Designed for Outrage: How Algorithms Profit from Hate

hate. A nationwide boycott of Jewish owned shops and businesses sees brown shirts man the blockade. They produced films to demonize Jews by comparing them to rats. Propaganda is Hitler. Hitler Hitler. and misinformation are not new. But the digital age has changed one thing, speed. Today, hate can spread with just a single tap. Messages that once traveled slowly through letters, newspapers, or word of mouth now reach millions instantly. The distance between sender and receiver is almost non-existent. It is as if time has conquered space. In his book, The Condition of Postmodernity, Marxist geographer David Harvey explains the idea of time space compression. He argues that capitalism has steadily reduced the barriers of distance and time, shrinking the world until it sometimes feels as if it's collapsing in on itself. Harvey says this doesn't just change how fast we move through the world, but how we perceive it. This means when everything happens in real time, our sense of space, distance, and even truth begins to blur. And in that blurring, in this compressed digital age, hate becomes infinitely scalable. And in India, that hate has found a rising target. Social media space that is teeming with hatred directed at Muslims. This video was basically a call to unleash genocide on Muslims to ethnically cleanse India's Muslim population. Over the past decade, hate speech against Muslims has exploded not just on the streets, but across our screens. From WhatsApp universities spreading conspiracies to AI generated Islamophobic images to apps designed to humiliate Muslim women to videos openly calling for genocide of Muslims circulating freely. Digital hate particularly against Indian Muslims has become a norm. To understand why hate spreads so easily online, we need to look inside the platform itself. Every second you spend online, do you ever wonder what happens behind the screen? You're scrolling through posts, videos, and memes. But what about the platforms? They're scrolling through you. Biggest companies in Silicon Valley have been in the business of selling their users. Advertisers pay for the products that we use. Advertisers are the customers. We're the thing being sold. The classic saying is if you're not paying for the product, then you are the product. Every hesitation, every click, every pause of yours is all recorded, analyzed, and fed back into a loop designed to keep you watching. But why do they want to keep you watching? These platforms began as spaces to connect people. I built the first version of Facebook because it's something that my friends and I wanted to use at Harvard, a directory and a way to connect with the other people around us and I could share what I was doing and maybe I could also see what other people were doing. But we don't pay for them yet they're worth billions. So how do they make money? The business model is advertising based. And when you have an advertising based business model, your objective is to get more people to pay attention to your product and to pay attention longer each and every day. So the longer you stay, the more ads you see and the more ad revenue you generate for them, the more valuable your attention becomes. This idea isn't new. In 1971, Herbert A. Simon, a nobel laureate in economic sciences, warned that a wealth of information creates a poverty of attention. He called it the attention economy. And here your scarce attention becomes a valuable commodity. Social media campaigns turn that insight into a business model. Because if attention is scarce, whoever can capture it owns the market, thus monetizing your attention. That thought process was all about how do we consume as much of your time and conscious attention as possible. It's a social validation feedback loop because you're exploiting a vulnerability in human psychology. If these platforms are designed to keep you hooked, what kind of content keeps you hooked the longest? Polarization has a home field advantage in terms of the business model. The natural function of these platforms is to reward conspiracy theories, outrage, what we call the race to the bottom of the brain stem. The algorithm doesn't understand politics or hate. It understands performance and in a world where polarization performs best, that's exactly what it learns to optimize. A huge new Pew Research Center study of 10,000 American adults finds us more divided than ever with personal and political polarization at a 20-year high. In 2018, a MIT study analyzing 126,000 stories tweeted by 3 million users over more than a decade found that false news is 70% more likely to spread faster and further and it reached 1,500 people six times faster than accurate news. But why does falsehood travel faster than fact? Is it just the users to blame? Meta ran an experiment once where they classified two groups of content. They had good for the world content, which was stuff they thought was not disinformation, was truthful, interesting and important, and bad for the world content, meaning misinformation, disinformation, harmful information, hate speech, whatever. And they discovered that people were more likely to spend time looking at bad for the world content. And so they had a choice: alter the algorithm so it shows less bad world content, or not, and they decided not to intervene too much in negating the bad of the world content. That single choice to let harmful content run captures the core of the problem. Our brains are wired with negativity bias, where negative events and information have a greater impact on a person's psychological state than positive or neutral events of the same intensity. This isn't because people are inherently evil. It's an evolutionary adaptation. Our ancestors who were more alert to threats and dangers were more likely to survive. So our brains evolved to prioritize negative information for safety and survival. Tech companies know this, and instead of protecting users from it, they design for it. The algorithm feeds on engagement. Since outrage drives engagement, the system learns to show more outrage. It is as if you're always in fight or flight mode. It's a feedback loop. Attention generates data. Data trains the algorithm and the algorithm optimizes for what gets the most reaction. So when Meta decided not to intervene, it wasn't neutrality. It was monetizing human vulnerability, putting profit over truth. In the 1920s, Edward Bernays, the father of modern PR, argued that if you understand human psychology, you can shape public opinion, not by force but by engineering desire and belief. He called this the engineering of consent. A century later, that principle operates on a far greater scale. Today, public