Attention and amplification in the hybrid media system: The composition and activity of Donald Trump’s Twitter following during the 2016 presidential election

Abstract

Building on studies of the hybrid media system and attention economy, we develop the concept of amplification to explore how the activities of social media–based publics may enlarge the attention paid to a given person or message. We apply the concept to the 2016 US election, asking who constituted Donald Trump’s enormous Twitter following and how that following contributed to his success at attracting attention, including from the mainstream press. Using spectral clustering based on social network similarity, we identify key publics that constituted Trump’s Twitter following and demonstrate how particular publics amplified his social media presence in different ways. Our discussion raises questions about how algorithms “read” metrics to guide content on social media platforms, how journalists draw on social media metrics in their determinations of news value and worthiness, and how the process of amplification relates to possibilities of citizen action through digital communication.

Keywords

Amplification attention engagement algorithm metrics power social media Twitter

Get full access to this article

View all access options for this article.

References

Anderson

(2011) Between creative and quantified audiences: web metrics and changing patterns of newswork in local US newsrooms. Journalism 12(5): 550–566.

Andrews

Caren

(2010) Making the news: movement organizations, media attention, and the public agenda. American Sociological Review 75(6): 841–866.

Benkler

(2006) The Wealth of Networks: How Social Production Transforms Markets and Freedom. New Haven, CT: Yale University Press.

Bennett

Segerberg

(2012) The logic of connective action: digital media and the personalization of contentious politics. Information, Communication & Society 15(5): 739–768.

Bimber

Flanagin

Stohl

(2005) Reconceptualizing collective action in the contemporary media environment. Communication Theory 15(4): 365–388.

Bimber

Flanagin

Stohl

(2012) Collective action in organizations: Interaction and engagement in an era of technological change. New York, NY: Cambridge University Press.

Blumler

Kavanagh

(1999) The third age of political communication: influences and features. Political Communication 16(3): 209–230.

Bucher

(2012) Want to be on the top? Algorithmic power and the threat of invisibility on Facebook. New Media & Society 14(7): 1164–1180.

Castells

(2007) Communication, power and counter-power in the network society. International Journal of Communication 1(1): 238–266.

10.

Castells

(2013) Communication Power. Oxford: Oxford University Press.

11.

Chadwick

(2013) The Hybrid Media System: Politics and Power. Oxford: Oxford University Press.

12.

Confessore

Yourish

(2016) $2 billion worth of free media for Trump. The New York Times, 16 March. Available at: https://www.nytimes.com/2016/03/16/upshot/measuring-donald-trumps-mammoth-advantage-in-free-media.html?_r=0 (accessed 1 March 2017).

13.

Conway

Kenski

Wang

(2015) The rise of Twitter in the political campaign: searching for intermedia agenda-setting effects in the presidential primary. Journal of Computer-mediated Communication 20(4): 363–380.

14.

Davenport

Beck

(2001) The Attention Economy: Understanding the New Currency of Business. Boston, MA: Harvard Business Press.

15.

Dhillon

(2001) Co-clustering documents and words using bipartite spectral graph partitioning. In. Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, 26–29 August. pp. 269–274. New York: ACM.

16.

Eilperin

(2016) White House women want to be in the room where it happens. Washington Post, 13 September. Available at: https://www.washingtonpost.com/news/powerpost/wp/2016/09/13/white-house-women-are-now-in-the-room-where-it-happens/?utm_term=.b58db045ff96 (accessed 4 November 2017).

17.

Flanagin

Stohl

Bimber

(2006) Modeling the structure of collective action. Communication Monographs 73(1): 29–54.

18.

Freelon

McIlwain

Clark

(2018) Quantifying the power and consequences of social media protest. New Media & Society 20: 990–1011.

19.

(2012) Selecting online videos from graphics, text, and view counts: the moderation of popularity bandwagons. Journal of Computer-mediated Communication 18(1): 46–61.

20.

Gamson

Wolfsfeld

(1993) Movements and media as interacting systems. Annals of the American Academy of Political and Social Science 528: 114–125.

21.

Gans

(1979) Deciding What’s News: A Study of CBS Evening News, NBC Nightly News, Newsweek, and Times. New York: Vintage Books.

22.

Giddens

(1984) The Constitution of Society: Outline of the Theory of Structuration, vol. 349. Berkeley, CA: University of California Press.

23.

Gillespie

(2011) Can an algorithm be wrong? Twitter trends, the specter of censorship, and our faith in the algorithms around us. Culture Digitally. Available at: http://culturedigitally.org/2011/10/can-an-algorithm-be-wrong/

24.

Gitlin

(1980) The Whole World Is Watching: Mass Media in the Making & Unmaking of the New Left. Berkeley, CA: University of California Press.

25.

Goldhaber

(1997) The attention economy and the net. First Monday 2(4–7). Available at: http://journals.uic.edu/ojs/index.php/fm/article/view/519/440 (accessed 1 March 2017).

26.

Graham

(2016) Inter-ideological mingling: white extremist ideology entering the mainstream on Twitter. Sociological Spectrum 36(1): 24–36.

27.

Guggenheim

Jang

Bae

et al . (2015) The dynamics of issue frame competition in traditional and social media. The ANNALS of the American Academy of Political and Social Science 659(1): 207–224.

28.

Hallinan

Striphas

(2016) Recommended for you: the Netflix Prize and the production of algorithmic culture. New Media & Society 18(1): 117–137.

29.

