{"id":795,"date":"2026-02-05T17:27:49","date_gmt":"2026-02-06T01:27:49","guid":{"rendered":"https:\/\/dornsife.usc.edu\/scribe\/?p=795"},"modified":"2026-02-05T17:28:53","modified_gmt":"2026-02-06T01:28:53","slug":"performing-for-the-algorithm-how-political-communication-strategies-are-evolving-in-response-to-algorithmic-incentives","status":"publish","type":"post","link":"https:\/\/dornsife.usc.edu\/scribe\/2026\/02\/05\/performing-for-the-algorithm-how-political-communication-strategies-are-evolving-in-response-to-algorithmic-incentives\/","title":{"rendered":"Performing for the Algorithm: How Political Communication Strategies are Evolving in Response to Algorithmic Incentives"},"content":{"rendered":"\n\n\n\n\n \n \n\n\n\n\n\n\n\n
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Performing for the Algorithm: How Political Communication Strategies are Evolving in Response to Algorithmic Incentives<\/h1>\n\n\n<\/div>\n \n \n By<\/span>Zayanna Serrano<\/a><\/strong>\n \n February 5, 2026<\/span>\n <\/div>\n<\/div>\n\n\n <\/div><\/div>\n\n \n \n\n\n\n\n\n\n\n
\n\n \n
\n \n \n Share\n <\/span>\n \n \n <\/svg>\n <\/span>\n Copy Link<\/span>\n <\/a>\n \n \n <\/svg>\n <\/span>\n Facebook<\/span>\n <\/a>\n \n \n \n\n<\/svg>\n\n <\/span>\n X<\/span>\n <\/a>\n \n \n <\/svg>\n <\/span>\n LinkedIn<\/span>\n <\/a>\n \n \n \n \n <\/svg>\n <\/span>\n Bluesky<\/span>\n <\/a>\n \n \n <\/svg>\n <\/span>\n Email<\/span>\n <\/a>\n <\/span>\n <\/div>\n\n <\/div><\/div>\n \n\n\n\n \n \n\n\n\n\n\n\n\n
\n\n \n \n
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Algorithms have become central to the modern digital media landscape, shaping and
\nfiltering which political messages gain visibility. Social media platforms such as Twitter\/X,
\nInstagram, TikTok, and Facebook have engagement based algorithms that favor content that
\nengage emotions, controversy, and attention. For many politicians, this means that traditional
\ncommunication strategies such as op-eds or political ads are no longer sufficient to reach a wider
\naudience. As a result, many communication teams tend to focus their attention on making
\npolitical content that is visible, viral, and optimized for the algorithm. Both governance and
\ncampaigning alike are increasingly shaped by what the algorithm rewards.
\nThis paper examines how U.S. politicians are adapting their communication strategies on
\nsocial media in response to algorithmic incentives, focusing on the relationship between
\nengagement metrics and political motives. Through several case studies, the paper explores how
\npolitical figures have learned to perform within, and occasionally resist, the algorithmic
\nconditions of social media. From prominent politicians such as Donald Trump, Marjorie Taylor
\nGreene, Gavin Newsom, and Zohran Mamdani, each represents a different strategy for
\nnavigating algorithmic visibility from outrage based engagement to digital branding and
\nmovement building.
\nThe analysis will pay close attention to how these figures employ language and use
\nplatform specific features such as short form video and memes to maximize reach. The paper
\nwill include insights from my campaign strategy internship, which has provided first-hand
\nexposure to the decision-making processes behind political messaging. By integrating scholarly theory with practical observation to answer the central question: How has political
\ncommunication strategies evolved in response to algorithmic incentives?<\/p>\n

