An ecological approach to structural flexibility in online communication systems.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
29 03 2021
29 03 2021
Historique:
received:
11
05
2020
accepted:
24
02
2021
entrez:
30
3
2021
pubmed:
31
3
2021
medline:
31
3
2021
Statut:
epublish
Résumé
Human cognitive abilities are limited resources. Today, in the age of cheap information-cheap to produce, to manipulate, to disseminate-this cognitive bottleneck translates into hypercompetition for rewarding outcomes among actors. These incentives push actors to mutualistically interact with specific memes, seeking the virality of their messages. In turn, memes' chances to persist and spread are subject to changes in the communication environment. In spite of all this complexity, here we show that the underlying architecture of empirical actor-meme information ecosystems evolves into recurring emergent patterns. We then propose an ecology-inspired modelling framework, bringing to light the precise mechanisms causing the observed flexible structural reorganisation. The model predicts-and the data confirm-that users' struggle for visibility induces a re-equilibration of the network's mesoscale towards self-similar nested arrangements. Our final microscale insights suggest that flexibility at the structural level is not mirrored at the dynamical one.
Identifiants
pubmed: 33782408
doi: 10.1038/s41467-021-22184-2
pii: 10.1038/s41467-021-22184-2
pmc: PMC8007599
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1941Références
Lorenz-Spreen, P., Mønsted, B. M., Hövel, P. & Lehmann, S. Accelerating dynamics of collective attention. Nat. Commun. 10, 1759 (2019).
pubmed: 30988286
pmcid: 6465266
doi: 10.1038/s41467-019-09311-w
Simon, H. A. Theories of bounded rationality. Decision Organizat. 1, 161–176 (1972).
Kahneman, D. Attention and Effort. (Prentice-Hall, 1973).
Bruns, A. & Highfield, T. Is Habermas on Twitter?: social media and the public sphere. In The Routledge Companion To Social Media And Politics. (eds Bruns, A., Enli, G., Skogerbø, E., Larsson, A. O. & Christensen, C.) 56–73 (Routledge, 2015).
Fareri, D. S. & Delgado, M. R. Social rewards and social networks in the human brain. Neuroscientist 20, 387–402 (2014).
pubmed: 24561513
doi: 10.1177/1073858414521869
Malik, A., Dhir, A. & Nieminen, M. Uses and gratifications of digital photo sharing on Facebook. Telemat. Informat. 33, 129–138 (2016).
doi: 10.1016/j.tele.2015.06.009
Sherman, L. E., Hernandez, L. M., Greenfield, P. M. & Dapretto, M. What the brain ‘likes’: neural correlates of providing feedback on social media. Soc. Cogn. Affect. Neurosci. 13, 699–707 (2018).
pubmed: 29982823
pmcid: 6121147
doi: 10.1093/scan/nsy051
Gonçalves, B., Perra, N. & Vespignani, A. Modeling users’ activity on Twitter networks: validation of Dunbar’s number. PLoS ONE 6, e22656 (2011).
pubmed: 21826200
pmcid: 3149601
doi: 10.1371/journal.pone.0022656
Anderson, S. P. & De Palma, A. Competition for attention in the information (overload) age. RAND J. Econom. 43, 1–25 (2012).
doi: 10.1111/j.1756-2171.2011.00155.x
Weng, L., Flammini, A., Vespignani, A. & Menczer, F. Competition among memes in a world with limited attention. Sci. Rep. 2, 335 (2012).
pubmed: 22461971
pmcid: 3315179
doi: 10.1038/srep00335
Gleeson, J. P., Ward, J. A., O’Sullivan, K. P. & Lee, W. T. Competition-induced criticality in a model of meme popularity. Phys. Rev. Lett. 112, 048701 (2014).
pubmed: 24580496
doi: 10.1103/PhysRevLett.112.048701
Gleeson, J. P., O’Sullivan, K. P., Baños, R. A. & Moreno, Y. Effects of network structure, competition and memory time on social spreading phenomena. Phys. Rev. X 6, 021019 (2016).
pmcid: 7219474
Iyer, G. & Katona, Z. Competing for attention in social communication markets. Manag. Sci. 62, 2304–2320 (2016).
doi: 10.1287/mnsc.2015.2209
McMahon, A. M. & April, M. Understanding Language Change. (Cambridge University Press, 1994).
