Skip to main content
  • 1146 Accesses

Abstract

Here, Networked identity indicates interconnected modality on social synchronization and in/exclusiveness among massive global citizens. Those social mechanisms beyond the physical distance should be revealed how and why occurs or not in each condition. Although the advent of social media gained harmonized opinion formation and contagions by through massive influences, the digitalized social world would be coined in sharing emotional experiences among global citizens. Such social dynamics has regenerated influentially fake news, social movements, and responsive support networking. An online virtual nation has been committed and identified among participatory citizens, and their motivations to be recognized as an independent nation could facilitate their progress by online- interconnected citizens.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 119.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://blog.twitter.com/2011/06/global-pulse.html (∗currently, original page cannot be accessed).

References

  • Alberto, B., Maurice, H., & Mikael, V. J. (2010). Networked control systems. London: Springer.

    MATH  Google Scholar 

  • Amblard, F., & Deffuant, G. (2004). The role of network topology on extremism propagation with the relative agreement opinion dynamics. Physica A, 343, 725–738.

    Article  Google Scholar 

  • Baggag, A., et al. (2018). Resilience analytics: Coverage and robustness in multi-modal transportation networks. EPJ Data Science, 7, 14. https://doi.org/10.1140/epjds/s13688-018-0139-7.

    Article  Google Scholar 

  • Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and opinion on Facebook. Science, 348(6239), 1130–1132. https://doi.org/10.1126/science.aaa1160.

    Article  MathSciNet  MATH  Google Scholar 

  • Barabási, A.-L. (2016). Network science. Cambridge, UK: Cambridge University Press.

    MATH  Google Scholar 

  • Barbera, M. V., et al. (2013) Signals from the crowd: Uncovering social relationships through smartphone probes. http://conferences.sigcomm.org/imc/2013/papers/imc148-barberaSP106.pdf

  • Bauer, M., et al. (2018). Social contagion of ethnic hostility. PNAS, 115(19), 4881–4886.

    Article  Google Scholar 

  • Blau, P. M. (1998). Exchange & power in social life. Piscataway, NJ: Transaction Publishers.

    Google Scholar 

  • Bobo, L. D. (2017). Racism in Trump’s America reflections on culture, sociology, and the 2016 US presidential election. The British Journal of Sociology, 68(S1), S85–S104. https://doi.org/10.1111/1468-4446.12324.

    Article  Google Scholar 

  • Boening, A. (2014). The Arab Spring: Re-balancing the greater Euro-Mediterranean? Cham: Springer.

    Google Scholar 

  • Botta, F., et al. (2015) Quantifying crowd size with mobile phone and Twitter data. Royal Society Open Science. http://rsos.royalsocietypublishing.org/content/2/5/150162

  • Bozzo, E., & Franceschet, M. (2016). A theory on power in networks. Communication of the ACM, 59(11), 75–83.

    Article  Google Scholar 

  • Broadbent, S. R., & Hammersley, J. M. (1957). Percolation processes. Mathematical Proceedings of the Cambridge Philosophical Society. https://doi.org/10.1017/S0305004100032680.

  • Brown, R. (2000). Group processes (2nd ed.). Oxford, UK: Blackwell.

    Google Scholar 

  • Candeago, L., Bertagnolli, G., Bosetti, P., Vescovi, M., Sacco, F., & Lepri, B. (2019). Cities of a feather flock together: A study on the synchronization of communication between Italian cities. EPJ Data Science, 8, 19. https://doi.org/10.1140/epjds/s13688-019-0198-4.

    Article  Google Scholar 

  • Casilli, A. A., & Tubaro, R. (2012). Social media censorship in times of political unrest - A social simulation experiment with the UK riots. Bulletin de Methodologie Sociologique, 115(1), 5–20. https://doi.org/10.1177/0759106312445697.