I. The Logic of the Algorithm
\nAlgorithms on major platforms such as Twitter\/X, Meta services (Facebook, Instagram,
\nThreads), and TikTok determine what users see based on engagement such as likes, shares,
\ncomments, even amount of viewing time as a signal of relevance to amplify similar content. This
\nfeedback loop has the capability to limit the exposure of other viewpoints to the user and instead
\nfavor what the user would consider their \u201cin-group\u201d. The constant regurgitation of the same
\nnarrative leads to the reinforcement of the shared viewpoint within the \u201cin-group\u201d leading to
\nwhat is known as an echo chamber (Cinelli, 2021). In order to determine if the echochamber
\neffect is a byproduct of social media or the internet, a study from the University of Oslo was
\nconducted. Researchers used Reddit, an online forum, and Facebook, a social media platform, to
\ndetermine the amount of homophily in interaction networks can be attributed to. It was found
\nthat users interacting with controversial topics (ex: abortion, gun control) demonstrated receiving
\nhomophilic content due to the algorithm used in social networks whereas an online forum like
\nReddit has as an opt-in format to the content users are viewing (Cinelli, 2021). This insight
\nallows for the understanding of how social media plays a vital role in information consumption
\nand political engagement.<\/p>\n

II. Case Studies: Algorithmic Adaptation in Action
\nA. Attention-Seeking Populism
\nThe digital age has created a new form of populism that can be seen through politicians
\non social media. According to Professor Maly from Tilburg University, who introduces the term
\n\u201calgorithmic populism\u201d, explaining how politicians intentionally design content to appeal to
\nalgorithms\u2019 preference for emotional engagement (likes, comments, views) to produce \u2018popular
\nposts\u2019 that trend, so a larger audience can view their online messages. The more \u2018popular\u2019 this
\npolitician becomes online, the more they are succeeding in their mission. However, in order to
\nappease the algorithm, this may mean outlandish posts for the sake of engagement (Maly, 2018).
\nTwo profound examples of this phenomenon include: Donald Trump, current U.S. President, and
\nMajorie Taylor Greene (MTG), U.S. Representative. Donald
\nTrump\u2019s prolific use of Twitter\/X and his later reliance on
\nTruth Social (his own social media platform) demonstrate
\nhow a politician can weaponize algorithms to dominate
\ndiscourse. His posts often blend policy announcements with
\ninflammatory commentary, ensuring continuous algorithmic
\namplification through outrage and reaction. As demonstrated
\nin Figure 1, Donald Trump posted a tweet in 2020 referring to
\nthe Coronavirus as the \u201cChina Virus\u201d and describing himself
\nas the most patriotic person due to his usage of face masks
\nwhen he had denied the usefulness of facial coverings for months, ignoring public health
\nofficials. Messages as such drive engagement due to the emotional and controversial drive.<\/p>\n

On a similar note, Marjorie Taylor Greene\u2019s approach leverages provocation and cultural
\nconflict to maintain constant online visibility. During the
\ncoronavirus pandemic, one of Greene\u2019s most popular
\ntweets included \u201cNO LOCKDOWNS NO
\nMANDATORY VACCINES! #RESIST\u201d. Although this
\nreflected many constituents’ sentiment on the protocol
\nfor a global pandemic, as a leader, it was not the most
\nappropriate tweet to put out considering the importance
\nof following public health recommendations for
\nlockdowns and vaccines. In Figure 2, the highest
\naverage number of retweets Greene received included
\ninflammatory commentary. However, these popular
\ntweets drive engagement. In the era of social media
\nengagement, populist politicians will use algorithms to
\nbypass traditional media filters and transform
\ncontroversy into attention currency.<\/p>\n

B. Digital Branding and Image Management
\nOn the other side of the same coin resides Gavin Newsom, California\u2019s Governor.
\nNewsom\u2019s content balances authenticity with strategic curation illustrating how politicians can
\nuse algorithmic systems for image cultivation rather than outrage.<\/p>\n

