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Theorizing Social Media: A Formalization of the Multilevel Model of Meme Diffusion 2.0 (M3D2.0)

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Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics

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Abstract

The capabilities of researching social dynamics as big data have significantly outpaced the formulation of theory to account for the processes being discovered. This essay extends a formative conceptualization of social media communication as meme diffusion into a propositional model, animated largely by evolutionary and attention economy explanatory metaphors. The result is an integrative model formalized in 18 propositions, indicating that multiple system factors influence the generation and attrition of social media messages. The system levels include features of the meme itself, its medium, its source, its social network and societal context, the interference or facilitation of geospatial, technical and significant societal events. As such, memes diffuse sometimes because of the information value of events (evememic), viral meme cycles (entymemic), or some combination of these processes (polymemic). The model integrates extensive cross-disciplinary research and manifold theoretical influences in the interest of demonstrating a process of theory construction in the context of social media and new media.

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References

  • Adamic, L. A. (2015). The information life of social networks. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining. Association for Computing Machinery, New York, NY, USA, pp. 273–274.

    Google Scholar 

  • Adams, P. C. (2011). A taxonomy for communication geography. Progress in Human Geography, 35, 37–57.

    Article  Google Scholar 

  • Adams, P. C. (2017). Geographies of media and communication I: Metaphysics of encounter. Progress in Human Geography, 41, 365–374.

    Article  Google Scholar 

  • Andersen, P. A. (1998). The cognitive valence theory of intimate communication. In M. T. Palmer & G. A. Barnett (Eds.), Mutual Influ Interpers. Commun. Theory Res. Cogn. Affect Behav (pp. 39–72). Westport: Greenwood Publishing Group.

    Google Scholar 

  • Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences, 106, 21544–21549.

    Article  Google Scholar 

  • Bae, Y., & Lee, H. (2012). Sentiment analysis of Twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the American Society for Information Science and Technology, 63, 2521–2535.

    Article  Google Scholar 

  • Bauckhage, C., Kersting, K. (2014). Strong regularities in growth and decline of popularity of social media services. Ar**v14066529 Phys.

    Google Scholar 

  • Bauckhage, C., Kersting, K. (2016). Collective attention on the web. Foundations and Trends® Web Science, 5, 1–136

    Google Scholar 

  • Baum, W. M. (2013). What counts as behavior? The molar multiscale view. Behavior Analyst, 36, 283–293.

    Article  Google Scholar 

  • Bennett, W. L., Segerberg, A., & Yang, Y. (2018). The strength of peripheral networks: Negotiating attention and meaning in complex media ecologies. The Journal of Communication, 68, 659–684.

    Article  Google Scholar 

  • Berger, J. (2011). Arousal increases social transmission of information. Psychological Science, 22, 891–893.

    Article  Google Scholar 

  • Berger, J., & Milkman, K. L. (2012). What makes online content viral? Journal of Marketing Research, 49, 192–205.

    Article  Google Scholar 

  • Berger, J., & Packard, G. (2018). Are atypical things more popular? Psychological Science, 29, 1178–1184.

    Article  Google Scholar 

  • Chiang, Y.-S. (2007). Birds of moderately different feathers: Bandwagon dynamics and the threshold heterogeneity of network neighbors. Journal of Mathematical Sociology, 31, 47–69.

    Article  Google Scholar 

  • Chiu, C.-Y. (Chad), Balkundi, P., Weinberg, F. J. (2017). When managers become leaders: The role of manager network centralities, social power, and followers’ perception of leadership. The Leadership Quarterly, 28, 334–348.

    Google Scholar 

  • Chiu, H.-C., Hsieh, Y.-C., Kao, Y.-H., & Lee, M. (2007). The determinants of email receivers’ disseminating behaviors on the internet. Journal of Advertising Research, 47, 524–534.