    Article  Google Scholar 

  • Castro, P., Chiu, P., Kremenek, T., & Muntz, R. (2001). A probabilistic room location service for wireless networked environments. Atlanta, GA: Ubiquitous Computing. http://godfather.cs.ucla.edu/publications/pdf/ubicomp01.zip.

    Book  MATH  Google Scholar 

  • Chan, M. (2014). Social identity gratifications of social network sites and their impact on collective action participation. Asian Journal of Social Psychology, 17(3), 229–235.

    Article  Google Scholar 

  • Choucri, N. (2012). Cyberpolitics in international relations. Cambridge, MA: The MIT Press.

    Book  Google Scholar 

  • Choudhary, A., et al. (2012). Social media evolution of the Egyptian revolution. Communications of the ACM, 55(5), 74–80.

    Article  Google Scholar 

  • Christensen, P. N., Rothgerber, H., Wood, W., & Matz, D. C. (2004). Social norms and identity relevance: A motivational approach to normative behavior. Personality and Social Psychology Bulletin, 30(10), 1295–1309.

    Article  Google Scholar 

  • Ciamapglia, G. L. (2018). Fighting fake news: A role for computational social science in the fight against digital misinformation. Journal of Computational Social Science, 1, 147–153.

    Article  Google Scholar 

  • Claire, T., & Fiske, S. T. (1998). A systemic view of behavioral confirmation. In C. Sedikides et al. (Eds.), Intergroup cognition and intergroup behavior. Boca Raton, FL: LEA.

    Google Scholar 

  • Coleman, J. S. (1974). Power and the structure of society. New York: Norton.

    Google Scholar 

  • Coleman, J. S. (1990). Foundations of social theory. Cambridge, MA: Berknap Press of University of Harvard Press.

    Google Scholar 

  • Cook, K. S., Emerson, R. M., Gillmore, M. R., & Yamagishi, T. (1983). The distribution of power in exchange networks: Theory and experimental results. American Journal of Sociology, 89, 275–305.

    Article  Google Scholar 

  • Cook, K. S., Hegtvedt, K. A., & Yamagishi, T. (1988). Structural inequality, legitimation and reactions to inequity in exchange networks. In M. Webster & M. Foschi (Eds.), Status generalization: New theory and research. Palo Alto, CA: Stanford University Press.

    Google Scholar 

  • Copeland, B. J., & Shagrir, O. (2019). The church-turing thesis: Logical limit or breachable barrier? Communications of the ACM, 62(1), 66–74.

    Article  Google Scholar 

  • Corner, J. (2017). Fake news, post-truth and media–political change. Media, Culture & Society, 39(7), 1100–1107.

    Article  Google Scholar 

  • Deffuant, G., Neau, D., Amblard, F., & Weisbuch, G. (2000). Mixing beliefs among interacting agents. Advances in Complex Systems, 3, 87–98.

    Article  Google Scholar 

  • Deutsch, M., & Coleman, P. T. (Eds.). (2000). The handbook of conflict resolution theory and practice. San Francisco, CA: Jossey-Bass Publisher.

    Google Scholar 

  • DiResta, R., et al. (2019). The tactics & tropes of the internet research agency. https://disinformationreport.blob.core.windows.net/disinformation-report/NewKnowledge-Disinformation-Report-Whitepaper.pdf

  • Doer, B., Fouz, M., & Friedrich, T. (2012). Why rumors spread so quickly in social networks. Communications of the ACM, 55(6), 70–75.

    Article  Google Scholar 

  • Dolata, U. (Ed.). (2018). Collectivity and power on the internet: A sociological perspective (Springer briefs in sociology). Cham: Springer.

    Google Scholar 

  • Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge, UK: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Ertugrul, A. M., Lin, Y.-R., Chung, M. T., Yan, M., & Li, A. (2019). Activism via attention: Interpretable spatiotemporal learning to forecast protest activities. EPJ Data Science, 8, 5. https://doi.org/10.1140/epjds/s13688-019-0183-y.

