University of Kwazulu-Natal

The Threat Of Algorithmic Populism Intelligence Strategies For

Bonisiwe Shabane
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the threat of algorithmic populism intelligence strategies for

This entry argues that populist governments generate disruptive narratives through two distinct methods: (1) knowledge and manipulation of the algorithmic parameters and (2) manipulation and amplification of propaganda content. This entry demonstrates that recommender systems of the online platforms enable the power to microtarget sensitive communities and to generate and amplify misinformation and propaganda. This is made possible through personalization, where content and users are formed into clusters on the basis of the history and context of the users and the content. Since data protection bills vary vastly across countries, unequal and often inadequate protection is available across geographical regions. Populist leadership can use technological tools such as spyware or aides to create strong narratives. Examples include the following: This is your Digital Life app used by Cambridge Analytica for Brexit propaganda or Pegasus spyware, used in the Indian subcontinent.

The access to parameters varies from simplistic features of specific clusters such as age group, gender to political affiliations, individual interest in events, or industrial affiliations. Many of the parameters remain unrevealed by the platforms because of a lack of regulation or strong demand. This form of manipulation and control threatens the ideals of a global democratic framework, expected from the online public sphere, such as autonomy, freedom of speech, and plurality. The control and relationship shared by the corporate and governmental stakeholders pose a challenge to the individual capacity of users to make rational-critical discourse. Constant regulation of parameters, audits to check power, and campaigns to increase technological awareness are an urgent need. This is a preview of subscription content, log in via an institution to check access.

Tax calculation will be finalised at checkout Airoldi M, Beraldo D, Gandini A (2016) Follow the algorithm: an exploratory investigation of Music on YouTube. Poetics 57:1–13. https://doi.org/10.1016/j.poetic.2016.05.001 LLMs can be exploited to produce harmful or toxic outputs used for adversarial attacks, or be prompted to generate content that is seemingly benign but contains subtle manipulations intended to deceive or cause harm... LLMs can generate discriminatory biases within their outputs, which could inadvertently lead to biased cybersecurity practices, where such biases could manifest in security tools that rely on LLMs, potentially leading to unequal protection measures...

To navigate these threats, the academic community has advocated for a human-centric approach to LLM governance in cybersecurity, which includes the development of frameworks that prioritize transparency, human oversight, and continuous evaluation of LLM... The digital age has facilitated unprecedented transformations in communication, reshaping the way political discourse and influence manifest in democratic societies. Among these transformations, algorithmic populism emerges as a significant phenomenon. This concept, defined by the interaction between political actors, online activism, and algorithm-driven amplification, highlights how extremist or populist political messages gain traction in the digital ecosystem. By analysing this dynamic through the lens of intelligence studies, we have tried to identify the risks posed by algorithmic populism to democratic systems and propose strategies to mitigate its impact. The need for intelligence agencies to address these risks is clear, as they represent not only political but also existential threats to the democratic order.

Research output: Contribution to journal › Article › Professional Research output: Contribution to journal › Article › Professional T1 - Algorithmic populism and algorithmic activism N2 - Digitalization gave birth to a new form of populism: algorithmic populism. If we want to understand contemporary populism, we should understand it in its social, political, economic and technological context. Algorithmic populism cannot be understood without taking the uptake and algorithmic activism in particular into account.

AB - Digitalization gave birth to a new form of populism: algorithmic populism. If we want to understand contemporary populism, we should understand it in its social, political, economic and technological context. Algorithmic populism cannot be understood without taking the uptake and algorithmic activism in particular into account. The political arena is no longer a square or a chamber in decent modern democracies. It lives in the binary schemes of social media platforms, in the immeasurable recommendation engines in the messaging applications that give recommendations of content that are directed to each user. Algorithms are weapons and a battlefield when they influence what we see, hear, and believe.

This is what constitutes the phenomenon of the algorithm-driven populism, which is, in a certain way, more insidious than the classical populism, as it continues to form the worldviews before the very conversation even... Tracing three trajectories is needed so as to anchor this argument. To begin with, digital populism uses the loss of trust in institutions. Two platforms privilege polarizing material and strengthen ideology compared to inquisitiveness. Third, democracies react belatedly but at other times, sluggishly or even too violently. The issue here is the ability to attain the preservation of pluralism without necessarily forfeiting the legitimacy

Digital populism is not created in a vacuum. According to social and political scientists, its roots can be traced to lack of confidence in the democratic institutions by the people. When voters lose trust in either the courts, media, or elections, a gap forms, and populists fill it easily. A broad meta-analysis of the political effects of digital media found increasing polarization as well as declining confidence in democratic institutions, particularly in well-established democracies. The expression populism becomes prevalent in the political language when encapsulating power as corrupt or alien. Such a plan was evident in the United States before Jan.

6, 2021, when courts and election boards were played as partisan watchdogs. In Brazil, it was different with a polarised Supreme Federal Court that intended to prevent a populist surge and then was itself accused of judicial overreach. In Hungary, the slide has been less dramatic but more lasting-Orbán has packed the courts, restructured media ownership, and suppressed democratic contest. Such developments have reflected the slow downfall in trust, a crisis that moves faster than headlines Algorithms help to advance populism even further as soon as trust is lost. Platforms are configured to attract rather than to engage in balanced discussion.