Tamariz, M. Experimental studies on the cultural evolution of language. Ann. Rev. Linguist. 3, 389–407 (2017).
doi: 10.1146/annurev-linguistics-011516-033807
Zaslavsky, N., Kemp, C., Regier, T. & Tishby, N. Efficient compression in color naming and its evolution. Proc. Natl Acad. Sci. USA 115, 7937–7942 (2018).
pubmed: 30021851
doi: 10.1073/pnas.1800521115
pmcid: 6077716
Chadwick, A. The political information cycle in a hybrid news system: the british prime minister and the “bullygate” affair. Int. J. Press/Poli. 16, 3–29 (2011).
doi: 10.1177/1940161210384730
Holme, P. & Newman, M. E. Nonequilibrium phase transition in the coevolution of networks and opinions. Phys. Rev. E 74, 056108 (2006).
doi: 10.1103/PhysRevE.74.056108
Vazquez, F., Eguíluz, V. M. & San Miguel, M. Generic absorbing transition in coevolution dynamics. Phys. Rev. Lett. 100, 108702 (2008).
pubmed: 18352241
doi: 10.1103/PhysRevLett.100.108702
Sheykhali, S. et al. Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks. Sci. Rep. 10, 1–12 (2020).
Bruns, A. & Burgess, J. E. The use of Twitter hashtags in the formation of ad hoc publics. In Proceedings of the 6th European Consortium for Political Research General Conference. (The European Consortium for Political Research, ECPR, 2011).
Borge-Holthoefer, J., Baños, R. A., Gracia-Lázaro, C. & Moreno, Y. Emergence of consensus as a modular-to-nested transition in communication dynamics. Sci. Rep. 7, 41673 (2017).
pubmed: 28134358
pmcid: 5278396
doi: 10.1038/srep41673
Suweis, S., Simini, F., Banavar, J. R. & Maritan, A. Emergence of structural and dynamical properties of ecological mutualistic networks. Nature 500, 449 (2013).
pubmed: 23969462
doi: 10.1038/nature12438
Guimarães Jr, P. R., Pires, M. M., Jordano, P., Bascompte, J. & Thompson, J. N. Indirect effects drive coevolution in mutualistic networks. Nature 550, 511–514 (2017).
doi: 10.1038/nature24273
Cai, W., Snyder, J., Hastings, A. & D’Souza, R. M. Mutualistic networks emerging from adaptive niche-based interactions. Nat. Commun. 11, 1–10 (2020).
pubmed: 31911652
pmcid: 6946686
doi: 10.1038/s41467-020-19154-5
Pilosof, S., Porter, M. A., Pascual, M. & Kéfi, S. The multilayer nature of ecological networks. Nat. Ecol. Evol. 1, 0101 (2017).
doi: 10.1038/s41559-017-0101
CaraDonna, P. J. & Waser, N. M. Temporal flexibility in the structure of plant–pollinator interaction networks. Oikos 129, 1369–1380 (2020).
Newman, M. E. & Girvan, M. Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004).
doi: 10.1103/PhysRevE.69.026113
Fortunato, S. Community detection in graphs. Phys. Rep. 486, 75–174 (2010).
doi: 10.1016/j.physrep.2009.11.002
Stouffer, D. B. & Bascompte, J. Compartmentalization increases food-web persistence. Proc. Natl Acad. Sci. USA 108, 3648–3652 (2011).
pubmed: 21307311
doi: 10.1073/pnas.1014353108
pmcid: 3048152
Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl Acad. Sci. USA 100, 9383–9387 (2003).
pubmed: 12881488
doi: 10.1073/pnas.1633576100
pmcid: 170927
Bascompte, J. & Jordano, P. Plant-animal mutualistic networks: the architecture of biodiversity. Ann. Rev. Ecol. Evol. Syst. 38, 67–593 (2007).