    Article  Google Scholar 

  • Choi, D., Chun, S., Oh, H., Han, J., & Kwon, T. (2020). Rumor propagation is amplified by echo chambers in social media. Science and Reports, 10, 310.

    Article  Google Scholar 

  • Ciampaglia, G. L., Flammini, A., & Menczer, F. (2015). The production of information in the attention economy. Science and Reports, 5, 9452.

    Article  Google Scholar 

  • Clark, J. C., Spitzberg, B. H., Tsou, M. H. (2018). The digipolitical animal: Investigating the memetic diffusion of political messages on Twitter. Western States Communication Association Conference.

    Google Scholar 

  • Coscia, M. (2017). Popularity spikes hurt future chances for viral propagation of protomemes. Communications of the ACM, 61, 70–77.

    Article  Google Scholar 

  • Crenshaw, A. O., Leo, K., & Baucom, B. R. W. (2019). The effect of stress on empathic accuracy in romantic couples. Journal of Family Psychology, 33, 327–337.

    Article  Google Scholar 

  • Dancygier, B., & Vandelanotte, L. (2017). Internet memes as multimodal constructions. Cognitive Linguistics, 28, 565–598.

    Article  Google Scholar 

  • Dawkins, R. (2016). The selfish gene. Oxford: Oxford University Press.

    Google Scholar 

  • De Meo, P., Ferrara, E., Fiumara, G., Provetti, A. (2014) On Facebook, most ties are weak. Communications of the ACM, 57, 78–84.

    Google Scholar 

  • Dzogang, F., Lightman, S., & Cristianini, N. (2017). Circadian mood variations in Twitter content. Brain and Neuroscience Advances, 1, 2398212817744501.

    Article  Google Scholar 

  • Dzogang, F., Lightman, S., Cristianini, N. (2018). Diurnal variations of psychometric indicators in Twitter content. PLoS One, 13, e0197002.

    Google Scholar 

  • Evans, S. K., Pearce, K. E., Vitak, J., & Treem, J. W. (2017). Explicating affordances: A conceptual framework for understanding affordances in communication research. Journal of Computer-Mediated Communication, 22, 35–52.

    Article  Google Scholar 

  • Falkinger, J. (2007). Attention economies. Journal of Economic Theory, 133, 266–294.

    Article  Google Scholar 

  • Feng, L., Hu, Y., Li, B., Stanley, H. E., Havlin, S., Braunstein, L. A. (2015). Competing for attention in social media under information overload conditions. PLoS One, 10, e0126090.

    Google Scholar 

  • Ferrara, E., Yang, Z. (2015). Measuring emotional contagion in social media. PLoS One, 10, e0142390.

    Google Scholar 

  • Fisher, J. T., Keene, J. R., Huskey, R., & Weber, R. (2018). The limited capacity model of motivated mediated message processing: Taking stock of the past. Annals of the International Communication Association, 42, 270–290.

    Article  Google Scholar 

  • Friedman, D., & Singh, N. (2003). Negative reciprocity: The coevolution of memes and genes. SSRN. https://doi.org/10.2139/ssrn.509242

    Article  Google Scholar 

  • Gleeson, J. P., Ward, J. A., O’Sullivan, K. P., Lee, W. T. (2014). Competition-induced criticality in a model of meme popularity. Physical Review Letters, 112, 048701.

    Google Scholar 

  • Glorieux, I. (1993). Social interaction and the social meanings of action: A time budget approach. Social Indicators Research, 30, 149–173.

    Article  Google Scholar 

  • Golo, N., & Galam, S. (2015). Conspiratorial beliefs observed through entropy principles. Entropy, 17, 5611–5634.

    Article  Google Scholar 

  • Gonçalves, B., Perra, N., Vespignani, A. (2011). Modeling users’ activity on Twitter networks: Validation of Dunbar’s Number. PLoS One 6, e22656.

    Google Scholar 

  • Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociol Theory, 1, 201–233.