From my meetings with his communication team, it was learned that
\nhis team uses Instagram, X, and TikTok to circulate content that
\nmerges policy promotion with humor, memes, and cinematic visuals.
\nThis strategy appeals to younger demographics while crafting an
\nonline persona that feels both authoritative and relatable. In Figure 3,
\nGovernor Newsom is pinning President Trump in a wrestling
\nposition. It was my study fellow, Jim Deboo, that came up with the
\nstoryboard for this meme that the communication team then put out
\non the newer popular social media platform, TikTok. It is a humorous
\nway of demonstrating Newsom fighting Trump. Another example of
\nhow Newsom uses social meda in image cultivation is seen in Figure
\n4. In this tweet on TikTok, this method is used to get more
\nengagement from an audience
\nthat may not be on Twitter,
\nNewsom uses a similar capitalized and patriotic tone that
\nTrump is known for. Tweeting like Trump, allows Newsom\u2019s
\ncommunication team to develop a mocking tone and allow
\nNewsom to have a \u201cheroic\u201d persona against the current
\nadministration. Although it is humorous to mock the current
\noutlandish social media engagement politicians put out, it still
\nfeeds the algorithm the non-traditional communication that is
\nattributed to \u201calgorithmic populism\u201dfor the sake of
\nengagement and popularity (Maly, 2018).<\/p>\n

C. Grassroots and Movement Building
\nWhile Trump and Newsom highlight top-down use of algorithms, Zohran Mamdani
\nillustrates bottom-up mobilization during his campaigns in New York. Zohran Mamdani went
\nfrom being at a 1% probability of winning the
\nNYC mayoral race to being the first Muslim
\nmayor of NYC. Instead of mocking Trump\u2019s
\ndigital rhetoric and campaign image, Mamdani
\nused symbols and colors that represented the
\nregion he would
\ngovern, New York
\nCity. This allowed for nostalgia and a sense of trustworthiness
\namongst constituents as Mamdani left less polarizing and more of
\na candidate that understood and reflected the needs of New York
\nCity. In terms of social media usage, Mamdani uses short form
\nmedia to turn complex policy ideas into digestible, viral content
\naimed at young progressives and older generations. His content is
\nfilmed more like a documentary piece rather than a meme. In
\nFigure 6, a shot from a small clip shows the quality of the content
\nrather than a memeification of political engagement on social
\nmedia.
\nThis case shows how algorithms can also enable
\nparticipatory and issue-based politics, expanding civic
\nengagement even within a profit-driven media environment.<\/p>\n

III. Democratic Consequences and the Future of Political Strategy
\nThe paradigm shift of political communication norms has moved significantly with the
\nintroduction to the algorithmic system on social media. Algorithms, while democratizing access,
\ncan also encourage polarization, the rapid spread of misinformation, and a weakened factual
\nfoundation that is fundamental to democracies (Benkler, 2018; Cinelli, 2021). This has shifted
\nnot only political thought and behavior, but also political strategy.
\nConfronted with an emerging algorithmic environment, politicians and staffers alike have
\nto strategically think about their platform\u2019s incentives on social media and what bases they want
\nto reach. Political campaigns can exacerbate this issue by opportunistically adhering to
\nalgorithmic incentives. In order to be seen, campaigns increasingly use outrage driven messages
\nsuch as Donald Trump or even in mocking tone, like Gavin Newsom. Microtargeting and
\npersonalized messages can further erode transparency and accountability in campaign strategy
\ndue to misleading narratives to certain audiences (Borgesius et al., 2018). In doing so, political
\nstrategy does not simply reinforce social media dynamics but instead fosters polarization.
\nHowever, at the same time, political strategy can also be a mitigating factor in social
\nmedia\u2019s democratic harms when deployed with the right intention. Some campaigns used social
\nmedia to create more transparency by explaining policy through a more accessible format as seen
\nin Zohran Mamdani\u2019s campaign. Movement based strategies, particularly used on social media,
\ncan amplify marginalized voices and encourage participatory engagement over and against
\npolitical spectacle.<\/p>\n