opinion can be shaped for or against almost anything, not through persuasion, but through algorithmic amplification. Social media has become a powerful tool to engineer consent, not just to sell ideas, but to normalize hate, prejudice, and even violence. Decades later, Herman and Chomsky expanded Bernays's idea into what they called manufacturing consent. They argued that the media doesn't simply inform, it filters and frames information in ways that serve power and profit. But today, that system has evolved. Propaganda is no longer broadcast from the top. Social media has not only digitized propaganda, it has incentivized it. What used to require state machinery or mass media can now be done by anyone with a smartphone and an algorithm that rewards outrage. The platforms don't just monetize attention anymore. They've created an entire economy around hate as engagement. Creators, influencers, and troll networks all benefit from the same logic. The more divisive the content, the higher the reach, the better the payout. And those consequences aren't limited to what happens online, because when the same system that rewards engagement also rewards outrage, digital hate doesn't just stay on screens, it spills into the real world. Home Minister Amit Shah called Bengali Muslims termites. A Muslim man burnt alive in Rajasthan, all of it on video, all in the name of love jihad. Shocking remarks by a UP BJP MLA who asked for a boycott of Muslim vendors. When engagement becomes profit and profit aligns with power, these platforms stop being neutral spaces. They become instruments, and in politically charged environments that design has real world costs. Take Myanmar. Facebook's platform let extremist speech spread unchecked, fueling the 2017 Rohingya genocide. Peer-to-peer networks cast Rohingya Muslims as violent foreigners while glorifying the military. Posts dehumanizing them flooded the platform. The military ran fake pages to quietly spread hate and incendiary posts, including a rumor by monk Wirathu, directly inciting violence, killing and displacing thousands. Warnings from researchers about the platform amplifying hate were ignored by the platform itself. This had given propagandists unprecedented reach. Later, in 2018, Facebook admitted after years of denial that it had not done enough to help prevent its platform from being used to incite offline violence. What happened in Myanmar revealed a deeper trend. Platforms don't just host content. Their design decides which voices are amplified and which are sidelined, often in ways that side with dominant narrative, power, and profit. And that pattern is not limited to one country. In 2021, internal data from X showed it amplified right-wing content more than left-leaning sources. In 2023, Human Rights Watch accused Meta of suppressing pro-Palestinian content while allowing violent anti-Palestinian rhetoric to circulate freely. The bias here was then political and global. Despite this, by early 2025, both Meta and X had rolled back key hate speech and fact-checking rules, even after years of evidence linking viral hate to real world harm. Every digital ecosystem develops its own algorithmic rules shaped by local politics, profit incentives, and cultural context. Algorithms don't just reflect power. They act on it, deciding what content is amplified and what is shadowbanned. Over the past decade, India's digital sphere has become a lab where platform capitalism and Hindutva nationalism reinforce each other. A 2018 Observer Research Foundation study found a rise of 19 to 30% in incidents over the one-year time frame of the study focusing on digital hate. It revealed that most of the comments were targeting Muslims around cow protection, beef consumption, and interfaith marriage, often with calls for bodily harm or violence. In 2020, a Wall Street Journal investigation revealed Facebook's top India executive, Ankhi Das, shielded hate posts by BJP leader T. Raja Singh, who had called for Muslims to be shot. She suggested enforcing hate speech rules could hurt business prospects. After the Delhi pogrom, an inquiry found Facebook complicit in letting hate go unchecked. In 2024, India Hate Lab documented 1,165 offline hate events, a 74% jump from 2023. Almost 1,000 of them began as social media broadcasts. Facebook hosted nearly half of them. 98% stayed online. The peaks matched election months, showing how digital hate scales with political mobilization. Meanwhile, X blocked accounts of hate crime trackers like India Hate Lab and Hindutva Watch on Indian government orders, not because they spread hate but because they tracked it. The irony is clear: the algorithm that amplifies hate also silences those documenting it. At the same time, far-right figures like Yati Narsingh Anand, who once called for genocide at the Haridwar Dharma Sansad, continue to thrive online. His speeches circulate through WhatsApp groups and YouTube clips, turning hate into both ideology and revenue. So here, when the state benefits from communal polarization and corporations benefit from engagement, hate becomes a shared enterprise, a two-way profit, one political, one financial. India today stands at the center of a disturbing global pattern. In 2022, reports found that India topped the world in online anti-Muslim hate posts and saw the third largest spike in Islamophobic tweets. What that means is not just a statistic. It's a signal that the world's largest democracy has become a digital factory of hate, where communal propaganda is produced, scaled, and exported through algorithms engineered for outrage. shocking calls for economic boycott of Muslims. But this didn't happen by accident. It's the result of a system where political power, corporate profit, and digital design align. When hate drives engagement and engagement drives revenue, hate becomes infrastructure, built into the code, rewarded by the market, and weaponized by politics. And this is why it matters, because the algorithm that rewards hate doesn't just shape what we see online. It reshapes democracy itself. It normalizes violence, distorts truth, and corrodes the very public sphere that democracy depends on. But the algorithm itself isn't the enemy. The problem lies in how it's designed and for whom. Digital technologies don't exist in isolation. They replicate and amplify the inequalities and hierarchies already present in society. What we call neutral technology often mirrors the interests of power. In the next episode, we will go deeper into digital sovereignty: how power moves between corporations and states, how platforms become instruments of governance, and how sovereignty in digital spaces isn't absolute but negotiated, shaped by profit, ideology, and control.