    Article  Google Scholar 

  • Gupta, K., Jenkins-Smith, H. (2015). Anthony Downs, Up and down with ecology: The ‘issue-attention’ cycle. In M. Lodge, E. C. Page, S. J. Balla (Eds.), The Oxford Handbook of Classics in Public Policy and Administration. Oxford, pp. 1–12.

    Google Scholar 

  • Han, S. Y., Tsou, M.-H., & Clarke, K. C. (2018). Revisiting the death of geography in the era of big data: The friction of distance in cyberspace and real space. International Journal of Digital Earth, 11, 451–469.

    Article  Google Scholar 

  • Hayes, A. F., Shanahan, J., & Glynn, C. J. (2001). Willingness to express one’s opinion in a realistic situation as a function of perceived support for that opinion. International Journal of Public Opinion Research, 13, 45–58.

    Article  Google Scholar 

  • He, S., Zheng, X., & Zeng, D. (2016). A model-free scheme for meme ranking in social media. Decision Support Systems, 81, 1–11.

    Article  Google Scholar 

  • Hodas, N. O., & Lerman, K. (2014). The simple rules of social contagion. Science and Reports, 4, 4343.

    Article  Google Scholar 

  • Hofstadter, D. R. (1985). Metamagical Themas: Questing for the Essence of Mind and Pattern. New York: Basic Books.

    Google Scholar 

  • Jang, S. M., & Pasek, J. (2015). Assessing the carrying capacity of Twitter and online news. Mass Communication and Society, 18, 577–598.

    Article  Google Scholar 

  • Kätsyri, J., Kinnunen, T., Kusumoto, K., Oittinen, P., Ravaja, N. (2016). Negativity bias in media multitasking: The effects of negative social media messages on attention to television news broadcasts. PLoS One, 11, e0153712.

    Google Scholar 

  • Kidd, C., Piantadosi, S. T., Aslin, R. N. (2012). The goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PLoS One, 7, e36399.

    Google Scholar 

  • Kim, H. S. (2015). Attracting views and going viral: How message features and news-sharing channels affect health news diffusion. The Journal of Communication, 65, 512–534.

    Article  Google Scholar 

  • LaRose, R., Connolly, R., Lee, H., Li, K., & Hales, K. D. (2014). Connection overload? A cross cultural study of the consequences of social media connection. Information Systems Management, 31, 59–73.

    Article  Google Scholar 

  • Lee, E.-J., & Shin, S. Y. (2014). When the medium is the message: How transportability moderates the effects of politicians’ Twitter communication. Communication Research, 41, 1088–1110.

    Article  Google Scholar 

  • Leskovec, J., Backstrom, L., Kleinberg, J. (2009). Meme-tracking and the dynamics of the news cycle. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp. 497–506.

    Google Scholar 

  • Liang, H. (2018). Broadcast versus viral spreading: The structure of diffusion cascades and selective sharing on social media. The Journal of Communication, 68, 525–546.

    Article  Google Scholar 

  • Liang, H., Fu, K. (2015). Testing propositions derived from Twitter studies: Generalization and replication in computational social science. PLoS One, 10, e0134270.

    Google Scholar 

  • Liu-Thompkins, Y. (2012). Seeding viral content: The role of message and network factors. Journal of Advertising Research, 52, 465–478.

    Article  Google Scholar 

  • Lu, J., & Cheng, L. (2013). Perceiving and interacting affordances: A new model of human–affordance interactions. Integrative Psychological & Behavioral Science, 47, 142–155.

    Article  Google Scholar 

  • Luginbuehl, T., & Schoebi, D. (2020). Emotion dynamics and responsiveness in intimate relationships. Emotion, 20, 133–148.

    Article  Google Scholar 

  • Luo, S., & Zhang, G. (2009). What leads to romantic attraction: Similarity, reciprocity, security, or beauty? Evidence from a speed-dating study. Journal of Personality, 77, 933–964.

