14, pp 925–936

  • Clauset A, Shalizi CR, Newman MEJ (2009) Power-law distributions in empirical data. SIAM Rev 51(4):661–703

    Article  MathSciNet  MATH  Google Scholar 

  • Clementi F, Gallegati M (2005) Pareto’s law of income distribution: evidence for Germany, the United Kingdom, and the United States. Microeconomics 0505006, EconWPA.

  • De Choudhury M, Counts S, Horvitz EJ, Hoff A (2014) Characterizing and predicting postpartum depression from shared Facebook data. In: Proceedings of the 17th ACM conference on computer supported cooperative work and social computing, ACM, New York, NY, USA, CSCW ’14, pp 626–638

  • De Melo POSV, Akoglu L, Faloutsos C, Loureiro AAF (2010) Surprising patterns for the call duration distribution of mobile phone users. In: ECML PKDD’10, pp 354–369

  • Dunbar RI (1992) Neocortex size as a constraint on group size in primates. J Hum Evol 22(6):469–493

    Article  Google Scholar 

  • Facebook Newsroom (2016) Facebook Company Information. Accessed 10 Oct 2016

  • Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. SIGCOMM pp 251–262

  • Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, New York, NY, USA, CHI ’09, pp 211–220

  • Gjoka M, Kurant M, Butts CT, Markopoulou A (2010) Walking in facebook: A case study of unbiased sampling of osns. In: Proceedings of the 29th conference on information communications, IEEE Press, Piscataway, NJ, USA, INFOCOM’10, pp 2498–2506

  • Gonccalves B, Perra N, Vespignani A (2011) Modeling users’ activity on twitter networks: validation of dunbar’s number. PloS ONE 6(8):e22656

    Article  Google Scholar 

  • Gosling SD, Gaddis S, Vazire S et al (2007) Personality impressions based on Facebook profiles. ICWSM 7:1–4

    Google Scholar 

  • Guo L, Tan E, Chen S, Zhang X, Zhao YE (2009) Analyzing patterns of user content generation in online social networks. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 369–378

  • Gupta S, Yan X, Lerman K (2015) Structural properties of ego networks. In: International conference on social computing, behavioral modeling and prediction

  • Huang TK, Rahman MS, Madhyastha HV, Faloutsos M, Ribeiro B (2013) An analysis of socware cascades in online social networks. In: Proceedings of the 22Nd international conference on world wide web, international world wide web conferences steering committee, Republic and Canton of Geneva, Switzerland, WWW ’13, pp 619–630

  • Karagiannis T, Le Boudec JY, Vojnovic M (2010) Power law and exponential decay of intercontact times between mobile devices. IEEE Trans Mob Comput 9(10):1377–1390

    Article  Google Scholar 

  • Kosinski M, Stillwell D, Graepel T (2013) Private traits and attributes are predictable from digital records of human behavior. Proc Nat Acad Sci 110(15):5802–5805

    Article  Google Scholar 

  • Koutra D, Koutras V, Prakash BA, Faloutsos C (2013) Patterns amongst competing task frequencies: super-linearities, and the almond-DG model. In: PAKDD, pp 201–212

  • McCool JI (2012) Using the Weibull distribution: reliability, modeling and inference., Wiley Series in Probability and StatisticsWiley, Hoboken

    Book  MATH  Google Scholar 

  • Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on internet measurement, ACM, New York, NY, USA, IMC ’07, pp 29–42

  • Mitzenmacher M (2004) A brief history of generative models for power law and lognormal distributions. Internet Math 1(2):226–251

    Article  MathSciNet  MATH  Google Scholar 

  • Rahman MS, Huang TK, Madhyastha HV, Faloutsos M (2012) Frappe: detecting malicious Facebook applications. In: Proceedings of the 8th international conference on emerging networking experiments and technologies, ACM, New York, NY, USA, CoNEXT ’12, pp 313–324

  • Redner S (1998) How popular is your paper? An empirical study of the citation distribution. Eur Phys J B 4(2):131–134

    Article  Google Scholar 

  • Seshadri M, Machiraju S, Sridharan A, Bolot J, Faloutsos C, Leskovec J (2008) Mobile call graphs: beyond power-law and lognormal distributions. In: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, Las Vegas, Nevada, USA, pp 596–604

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

  • Vaz de Melo POS, Faloutsos C, Assunção R, Loureiro A (2013) The self-feeding process: a unifying model for communication dynamics in the web. In: Proceedings of the 22Nd international conference on world wide web, international world wide web conferences steering committee, Republic and Canton of Geneva, Switzerland, WWW ’13, pp 1319–1330

  • Viswanath B, Mislove A, Cha M, Gummadi KP (2009) On the evolution of user interaction in Facebook. In: Proceedings of the 2Nd ACM workshop on online social networks, ACM, New York, NY, USA, WOSN ’09, pp 37–42

  • Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Ben-Zeev D, Campbell AT (2014) Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing, ACM, pp 3–14

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This material is based upon work supported by the National Science Foundation under Grants Nos. SaTC 1314935, IIS-1217559, CNS-1314632, IIS-1408924, by DARPA Grant Social Media in Strategic Communication (SMISC) program under Agreement Number W911NF-12-C-0028 and by the Army Research Laboratory under Cooperative Agreement Number W911NF-09-2-0053. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or other funding parties. The US Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.

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Correspondence to Pravallika Devineni.

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Devineni, P., Koutra, D., Faloutsos, M. et al. Facebook wall posts: a model of user behaviors. Soc. Netw. Anal. Min. 7, 6 (2017).

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