The proliferation in social media systems is based on engagement, meaning that sensational or extreme posts have visibility. These mechanics are well known theoretically. One very large randomized experiment revealed that the way Twitter presented tweets in its timeline favored right-leaning political messages over more moderate messages-which is a taste of how platform logic can be a distortion... Over the past decade, social media has become deeply entwined with American political discourse. Platforms like Facebook, Twitter (now X), YouTube, and TikTok use opaque algorithms to determine what content users see, with profound effects on civic engagement and opinion formation. In the early days of social networking, many were optimistic – “by connecting people and giving them a voice, social media had become a global force for plurality, democracy and progress,” as The Economist...

| Impact). However, as social media’s influence grew, concerns mounted that these platforms were “hijacking democracy” by amplifying extreme voices, disinformation, and populist rhetoric ( Social Media Effects: Hijacking Democracy and Civility in Civic Engagement –... Today, scholars and experts are examining how algorithm-driven feeds may contribute to political polarization and the rise of populism in the United States. This report analyzes: Engagement-Driven Algorithms: Most social media platforms use recommendation algorithms designed to maximize user engagement (likes, shares, view time). These algorithms learn from each user’s behavior and prioritize content likely to keep them hooked.

While this personalization can make feeds more relevant, it also tends to favor provocative or emotionally charged material that generates stronger reactions. Researchers note that “social media technology employs popularity-based algorithms that tailor content to maximize user engagement,” and that maximizing engagement “increases polarization, especially within networks of like-minded users” (How tech platforms fuel U.S. political polarization and what government can do about it). In other words, the more a post incites outrage or passion, the more the algorithms will spread it, potentially skewing the political discourse toward extremes. Facebook: On Facebook, the News Feed algorithm ranks posts based on metrics like comments, shares, and reactions. Internal studies at Facebook found that this system “exploit[s] the human brain’s attraction to divisiveness” (Facebook Knew Its Algorithms Divided Users, Execs Killed Fixes: Report – Business Insider).

In fact, a leaked 2016 Facebook report concluded that “64% of all extremist group joins are due to our recommendation tools”, notably the “Groups You Should Join” and “Discover” algorithms that suggested communities to... This means the platform’s own automated suggestions were steering a majority of users who joined extremist or hyper-partisan groups, dramatically widening those groups’ reach. Facebook’s algorithm changes have also been linked to heightened partisan content. For example, in 2018 Facebook adjusted its feed to emphasize “meaningful social interactions,” but this inadvertently boosted posts that sparked argument and anger—leading to more divisive political content appearing in people’s feeds (Facebook CEO... Although top Facebook executives have publicly downplayed the platform’s role (“some people say… social networks are polarizing us, but that’s not at all clear from the evidence,” CEO Mark Zuckerberg argued (How tech platforms... political polarization and what government can do about it)), the company’s own documents and actions suggest otherwise.

Facebook has occasionally tweaked its algorithms to suppress incendiary posts – such as during the tense period right after the 2020 U.S. election – acknowledging that its automated ranking can fuel extremism (How tech platforms fuel U.S. political polarization and what government can do about it). However, these interventions tend to be temporary, since permanently tamping down divisive content would reduce user engagement (How tech platforms fuel U.S. political polarization and what government can do about it), and thus advertising revenue. Twitter (X): Twitter initially showed users an unfiltered chronological timeline, but it introduced an algorithmic “Home” timeline and trending topic algorithms that highlight popular tweets.

These systems can accelerate the viral spread of polarizing hashtags or sensational political takes. Twitter’s own internal research in 2021 revealed a concerning bias: the algorithm was found to amplify tweets from right-wing politicians and news sources more than those from left-wing sources (Twitter admits bias in algorithm... In other words, Twitter admitted its recommendation system disproportionately boosted certain political content on the right. This kind of amplification can skew the platform’s discourse, making extreme or populist right-wing narratives more visible. (Notably, Twitter’s trending topics have often been dominated by partisan campaigns or outrage-fueled discussions, illustrating how the algorithm magnifies whatever draws engagement, for better or worse.) YouTube: YouTube’s recommendation algorithm is engineered to maximize watch time by suggesting videos a viewer is likely to click next.

In practice, critics have long accused YouTube of leading users down a “rabbit hole” of increasingly extreme content to keep them watching. For instance, a user who starts with an innocuous political video might be recommended slightly more provocative videos, and over time these can escalate to fringe conspiracy theories or hyper-partisan channels. A Northwestern University analysis noted that these algorithms can “amplify inherent human biases” and interfere with normal social learning by over-rewarding sensational content (Social-Media Algorithms Have Hijacked “Social Learning”). While recent studies offer a mixed picture (some find that already-polarized viewers drive the consumption of extremist videos more than casual viewers being radicalized by the algorithm (Study Finds Extremist YouTube Content Mainly Viewed... In 2019, the platform adjusted its algorithm to reduce recommendations of content that “comes close to” violating policies (e.g. conspiracy theories or disinformation) – a response to evidence that its automated suggestions were promoting such content.

Nevertheless, anecdotes of users being “radicalized” via YouTube abound, and the site has hosted influential populist firebrands who built large followings through algorithmic promotion. Academia.edu no longer supports Internet Explorer. To browse Academia.edu and the wider internet faster and more securely, please take a few seconds to upgrade your browser. 2021, Archives of Criminology [Archiwum Kryminologii] This paper introduces the concept of algorithm-driven populism, considering whether it has a consonant or a conflicting relation with liberal democracy. The overall argument is that social media platforms are not just new media used by populists; algorithms have co-constituted a new form of populism.

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