Performing for the Algorithm: How Political Communication Strategies are Evolving in Response to Algorithmic Incentives

Algorithms have become central to the modern digital media landscape, shaping and
filtering which political messages gain visibility. Social media platforms such as Twitter/X,
Instagram, TikTok, and Facebook have engagement based algorithms that favor content that
engage emotions, controversy, and attention. For many politicians, this means that traditional
communication strategies such as op-eds or political ads are no longer sufficient to reach a wider
audience. As a result, many communication teams tend to focus their attention on making
political content that is visible, viral, and optimized for the algorithm. Both governance and
campaigning alike are increasingly shaped by what the algorithm rewards.
This paper examines how U.S. politicians are adapting their communication strategies on
social media in response to algorithmic incentives, focusing on the relationship between
engagement metrics and political motives. Through several case studies, the paper explores how
political figures have learned to perform within, and occasionally resist, the algorithmic
conditions of social media. From prominent politicians such as Donald Trump, Marjorie Taylor
Greene, Gavin Newsom, and Zohran Mamdani, each represents a different strategy for
navigating algorithmic visibility from outrage based engagement to digital branding and
movement building.
The analysis will pay close attention to how these figures employ language and use
platform specific features such as short form video and memes to maximize reach. The paper
will include insights from my campaign strategy internship, which has provided first-hand
exposure to the decision-making processes behind political messaging. By integrating scholarly theory with practical observation to answer the central question: How has political
communication strategies evolved in response to algorithmic incentives?

I. The Logic of the Algorithm
Algorithms on major platforms such as Twitter/X, Meta services (Facebook, Instagram,
Threads), and TikTok determine what users see based on engagement such as likes, shares,
comments, even amount of viewing time as a signal of relevance to amplify similar content. This
feedback loop has the capability to limit the exposure of other viewpoints to the user and instead
favor what the user would consider their “in-group”. The constant regurgitation of the same
narrative leads to the reinforcement of the shared viewpoint within the “in-group” leading to
what is known as an echo chamber (Cinelli, 2021). In order to determine if the echochamber
effect is a byproduct of social media or the internet, a study from the University of Oslo was
conducted. Researchers used Reddit, an online forum, and Facebook, a social media platform, to
determine the amount of homophily in interaction networks can be attributed to. It was found
that users interacting with controversial topics (ex: abortion, gun control) demonstrated receiving
homophilic content due to the algorithm used in social networks whereas an online forum like
Reddit has as an opt-in format to the content users are viewing (Cinelli, 2021). This insight
allows for the understanding of how social media plays a vital role in information consumption
and political engagement.

II. Case Studies: Algorithmic Adaptation in Action
A. Attention-Seeking Populism
The digital age has created a new form of populism that can be seen through politicians
on social media. According to Professor Maly from Tilburg University, who introduces the term
“algorithmic populism”, explaining how politicians intentionally design content to appeal to
algorithms’ preference for emotional engagement (likes, comments, views) to produce ‘popular
posts’ that trend, so a larger audience can view their online messages. The more ‘popular’ this
politician becomes online, the more they are succeeding in their mission. However, in order to
appease the algorithm, this may mean outlandish posts for the sake of engagement (Maly, 2018).
Two profound examples of this phenomenon include: Donald Trump, current U.S. President, and
Majorie Taylor Greene (MTG), U.S. Representative. Donald
Trump’s prolific use of Twitter/X and his later reliance on
Truth Social (his own social media platform) demonstrate
how a politician can weaponize algorithms to dominate
discourse. His posts often blend policy announcements with
inflammatory commentary, ensuring continuous algorithmic
amplification through outrage and reaction. As demonstrated
in Figure 1, Donald Trump posted a tweet in 2020 referring to
the Coronavirus as the “China Virus” and describing himself
as the most patriotic person due to his usage of face masks
when he had denied the usefulness of facial coverings for months, ignoring public health
officials. Messages as such drive engagement due to the emotional and controversial drive.

On a similar note, Marjorie Taylor Greene’s approach leverages provocation and cultural
conflict to maintain constant online visibility. During the
coronavirus pandemic, one of Greene’s most popular
tweets included “NO LOCKDOWNS NO
MANDATORY VACCINES! #RESIST”. Although this
reflected many constituents’ sentiment on the protocol
for a global pandemic, as a leader, it was not the most
appropriate tweet to put out considering the importance
of following public health recommendations for
lockdowns and vaccines. In Figure 2, the highest
average number of retweets Greene received included
inflammatory commentary. However, these popular
tweets drive engagement. In the era of social media
engagement, populist politicians will use algorithms to
bypass traditional media filters and transform
controversy into attention currency.

B. Digital Branding and Image Management
On the other side of the same coin resides Gavin Newsom, California’s Governor.
Newsom’s content balances authenticity with strategic curation illustrating how politicians can
use algorithmic systems for image cultivation rather than outrage.

From my meetings with his communication team, it was learned that
his team uses Instagram, X, and TikTok to circulate content that
merges policy promotion with humor, memes, and cinematic visuals.
This strategy appeals to younger demographics while crafting an
online persona that feels both authoritative and relatable. In Figure 3,
Governor Newsom is pinning President Trump in a wrestling
position. It was my study fellow, Jim Deboo, that came up with the
storyboard for this meme that the communication team then put out
on the newer popular social media platform, TikTok. It is a humorous
way of demonstrating Newsom fighting Trump. Another example of
how Newsom uses social meda in image cultivation is seen in Figure
4. In this tweet on TikTok, this method is used to get more
engagement from an audience
that may not be on Twitter,
Newsom uses a similar capitalized and patriotic tone that
Trump is known for. Tweeting like Trump, allows Newsom’s
communication team to develop a mocking tone and allow
Newsom to have a “heroic” persona against the current
administration. Although it is humorous to mock the current
outlandish social media engagement politicians put out, it still
feeds the algorithm the non-traditional communication that is
attributed to “algorithmic populism”for the sake of
engagement and popularity (Maly, 2018).

C. Grassroots and Movement Building
While Trump and Newsom highlight top-down use of algorithms, Zohran Mamdani
illustrates bottom-up mobilization during his campaigns in New York. Zohran Mamdani went
from being at a 1% probability of winning the
NYC mayoral race to being the first Muslim
mayor of NYC. Instead of mocking Trump’s
digital rhetoric and campaign image, Mamdani
used symbols and colors that represented the
region he would
govern, New York
City. This allowed for nostalgia and a sense of trustworthiness
amongst constituents as Mamdani left less polarizing and more of
a candidate that understood and reflected the needs of New York
City. In terms of social media usage, Mamdani uses short form
media to turn complex policy ideas into digestible, viral content
aimed at young progressives and older generations. His content is
filmed more like a documentary piece rather than a meme. In
Figure 6, a shot from a small clip shows the quality of the content
rather than a memeification of political engagement on social
media.
This case shows how algorithms can also enable
participatory and issue-based politics, expanding civic
engagement even within a profit-driven media environment.

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

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

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