Hindman

(2008) The Myth of Digital Democracy. Princeton, NJ: Princeton University Press.

30.

Jenkins

(2006) Convergence Culture: Where Old and New Media Collide. New York: New York University Press.

31.

Karpf

(2016a) Analytic Activism: Digital Listening and the New Political Strategy. Oxford: Oxford University Press.

32.

Karpf

(2016b) The Cickbait candidate. The Chronicle of Higher Education, 19 June. Available at: http://www.chronicle.com/article/The-Clickbait-Candidate/236815 (accessed 1 March 2017).

33.

Klinger

Svensson

(2016) Network media logic: some conceptual considerations. In: Bruns

Enli

Skogerbo

et al . (eds) The Routledge Companion to Social Media and Politics. New York: Routledge, pp. 23–38.

34.

Kollanyi

Howard

Woolley

(2016) Bots and automation over Twitter during the first US Presidential debate. Comprop Data Memo. Available at: http://comprop.oii.ox.ac.uk/wp-content/uploads/sites/89/2016/11/Data-Memo-US-Election.pdf

35.

Lanham

(2006) The Economics of Attention: Style and Substance in the Age of Information. Chicago, IL: University of Chicago Press.

36.

Latour

(2010) Tarde’s idea of quantification. In: Candea

(eds) The Social after Gabriel Tarde: Debates and Assessments. London. Routledge, pp. 145–162.

37.

Lecheler

Kruikemeier

(2016) Re-evaluating journalistic routines in a digital age: a review of research on the use of online sources. New Media & Society 18(1): 156–171.

38.

Marwick

(2013) Status Update: Celebrity, Publicity, and Branding in the Social Media Age. New Haven, CT: Yale University Press.

39.

Messing

Westwood

(2014) Selective exposure in the age of social media: endorsements trump partisan source affiliation when selecting news online. Communication Research 41(8): 1042–1063.

40.

Metzger

Flanagin

Medders

(2010) Social and heuristic approaches to credibility evaluation online. Journal of Communication 60(3): 413–439.

41.

Morozov

(2009) The brave new world of slacktivism. Foreign Policy, 19 May. Available at: http://foreignpolicy.com/

42.

Musgrave

(2017) Trump nation’s secret Twitter weapon. Politico, 9 August. Available at: http://politi.co/2viAxZA (accessed 9 August 2017).

43.

Neuman

Guggenheim

Mo Jang

et al . (2014) The dynamics of public attention: agenda-setting theory meets big data. Journal of Communication 64(2): 193–214.

44.

Jordan

Weiss

(2002) On spectral clustering: analysis and an algorithm. In: Ng

Jordan

Weiss

(eds) Advances in Neural Information Processing Systems. Cambridge, MA: The MIT Press, pp: 849–856.

45.

Papacharissi

(2015) Affective Publics: Sentiment, Technology, and Politics. Oxford: Oxford University Press.

46.

Papacharissi

(2016) Affective publics and structures of storytelling: sentiment, events and mediality. Information, Communication & Society 19(3): 307–324.

47.

Parmelee

(2014) The agenda-building function of political tweets. New Media & Society 16(3): 434–450.

48.

Patterson

(2016) News coverage of the 2016 general election: how the press failed the voters. Shorenstein Center, 7 December. Available at: https://shorensteincenter.org/news-coverage-2016-general-election/ (accessed 1 March 2017).

49.

Pennacchiotti

Popescu

(2011) A machine learning approach to Twitter user classification. ICWSM 11(1): 281–288.

50.

Phillips

(2015) This Is Why We Can’t Have Nice Things: Mapping the Relationship between Online Trolling and Mainstream Culture. Cambridge, MA: The MIT Press.

51.

Qin

(2015) A Normalized and Regularized Nyström Extension for Clustering Network with General Degrees. Unpublished doctoral dissertation, University of Wisconsin Madison, Madison, WI.

52.

Snow

Rochford

Jr Worden

et al . (1986) Frame alignment processes, micromobilization, and movement participation. American Sociological Review 51(4): 464–481.

53.

Tilly

Wood

(2013) Social Movements 1768–2012. New York: Routledge.

54.

Tufekci

(2013) “Not this one”: social movements, the attention economy, and microcelebrity networked activism. American Behavioral Scientist 57(7): 848–870.

55.

Van Dijck

(2013) The Culture of Connectivity: A Critical History of Social Media. Oxford: Oxford University Press.

56.

Von Luxburg

(2007) A tutorial on spectral clustering. Statistics and Computing 17(4): 395–416.

57.

Wang

Rohe

(2016) Discussion of “co-authorship and citation networks for statisticians.” The Annals of Applied Statistics 10(4): 1820–1826.

58.

Webster

(2014) The Marketplace of Attention: How Audiences Take Shape in a Digital Age. Cambridge, MA: The MIT Press.

59.

Webster

Ksiazek

(2012) The dynamics of audience fragmentation: public attention in an age of digital media. Journal of Communication 62(1): 39–56.

60.

Wells

Shah

Pevehouse

et al . (2016) How Trump drove coverage to the nomination: hybrid media campaigning. Political Communication 33(4): 669–676.

61.

Luk

RWP

Wong

et al . (2008) Interpreting TF-IDF term weights as making relevance decisions. ACM Transactions on Information Systems (TOIS) 26(3): 13.

62.

(2016) The Attention Merchants: The Epic Scramble to Get inside Our Heads. New York: Knopf.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.17 MB