    Article  Google Scholar 

  • Malmgren, R. D., Stouffer, D. B., Motter, A. E., & Amaral, L. A. N. (2008). A Poissonian explanation for heavy tails in e-mail communication. Proceedings of the National Academy of Sciences, 105, 18153–18158.

    Article  Google Scholar 

  • Marois, R., & Ivanoff, J. (2005). Capacity limits of information processing in the brain. Trends in Cognitive Sciences, 9, 296–305.

    Article  Google Scholar 

  • Martinez, L. S., Hughes, S., Walsh-Buhi, E. R., & Tsou, M.-H. (2018). Okay, we get it. You vape: An analysis of geocoded content, context, and sentiment regarding e-cigarettes on Twitter. Journal of Health Communication, 23, 550–562.

    Article  Google Scholar 

  • Mazzocchi, F. (2015). Could big data be the end of theory in science? EMBO Reports, 16, 1250–1255.

    Article  Google Scholar 

  • Miritello, G., Lara, R., Cebrian, M., & Moro, E. (2013). Limited communication capacity unveils strategies for human interaction. Science and Reports, 3, 1950.

    Article  Google Scholar 

  • Myers, S. A., Leskovec, J. (2012). Clash of the contagions: Cooperation and competition in information diffusion. In 2012 IEEE 12th International Conference on Data Mining, pp. 539–548.

    Google Scholar 

  • Myers, S. A., Zhu, C., Leskovec, J. (2012). Information diffusion and external influence in networks. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, New York, NY, USA, pp. 33–41.

    Google Scholar 

  • Myers, S. A., Sharma, A., Gupta, P., Lin, J. (2014). Information network or social network? The structure of the Twitter follow graph. In Proceedings of the 23rd International Conference World Wide Web. Association for Computing Machinery, New York, NY, USA, pp. 493–498.

    Google Scholar 

  • Neuman, R. W., Guggenheim, L., Mo Jang, S., & Bae, S. Y. (2014). The dynamics of public attention: Agenda-setting theory meets big data. The Journal of Communication, 64, 193–214.

    Article  Google Scholar 

  • Nikolinakou, A., & King, K. W. (2018). Viral video ads: Emotional triggers and social media virality. Psychology and Marketing, 35, 715–726.

    Article  Google Scholar 

  • Nikolov, D., Oliveira, D. F. M., Flammini, A., Menczer, F. (2015). Measuring online social bubbles. PeerJ Computer Science, 1, e38.

    Google Scholar 

  • Nissenbaum, A., & Shifman, L. (2018). Meme templates as expressive repertoires in a globalizing world: A cross-linguistic study. Journal of Computer-Mediated Communication, 23, 294–310.

    Article  Google Scholar 

  • Oh, S., & Syn, S. Y. (2015). Motivations for sharing information and social support in social media: A comparative analysis of Facebook, Twitter, Delicious, YouTube, and Flickr. Journal of the Association for Information Science and Technology, 66, 2045–2060.

    Article  Google Scholar 

  • Peng, T.-Q., Sun, G., Wu, Y. (2017). Interplay between public attention and public emotion toward multiple social issues on Twitter. PLoS One, 12, e0167896.

    Google Scholar 

  • Pereira, A. (2018). Exploring the multimodal argument: The interplay of multimodality and attention economy. Pedagogies: An International Journal, 13, 201–221.

    Article  Google Scholar 

  • Phua, J., **, S. V., & Kim, J (Jay). (2017). Uses and gratifications of social networking sites for bridging and bonding social capital: A comparison of Facebook, Twitter, Instagram, and Snapchat. Computers in Human Behavior, 72, 115–122.

    Article  Google Scholar 

  • Qiu, L., Tang, Q., & Whinston, A. B. (2015). Two formulas for success in social media: Learning and network effects. Journal of Management Information Systems, 32, 78–108.