    Article  Google Scholar 

  • Ginsberg, J., et al. (2009). Detecting influenza epidemics using search engine query data. Nature, 457, 1012–1014.

    Article  Google Scholar 

  • Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of National Academy Science, 99(12), 7821–7826.

    Article  MathSciNet  MATH  Google Scholar 

  • Golbeck, J., & Hansen, D. (2014). A method for computing political preference among Twitter followers. Social Networks, 36, 177–184.

    Article  Google Scholar 

  • Goncalves, B., & Perra, N. (Eds.). (2015). Social phenomena: From data analysis to models (Computational social sciences). Cham: Springer.

    Google Scholar 

  • Goodwin, S. A., Gubin, A., Fiske, S. T., & Yzerbyt, V. Y. (2000). Power can bias impression processes: Stereotyping: Subordinates by default and by design. Group Processes Intergroup Relations, 3, 227–256.

    Article  Google Scholar 

  • Granovetter, M. S. (1978). Threshold models of collective behavior. American Journal of Sociology, 78(6), 1420–1443.

    Article  Google Scholar 

  • Gudykunst, W. B. (1995). Anxiety/uncertainty management theory. In R. Wiseman (Ed.), Intercultural communication theory. Thousand Oaks, CA: SAGE.

    Google Scholar 

  • Habermas, J. (1991). The structural transformation of the public sphere. Cambridge, MA: MIT Press.

    Google Scholar 

  • Hayek, F. A. (1945). The use of knowledge in society. The American Economic Review, 35(4), 519–530.

    Google Scholar 

  • He, X., & Lin, Y.-R. (2017). Measuring and monitoring collective attention during shocking events. EPJ Data Science, 6, 30. https://doi.org/10.1140/epjds/s13688-017-0126-4.

    Article  Google Scholar 

  • Hegselmann, R., & Krause, U. (2002). Opinion dynamics and bounded confidence: Models, analysis and simulation. Journal of Artificial Social Simulation, 5, 3. http://jasss.soc.surrey.ac.uk/5/3/2.html.

    Google Scholar 

  • Helbing, D. (2012). Social self-organization. Cham: Springer.

    Book  Google Scholar 

  • Hendricks, V. F., & Vestergaard, M. (Eds.). (2018). Reality lost: Markets of attention, misinformation and manipulation. Cham: Springer.

    Google Scholar 

  • Hill, K. A., & Hughes, J. E. (1998). Cyberpolitics: Citizen activism in the age of the internet. Lanham, ML: Rowman & Littlefield Publishers.

    Google Scholar 

  • Hoshen, J., & Kopelman, R. (1976). Percolation and cluster distribution. I. Cluster multiple labeling technique and critical concentration algorithm. Physical Review B, 14, 3438.

    Article  Google Scholar 

  • Howard, D., et al. (2019). Evolving embodied intelligence from materials to machines. Nature Machine Intelligence, 1, 12–19.

    Article  Google Scholar 

  • Huckfeldt, R., et al. (2014). Noise, bias, and expertise in political communication networks. Social Networks, 36, 110–121.

    Article  Google Scholar 

  • Johansson, M., & Jäntti, R. (2011). Wireless networking for control: Technologies and models. In Networked control systems (Vol. 406, pp. 31–74). London: Springer.

    Chapter  MATH  Google Scholar 

  • Johnson, N. F., et al. (2019). Hidden resilience and adaptive dynamics of the global online hate ecology. Nature. https://doi.org/10.1038/s41586-019-1494-7.

  • Jordan, T. (2001). Language and libertarianism: The politics of cyberculture and the culture of cyberpolitics. The Sociological Review, 49(1), 1–17.

    Article  MathSciNet  Google Scholar 

  • Jost, J. T., et al. (2018). How social media facilitates political protest: Information, motivation, and social network. Advances in Political Psychology, 39(1), 85–118.