Payrató-Borràs, C., Hernández, L. & Moreno, Y. Breaking the spell of nestedness: the entropic origin of nestedness in mutualistic systems. Phys. Rev. X 9, 031024 (2019).
Lewinsohn, T. M., Inácio Prado, P., Jordano, P., Bascompte, J. & Olesen, J. M. Structure in plant–animal interaction assemblages. Oikos 113, 174–184 (2006).
doi: 10.1111/j.0030-1299.2006.14583.x
Kondoh, M., Kato, S. & Sakato, Y. Food webs are built up with nested subwebs. Ecology 91, 3123–3130 (2010).
pubmed: 21141173
doi: 10.1890/09-2219.1
Flores, C. O., Meyer, J. R., Valverde, S., Farr, L. & Weitz, J. S. Statistical structure of host–phage interactions. Proc. Natl Acad. Sci. USA 108, E288–E297 (2011).
pubmed: 21709225
doi: 10.1073/pnas.1101595108
pmcid: 3136311
Flores, C. O., Valverde, S. & Weitz, J. S. Multi-scale structure and geographic drivers of cross-infection within marine bacteria and phages. ISME J. 7, 520–532 (2013).
pubmed: 23178671
doi: 10.1038/ismej.2012.135
Beckett, S. J. & Williams, H. T. P. Coevolutionary diversification creates nested-modular structure in phage–bacteria interaction networks. Interf. Focus 3, 20130033 (2013).
doi: 10.1098/rsfs.2013.0033
Solé-Ribalta, A., Tessone, C. J., Mariani, M. S. & Borge-Holthoefer, J. Revealing in-block nestedness: detection and benchmarking. Phys. Rev. E 96, 062302 (2018).
doi: 10.1103/PhysRevE.97.062302
Mello, M. A. et al. Insights into the assembly rules of a continent-wide multilayer network. Nat. Ecol. Evol. 3, 1–8 (2019).
González-Bailón, S., Wang, N., Rivero, A., Borge-Holthoefer, J. & Moreno, Y. Assessing the bias in samples of large online networks. Soc. Netw. 38, 16–27 (2014).
doi: 10.1016/j.socnet.2014.01.004
Lehmann, J., Gonçalves, B., Ramasco, J. J. & Cattuto, C. Dynamical classes of collective attention in Twitter. In Proceedings of the 21st International Conference on World Wide Web. 251–260 (ACM, 2012). https://doi.org/https://dl.acm.org/doi/proceedings/10.1145/2187836 .
Borge-Holthoefer, J. et al. The dynamics of information-driven coordination phenomena: a transfer entropy analysis. Sci. Adv. 2, e1501158 (2016).
pubmed: 27051875
pmcid: 4820379
doi: 10.1126/sciadv.1501158
Park, H. W., Park, S. & Chong, M. Conversations and medical news frames on Twitter: infodemiological study on Covid-19 in South Korea. J. Med. Internet Res. 22, e18897 (2020).
pubmed: 32325426
pmcid: 7202309
doi: 10.2196/18897
Patterson, B. D. & Atmar, W. Nested subsets and the structure of insular mammalian faunas and archipelagos. Biol. J. Linnean Soc. 28, 65–82 (1986).
doi: 10.1111/j.1095-8312.1986.tb01749.x
Atmar, W. & Patterson, B. D. The measure of order and disorder in the distribution of species in fragmented habitat. Oecologia 96, 373–382 (1993).
pubmed: 28313653
doi: 10.1007/BF00317508
Palazzi, M., Borge-Holthoefer, J., Tessone, C. & Solé-Ribalta, A. Macro-and mesoscale pattern interdependencies in complex networks. J. Royal Soc. Interf. 16, 20190553 (2019).
doi: 10.1098/rsif.2019.0553
Blüthgen, N., Fründ, J., Vázquez, D. P. & Menzel, F. What do interaction network metrics tell us about specialization and biological traits. Ecology 89, 3387–3399 (2008).