IIII. Conclusion
\nSocial media has introduced structural incentives that at once challenge and reshape
\ndemocratic politics. In this reshape, political strategy plays a decisive role in determining which
\npath politicians and their communication teams take. While an engagement driven model of
\ncampaigning can exacerbate polarization and diminish thoughtful discourse, strategic choices
\ncan also place an emphasis on accountability and inclusion to reinforce democratic practices.
\nThe remaining question is: will political strategy be able to keep changing in a way that
\nfocuses on direct engagement and democratic accountability or will political incentives further
\ncement a politics of spectacle on social media? The future of political communication depends
\non not only how politicians adapt but also on whether platforms develop additional mechanisms
\nfor transparency and public discourse and if users continue to believe everything they see online.<\/p>\n

Works Cited<\/p>\n

Benkler, Yochai, and Robert Faris. \u201cNetwork Propaganda: Manipulation, Disinformation, and
\nRadicalization in American Politics.\u201d Oxford Academic, 2018,
\nhttps:\/\/academic.oup.com\/book\/26406. Accessed 11 December 2025.
\nBorgesius, Frederik, and Judith M\u00f6ller. \u201cOnline Political Microtargeting: Promises and Threats
\nfor Democracy.\u201d Utrecht Law Review, 9 February 2018,
\nhttps:\/\/utrechtlawreview.org\/articles\/10.18352\/ulr.420. Accessed 12 December 2025.
\nCinelli, Matteo. \u201cThe echo chamber effect on social media.\u201d Proceedings of the National
\nAcademy of Sciences, vol. 118, no. 9, 2021, pp. 1-30. PNAS,
\nhttps:\/\/www.pnas.org\/doi\/full\/10.1073\/pnas.2023301118.
\nColes, Amy. \u201cUS election: Donald Trump’s 45 most controversial tweets.\u201d Sky News, 12 October
\n2020,
\nhttps:\/\/news.sky.com\/story\/us-election-donald-trumps-45-most-controversial-tweets-1209
\n8204. Accessed 12 December 2025.
\nEnlow, Madeline. \u201cSentiment trends in political tweets: a case study of Marjorie Taylor Greene.\u201d
\nMississippi State University, 2022,
\nhttps:\/\/scholarsjunction.msstate.edu\/cgi\/viewcontent.cgi?article=1106&context=honorsth
\neses. Accessed 11 December 2025.
\n\u201cGavin Newsom (@gavinnewsom).\u201d TikTok, https:\/\/www.tiktok.com\/@gavinnewsom?lang=en.
\nAccessed 12 December 2025.
\nMaly, Ico. \u201cAlgorithmic populism and algorithmic activism | diggit magazine.\u201d Diggit Magazine,
\n10 October 2018, https:\/\/www.diggitmagazine.com\/articles\/algorithmic-populism-activism. Accessed 12
\nDecember 2025.
\nMaly, Ico. \u201cAlgorithmic Populism and the Logic of Virality.\u201d Convergence: The International
\nJournal of Research into New Media Technologies, vol. 26, no. 5\u20136, 2020, pp.
\n1185\u20131200.
\n\u201cZohran Mamdani Will Be The Next Mayor Of New York, And Here’s Why Every Graphic
\nDesigner Should Take Notes From His Masterclass In Political Branding.\u201d Digital
\nSynopsis, https:\/\/digitalsynopsis.com\/design\/zohran-mamdani-campaign-branding\/.
\nAccessed 12 December 2025.
\n\u201cZohran Mamdani (@zohran_k_mamdani).\u201d TikTok,
\nhttps:\/\/www.tiktok.com\/@zohran_k_mamdani?lang=en. Accessed 12 December 2025.<\/p>\n\n\n\n<\/div>\n\n\n <\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1036,"featured_media":644,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[13,26],"tags":[],"class_list":["post-795","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-editor-blogs","category-policy-and-technology"],"acf":[],"yoast_head":"\nPerforming for the Algorithm: How Political Communication Strategies are Evolving in Response to Algorithmic Incentives<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dornsife.usc.edu\/scribe\/2026\/02\/05\/performing-for-the-algorithm-how-political-communication-strategies-are-evolving-in-response-to-algorithmic-incentives\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Performing for the Algorithm: How Political Communication Strategies are Evolving in Response to Algorithmic Incentives - 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