    Article  Google Scholar 

  • Ruppel, E. K., & Burke, T. J. (2015). Complementary channel use and the role of social competence. Journal of Computer-Mediated Communication, 20, 37–51.

    Article  Google Scholar 

  • Sasahara, K., Hirata, Y., Toyoda, M., Kitsuregawa, M., Aihara, K. (2013) Correction: Quantifying collective attention from tweet stream. PLoS One, 8. https://doi.org/10.1371/annotation/25b6b59d.

  • Schäfer, M. S., Ivanova, A., & Schmidt, A. (2014). What drives media attention for climate change? Explaining issue attention in Australian, German and Indian print media from 1996 to 2010. International Communication Gazette, 76, 152–176.

    Article  Google Scholar 

  • Schlaile, M. P. (2021). A case for economemetics? Why evolutionary economists should re-evaluate the (f)utility of memetics. In M. P. Schlaile (Ed.), Memetics and Evolutionary Economics: To Boldly Go Where no Meme has Gone Before (pp. 33–68). Cham: Springer International Publishing.

    Chapter  Google Scholar 

  • Schlaile, M. P., Knausberg, T., Mueller, M., & Zeman, J. (2018). Viral ice buckets: A memetic perspective on the ALS Ice Bucket Challenge’s diffusion. Cognitive Systems Research, 52, 947–969.

    Article  Google Scholar 

  • Schonmann, R. H., & Boyd, R. (2016). A simple rule for the evolution of contingent cooperation in large groups. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 371, 20150099.

    Article  Google Scholar 

  • Shi, J., Hu, P., Lai, K. K., & Chen, G. (2018). Determinants of users’ information dissemination behavior on social networking sites: An elaboration likelihood model perspective. Internet Research, 28, 393–418.

    Article  Google Scholar 

  • Shifman, L. (2013). Memes in a digital world: Reconciling with a conceptual troublemaker. Journal of Computer-Mediated Communication, 18, 362–377.

    Article  Google Scholar 

  • Simkin, M. V., & Roychowdhury, V. P. (2015). Why does attention to web articles fall with time? Journal of the Association for Information Science and Technology, 66, 1847–1856.

    Article  Google Scholar 

  • Simon, H. A. (1971). Designing organizations for an information-rich world. In GreenBerger (Ed.), Computers, Communications, and the Public Interest. Johns Hopkins Press, pp. 37–72.

    Google Scholar 

  • Simons, R. F., Detenber, B. H., Cuthbert, B. N., Schwartz, D. D., & Reiss, J. E. (2003). Attention to television: Alpha power and its relationship to image motion and emotional content. Media Psychology, 5, 283–301.

    Article  Google Scholar 

  • Smith, T., Coyle, J. R., Lightfoot, E., & Scott, A. (2007). Reconsidering models of influence: The relationship between consumer social networks and word-of-mouth effectiveness. Journal of Advertising Research, 47, 387–397.

    Article  Google Scholar 

  • Spitzberg, B. H. (2014). Toward a Model of Meme Diffusion (M3D). Communication Theory, 24, 311–339.

    Article  Google Scholar 

  • Spitzberg, B. H. (2019). Trace of pace, place, and space in personal relationships: The chronogeometrics of studying relationships at scale. Personal Relationships, 26, 184–208.

    Article  Google Scholar 

  • Spitzberg, B. H., & Record, R. A. (2020). Mediated communication. In B. H. Spitzberg, D. J. Canary, H. E. Canary, & P. A. Andersen (Eds.), Commun . Capstone Commun. Inq. Theory Exp (pp. 321–335). San Diego: Cognella Academic Publishing.

    Google Scholar 

  • Spitzberg, B. H., Tsou, M.-H., Gupta, D. K., An, L., Gawron, J. M., & Lusher, D. (2013). The map is not which territory?: Speculating on the geo-spatial diffusion of ideas in the Arab Spring of 2011. Studies in Media and Communication, 1, 101–115.