pubmed: 19137945
doi: 10.1890/07-2121.1
Staniczenko, P. P., Kopp, J. C. & Allesina, S. The ghost of nestedness in ecological networks. Nat. Commun. 4, 1391 (2013).
pubmed: 23340431
doi: 10.1038/ncomms2422
James, A., Pitchford, J. W. & Plank, M. J. Disentangling nestedness from models of ecological complexity. Nature 487, 227–230 (2012).
pubmed: 22722863
doi: 10.1038/nature11214
Zubiaga, A. A longitudinal assessment of the persistence of twitter datasets. J. Assoc. Inform. Sci. Technol. 69, 974–984 (2018).
doi: 10.1002/asi.24026
Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).
pubmed: 20705861
doi: 10.1126/science.1188321
Fortuna, M. A. et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J. Animal Ecol. 79, 811–817 (2010).
Bastolla, U. et al. The architecture of mutualistic networks minimizes competition and increases biodiversity. Nature 458, 1018–1020 (2009).
pubmed: 19396144
doi: 10.1038/nature07950
Williams, R. J. & Martinez, N. D. Simple rules yield complex food webs. Nature 404, 180 (2000).
pubmed: 10724169
doi: 10.1038/35004572
Palazzi, M. J., Cabot, J., Izquierdo, J. L. C., Solé-Ribalta, A. & Borge-Holthoefer, J. Online division of labour: emergent structures in open source software. Sci. Rep. 9, 1–11 (2019).
doi: 10.1038/s41598-019-50463-y
Plata, C. A. et al. Neutral theory for competing attention in social networks. Phys. Rev. Res. 3, 013070 (2021).
doi: 10.1103/PhysRevResearch.3.013070
Azaele, S. et al. Statistical mechanics of ecological systems: neutral theory and beyond. Rev. Modern Phys. 88, 035003 (2016).
doi: 10.1103/RevModPhys.88.035003
Hui, C. & Richardson, D. M. Invasion Dynamics. (Oxford University Press, 2017).
Rahwan, I. et al. Machine behaviour. Nature 568, 477–486 (2019).
pubmed: 31019318
doi: 10.1038/s41586-019-1138-y
Olesen, J. M., Bascompte, J., Dupont, Y. L. & Jordano, P. The modularity of pollination networks. Proc. Natl Acad. Sci. 104, 19891–19896 (2007).
pubmed: 18056808
doi: 10.1073/pnas.0706375104
pmcid: 2148393
Almeida-Neto, M., Guimaraes, P., Guimarães, P. R., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).
doi: 10.1111/j.0030-1299.2008.16644.x
Zachary, W. W. An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977).
doi: 10.1086/jar.33.4.3629752
Guimerà, R. & Amaral, L. A. N. Functional cartography of complex metabolic networks. Nature 433, 895–900 (2005).
pubmed: 15729348
pmcid: 2175124
doi: 10.1038/nature03288
Adamic, L. A. & Glance, N. The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery. 36–43 (ACM, 2005). https://dl.acm.org/doi/proceedings/10.1145/1134271 .
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V. & Parisi, D. Defining and identifying communities in networks. Proc. Natl Acad. Sci. USA 101, 2658–2663 (2004).
pubmed: 14981240
doi: 10.1073/pnas.0400054101
pmcid: 365677
Duch, J. & Arenas, A. Community detection in complex networks using extremal optimization. Phys. Rev. E 72, 027104 (2005).
doi: 10.1103/PhysRevE.72.027104
Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Statis. Mechan. 2008, 10008 (2008).
doi: 10.1088/1742-5468/2008/10/P10008
Fortunato, S. & Hric, D. Community detection in networks: a user guide. Phys. Rep. 659, 1–44 (2016).
doi: 10.1016/j.physrep.2016.09.002
Barber, M. J. Modularity and community detection in bipartite networks. Phys. Rev. E 76, 066102 (2007).
doi: 10.1103/PhysRevE.76.066102