    Article  Google Scholar 

  • Katz, M. L., & Shapiro, C. (1985). Network externalities, competition and compatibility. The American Economic Review, 75(3), 424–444.

    Google Scholar 

  • Kozma, B., & Barrat, A. (2008). Consensus formation on adaptive networks. Physical Review, E, 77, 016102.

    Article  Google Scholar 

  • Lamont, M., et al. (2017). Trump’s electoral speeches and his appeal to the American white working class. The British Journal of Sociology, 68(S1), S153–S180. https://doi.org/10.1111/1468-4446.12315.

    Article  Google Scholar 

  • Le Bonn, G. (1931a). The crowd: Study of the popular mind. iBook version. (Apple iBook Store).

    Google Scholar 

  • Le Bonn, G. (1931b). The psychology of revolution. iBook version. (Apple iBook Store).

    Google Scholar 

  • Lee, J., & Oh, J. J. (2018). What motivates a citizen to take the initiative in e-participation?: The case of a south Korean parliamentary hearing. Communications of the ACM, 61(12), 56–61.

    Article  Google Scholar 

  • Li, J., Vishwanath, A., & Rao, H. R. (2014). Retweeting the Fukushima nuclear radiation disaster. Communication of the ACM, 57(1), 78–85.

    Article  Google Scholar 

  • Lippmann, W. (1922). Public opinion. San Diego, CA: Harcourt Brace and Company.

    Google Scholar 

  • MacCoun, R. J. (2012). The burden of social proof: Shared thresholds and social influence. Psychological Review, 119(2), 345–372.

    Article  Google Scholar 

  • Mackie, D. M., & Skelly, J. J. (1994). The social cognition analysis of social influence: Contributions to the understanding of persuasion and conformity. In P. G. Devine, D. L. Hamilton, & T. M. Ostrom (Eds.), Social cognition: Impact on social psychology. Cambridge, MA: Academic.

    Google Scholar 

  • Malarz, K., & Galam, M. (2005). Square-lattice site percolation at increasing ranges of neighbor bonds. Physical Review E, 71, 016125.

    Article  Google Scholar 

  • Malinick, T. E., et al. (2013). Network centrality and social movement media coverage: A two-mode network analytic approach. Social Networks, 35, 148–158.

    Article  Google Scholar 

  • Massimo, T., & Christophe, C. (Eds.). (2017). Handbook of biometrics for forensic science. Cham: Springer.

    Google Scholar 

  • Mendoza, M., Poblete, B., & Valderrama, I. (2019). Nowcasting earthquake damages with Twitter. EPJ Data Science, 8, 3. https://epjdatascience.springeropen.com/track/pdf/10.1140/epjds/s13688-019-0181-0.

    Article  Google Scholar 

  • Morgenthau, H. J. (1978). Politics among nations: The struggle for power and peace. New York: Knopf.

    Google Scholar 

  • Nash, R., et al. (2013). Investigating in people: The role of social networks in the diffusion of a large-scale fraud. Social Networks, 35(4), 686–698.

    Article  Google Scholar 

  • Newman, M. E. J. (2010). Networks: An introduction. Oxford, UK: Oxford University Press.

    Book  MATH  Google Scholar 

  • Newman, M. E. J., & Park, J. (2003). Why social networks are different from other types of networks. Physical Review E, 68, 036122.

    Article  Google Scholar 

  • Newman, M. E. J., & Ziff, R. M. (2001). Fast Monte Carlo algorithm for site or bond percolation. Physical Review E, 64, 016706.

    Article  Google Scholar 

  • Nielsen, R. K., & Graves, L. (2017). “News you don’t believe”: Audience perspective on fake news. Reuters Institute Fact Sheet (October 2017).

    Google Scholar 

  • Nowak, M. A. (2006). Five rules for the evolution of cooperation. Science, 314(5805), 1560–1563.