    Article  Google Scholar 

  • Stai, E., Karyotis, V., Bitsaki, A.-C., & Papavassiliou, S. (2017). Strategy evolution of information diffusion under time-varying user behavior in generalized networks. Computer Communications, 100, 91–103.

    Article  Google Scholar 

  • Steigenberger, N. (2015). Emotions in sensemaking: A change management perspective. Journal of Organizational Change Management, 28, 432–451.

    Article  Google Scholar 

  • Stephens, K. K., Mandhana, D. M., Kim, J. J., Li, X., Glowacki, E. M., & Cruz, I. (2017). Reconceptualizing communication overload and building a theoretical foundation. Communication Theory, 27, 269–289.

    Article  Google Scholar 

  • Stieglitz, S., & Dang-Xuan, L. (2013). Emotions and information diffusion in social media—Sentiment of microblogs and sharing behavior. Journal of Management Information Systems, 29, 217–248.

    Article  Google Scholar 

  • Susarla, A., Oh, J.-H., & Tan, Y. (2016). Influentials, imitables, or susceptibles? Virality and word-of-mouth conversations in online social networks. Journal of Management Information Systems, 33, 139–170.

    Article  Google Scholar 

  • Sutton, J., Gibson, C. B., Phillips, N. E., Spiro, E. S., League, C., Johnson, B., Fitzhugh, S. M., & Butts, C. T. (2015). A cross-hazard analysis of terse message retransmission on Twitter. Proceedings of the National Academy of Sciences, 112, 14793–14798.

    Article  Google Scholar 

  • Tamarit, I., Cuesta, J. A., Dunbar, R. I. M., & Sánchez, A. (2018). Cognitive resource allocation determines the organization of personal networks. Proceedings of the National Academy of Sciences, 115, 8316–8321.

    Article  Google Scholar 

  • Tomblinson, C. M., Wadhwa, V., Latimer, E., Gauss, C. H., & McCarty, J. L. (2019). Publicly available metrics underestimate AJNR Twitter impact and follower engagement. American Journal of Neuroradiology, 40, 1994–1997.

    Google Scholar 

  • Turner, J. H. (1990). The misuse and use of metatheory. Sociological Forum, 5, 37–53.

    Article  Google Scholar 

  • Twenge, J. M. (2019). More time on technology, less happiness? Associations between digital-media use and psychological well-being. Current Directions in Psychological Science, 28, 372–379.

    Article  Google Scholar 

  • Unkelbach, C., Koch, A., Silva, R. R., & Garcia-Marques, T. (2019). Truth by repetition: Explanations and implications. Current Directions in Psychological Science, 28, 247–253.

    Article  Google Scholar 

  • Vosoughi, S., Roy, D., & Aral, S. (2018). The spread of true and false news online. Science, 359, 1146–1151.

    Article  Google Scholar 

  • Vu, H. N. N., & Gehrau, V. (2010). Agenda diffusion: An integrated model of agenda setting and interpersonal communication. Journalism & Mass Communication Quarterly, 87, 100–116.

    Article  Google Scholar 

  • Waldherr, A. (2014). Emergence of news waves: A social simulation approach. The Journal of Communication, 64, 852–873.

    Article  Google Scholar 

  • Walker, L., Baines, P. R., Dimitriu, R., & Macdonald, E. K. (2017). Antecedents of retweeting in a (political) marketing context. Psychology and Marketing, 34, 275–293.

    Article  Google Scholar 

  • Wang, X., & Song, Y. (2020). Viral misinformation and echo chambers: The diffusion of rumors about genetically modified organisms on social media. Internet Research, 30, 1547–1564.

    Article  Google Scholar 

  • Wang, J., & Wang, H. (2015). From a marketplace to a cultural space: Online meme as an operational unit of cultural transmission. Journal of Technical Writing and Communication, 45, 261–274.