    Article  Google Scholar 

  • Nowak, M. A., & Sigmund, K. (2005). Evolution of indirect reciprocity. Nature, 43(7), 1291–1298.

    Article  Google Scholar 

  • O’Sullivan, D., & Perry, G. L. W. (2013). Spatial simulation: Exploring pattern and process. Hoboken, NJ: Wiley-Blackwell.

    Book  Google Scholar 

  • OECD/NEA. (2016). Five years after the Fukushima Daiichi accident: Nuclear safety improvements and lessons learnt. https://www.oecd-nea.org/nsd/pubs/2016/7284-five-years-fukushima.pdf

  • Olson, M. (1971). The logic of collective action: Public goods and the theory of groups. Cambridge, MA: Harvard University Press.

    Google Scholar 

  • Operario, D., Goodwin, S. A., & Fiske, S. T. (1998). Power is everywhere. In R. S. Wyer Jr. (Ed.), Stereotype activation and inhibition. Boca Raton, FL: LEA.

    Google Scholar 

  • Papacharissi, Z. (2010). A networked self: Identity, community, and culture on social network sites. Abingdon, UK: Routledge.

    Book  Google Scholar 

  • Pentland, A. (2014). Social physics. London: Penguin Press.

    Google Scholar 

  • Pew Research Center. (2017). News use across social media platforms 2017. http://assets.pewresearch.org/wp-content/uploads/sites/13/2017/09/13163032/PJ_17.08.23_socialMediaUpdate_FINAL.pdf

  • Popper, K. (1963). Conjectures and refutations. Abingdon, UK: Routledge.

    Google Scholar 

  • Rohlinger, D. A., & Snow, D. A. (2003). Social psychological perspectives on crowds and social movement. In J. Delamater (Ed.), Handbook of social psychology. Dordrecht: Kluwer Academic.

    Google Scholar 

  • Sadiki, L. (2015). Routledge handbook of the Arab Spring: Rethinking democratization. Abingdon, UK: Routledge.

    Google Scholar 

  • Science Council of Japan (SCJ). (2011). Report to the foreign academies from science council of Japan on the Fukushima Daiichi Nuclear Power Plant accident. http://www.scj.go.jp/en/report/houkoku-110502-7.pdf

  • Searle, J. R. (1996). The construction of social reality. London: Penguin Press.

    Google Scholar 

  • Shao, C., Ciampaglia, G. L., Varol, O., Yang, K.-C., Flammini, A., & Menczer, F. (2018). The spread of low-credibility content by social bots. Nature Communications, 9, 4787.

    Article  Google Scholar 

  • Shi, F., Teplitskiy, M., Duede, E., & Evans, J. A. (2019). The wisdom of polarized crowds. Nature Human Behaviour, 3, 329–336.

    Article  Google Scholar 

  • Shibuya, K. (2004). A framework of multi-agent based modeling, simulation and computational assistance in an ubiquitous environment. Simulation, 80(7–8), 367–380.

    Article  Google Scholar 

  • Shibuya, K. (2012). A study on participatory support networking by voluntary citizens-the lessons from the Tohoku earthquake disaster. Oukan, 6(2), 79–86. (in Japanese).

    Google Scholar 

  • Shibuya, K. (2017a). Bridging between cyber politics and collective dynamics of social movement. In M. Khosrow-Pour (Ed.), Encyclopedia of information science and technology (4th ed., pp. 3538–3548). (Chapter 307), IGI Global.

    Google Scholar 

  • Shibuya, K. (2017b). An exploring study on networked market disruption and resilience. KAKENHI report (no. 26590105), pp. 1–200 (in Japanese).

    Google Scholar 

  • Shibuya, K. (2018). A design of Fukushima simulation. The society for risk analysis: Asia conference 2018, Japan.

    Google Scholar 

  • Shibuya, K. (2021). Breaking fake news and verifying truth. In Mehdi Khosrow-Pour (Ed.) Encyclopedia of organizational knowledge, administration, and technologies (1st Ed.), IGI Global (in press).