    Article  Google Scholar 

  • Wang, Y., Zheng, B. (2014). On macro and micro exploration of hashtag diffusion in Twitter. In 2014 IEEEACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2014, pp. 285–288.

    Google Scholar 

  • Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. The Journal of Consumer Research, 34, 441–458.

    Article  Google Scholar 

  • Webberley, W. M., Allen, S. M., & Whitaker, R. M. (2016). Retweeting beyond expectation: Inferring interestingness in Twitter. Computer Communications, 73, 229–235.

    Article  Google Scholar 

  • Weng, L., Menczer, F. (2015). Topicality and impact in social media: Diverse messages, focused messengers. PLoS One, 10, e0118410.

    Google Scholar 

  • Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Science and Reports, 2, 335.

    Article  Google Scholar 

  • Weng, L., Menczer, F., & Ahn, Y.-Y. (2013). Virality prediction and community structure in social networks. Science and Reports, 3, 2522.

    Article  Google Scholar 

  • Weng, L., Menczer, F., Ahn, Y.-Y. (2014). Predicting successful memes using network and community structure. Ar**v14036199 Phys.

    Google Scholar 

  • Wise, A. F., & Shaffer, D. W. (2015). Why theory matters more than ever in the age of big data. The Journal of Learning Analytics, 2, 5–13.

    Article  Google Scholar 

  • **ao, L., Khazaei, T. (2019). Changing others’ beliefs online: online comments’ persuasiveness. In Proceedings of the 10th Int. Conf. Soc. Media Soc. Association for Computing Machinery, New York, NY, USA, pp. 92–101.

    Google Scholar 

  • **ao, M., Wang, R., & Chan-Olmsted, S. (2018). Factors affecting YouTube influencer marketing credibility: A heuristic-systematic model. Journal of Media Business Studies, 15, 188–213.

    Article  Google Scholar 

  • Xu, P., Wu, Y., Wei, E., Peng, T. Q., Liu, S., Zhu, J. J. H., & Qu, H. (2013). Visual analysis of topic competition on social media. IEEE Transactions on Visualization and Computer Graphics, 19, 2012–2021.

    Article  Google Scholar 

  • Yan, X., & Jiang, P. (2018). Effect of the dynamics of human behavior on the competitive spreading of information. Computers in Human Behavior, 89, 1–7.

    Article  Google Scholar 

  • Yang, Q., Tufts, C., Ungar, L., Guntuku, S., & Merchant, R. (2018). To retweet or not to retweet: Understanding what features of cardiovascular tweets influence their retransmission. Journal of Health Communication, 23, 1026–1035.

    Article  Google Scholar 

  • Zhang, L., Zhao, J., & Xu, K. (2016). Who creates trends in online social media: The crowd or opinion leaders? Journal of Computer-Mediated Communication, 21, 1–16.

    Article  Google Scholar 

  • Zhu, J.-H. (1992). Issue competition and attention distraction: A zero-sum theory of agenda-setting. Journalism Quarterly, 69, 825–836.

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Communication, San Diego State University, San Diego, CA, USA

    Brian H. Spitzberg

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  1. Department of Geography, San Diego State University, San Diego, CA, USA

    Atsushi Nara

  2. Department of Geography, San Diego State University, San Diego, CA, USA

    Ming-Hsiang Tsou

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Spitzberg, B. (2021). Theorizing Social Media: A Formalization of the Multilevel Model of Meme Diffusion 2.0 (M3D2.0). In: Nara, A., Tsou, MH. (eds) Empowering Human Dynamics Research with Social Media and Geospatial Data Analytics. Human Dynamics in Smart Cities. Springer, Cham. https://doi.org/10.1007/978-3-030-83010-6_2

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  • DOI: https://doi.org/10.1007/978-3-030-83010-6_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83009-0

  • Online ISBN: 978-3-030-83010-6

  • eBook Packages: Social SciencesSocial Sciences (R0)

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