    Google Scholar 

  • Skibski, O., Rahwan, T., Michalak, T. P., & Yokoo, M. (2019). Attachment centrality: Measure for connectivity in networks. Artificial Intelligence, 274, 151–179.

    Article  MathSciNet  MATH  Google Scholar 

  • Smelser, N. J. (1962). Theory of collective behavior. New York: The Free Press.

    Google Scholar 

  • Stovel, K., & Fountain, C. (2009). Matching. In P. Hedström & P. Bearman (Eds.), Oxford handbook of analytical sociology. Oxford, UK: Oxford University Press.

    Google Scholar 

  • Strogatz, S. (2000). From Kuramoto to Crawford: Exploring the onset of synchronization in populations of coupled oscillators. Physica D, 143, 1–20.

    Article  MathSciNet  MATH  Google Scholar 

  • Sunstein, C. R. (2001). Republic.com. Princeton, NJ: Princeton University Press.

    Google Scholar 

  • Tistarelli, M., Li, S. Z., & Chellappa, R. (Eds.). (2009). Handbook of remote biometrics: For surveillance and security. London: Springer.

    Google Scholar 

  • Topçu, S. (2013). La France nucléaire: L’art de gouverner une technologie contestée. Paris: Le Seuil.

    Book  Google Scholar 

  • Viviani, P., & Pasi, G. (2017). Credibility in social media: Opinions, news, and health information-a survey. WIREs Data Mining and Knowledge Discovery, 7, e1209. https://doi.org/10.1002/widm.1209.

    Article  Google Scholar 

  • Vosoughi, S., et al. (2018). The spread of true and false online. Science, 359, 1146–1151.

    Article  Google Scholar 

  • Wang, L., & Graddy, E. (2008). Social capital, volunteering, and charitable giving. Voluntas: International Journal of Voluntary and Nonprofit Organizations, 19(1), 23–42.

    Article  Google Scholar 

  • Wang, Y., et al. (2017). To follow or not to follow: Analyzing the growth patterns of the Trumpists on Twitter. https://arxiv.org/pdf/1603.08174

  • Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge, UK: Cambridge University Press.

    Book  MATH  Google Scholar 

  • Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441–458. http://www.uvm.edu/pdodds/teaching/courses/2009-08UVM-300/docs/others/2007/watts2007a.pdf.

    Article  Google Scholar 

  • Weinberger, S. (2011). Web of war. Nature, 471, 566–568.

    Article  Google Scholar 

  • Widener, W. J., et al. (2013). Simulating the effects of social networks on a population’s hurricane evacuation participation. Journal of Geographical Systems, 15(2), 193–209.

    Article  Google Scholar 

  • Wilson, J. (2000). Volunteering. Annual Review of Sociology, 26, 215–240.

    Article  Google Scholar 

  • Wittgenstein, L. (1922). Tractatus Logico-philosophicus. San Diego, CA: Harcourt, Brace.

    MATH  Google Scholar 

  • Yang, X. (2011). Urban remote sensing: Monitoring, synthesis and modeling in the urban environment. Hoboken, NJ: Wiley.

    Book  Google Scholar 

  • Yoshino, R., Nikaido, D., & Fujita, T. (2009). Cultural manifold analysis (CULMAN) of national character: Paradigm of cross-cultural survey. Behaviormetrika, 36(2), 89–114.

    Article  Google Scholar 

  • Zhao, S., Grasmuck, S., & Martin, J. (2013). Identity construction on Facebook: Digital empowerment in anchored relationships. Computers in Human Behavior, 24(5), 1816–1836.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Shibuya, K. (2020). Networked Identity. In: Digital Transformation of Identity in the Age of Artificial Intelligence. Springer, Singapore. https://doi.org/10.1007/978-981-15-2248-2_10

Download citation

Publish with us

Policies and ethics