Tag Archives: Black Hat SEO

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AI and reputation management

The newly released Justice Department files on Jeffrey Epstein contain something that should concern every executive, communications professional, and anyone who relies on their established name to do business: a detailed, years-long record of a reputation management campaign built entirely on deception.

And ultimately, it failed.

According to a New York Times review of thousands of pages of emails and financial records released by the DOJ, Epstein began his push to rehabilitate his online image within a year of his 2009 release from jail following a conviction for sex crimes involving a minor. Within two hours of receiving a cold email promising to make the “crap that comes up on Google search on your name basically disappear,” he responded with one word: “Yes.”

What followed was a multi-year, multi-hundred-thousand-dollar campaign involving SEO experts, content writers in the Philippines, self-described hackers, and a revolving cast of fixers — all working to scrub his criminal past from Google, sanitize his Wikipedia entry, and manufacture a false persona as a philanthropist and intellectual.

New York Times reporters Tiffany Hsu and Ken Bensinger‘s in-depth investigation into this ORM program is spot on about the dark side of the online reputation management industry. (For a look at the ethical practice of reputation management, check out my newly updated guide, Reputation Reboot: What Every Business Leader, Rising Star & VIP Needs to Know – 2026 AI Edition.)

The Light Side and the Dark Side

Online reputation management is a legitimate, valuable industry. Corporations, executives, public figures, and private individuals use it every day to ensure accurate information about them dominates their search results, to correct falsehoods, and to build a credible, authentic digital presence. Done right, it is a powerful tool for protecting something that, as I often tell clients, functions as real currency in today’s professional world.

But the documents reveal what Epstein’s team was doing was something else entirely. They built networks of fake Wikipedia editing accounts — known as “sock puppets” — to sneak changes past volunteer editors, who were catching and reversing their edits within 15 minutes. They manufactured fictitious websites and personas designed purely to fool search algorithms. They planted flattering articles in major publications that omitted any mention of his sex offender status. They called this work “pimping.”

As one legitimate ORM professional quoted in the Times put it: “This world has a light side and a dark side.” What Epstein’s crew was doing was “completely anathema” to ethical practice.

A Cautionary Tale with Real-World Consequences

Perhaps the most sobering part of the story is that the deception partially worked — for a while. MIT’s Media Lab accepted $750,000 in donations from Epstein between 2012 and 2017. A subsequent university investigation noted that edits to his Wikipedia page that softened the allegations against him may have influenced the decision to accept his money.

The manufactured reputation gave him enough cover to maintain relationships and access he should never have had. The human cost of that is incalculable.

But here is the other truth the documents make plain: it was never sustainable. No amount of money — and Epstein spent lavishly, constantly, and was still never satisfied — could permanently alter a reality that hadn’t changed. The Wikipedia editors kept coming back. Google kept surfacing the truth. His own emails show him writing, again and again: “Results still very bad.”

Reputation Cannot Be Manufactured

This is the core lesson every executive and organization should take from this story.

Reputation is not built online. It is reflected online. Your digital presence is a mirror of your actions, your conduct, and the truth of who you are. The most powerful thing legitimate online reputation management can do is ensure that mirror is accurate, complete, and favorable — not distorted, fabricated, or falsified.

When clients come to us after a reputational setback, one question we ask is not “what do you want people to find?” but “what is true about you that isn’t being told?” That is where sustainable reputation work begins: with authentic accomplishments, genuine expertise, and honest communication. That is the same technique used in personal branding, as well – when clients want more information about them online so prospective partners, investors, journalists and other pivotal figures can find it.

Black-hat tactics — fake reviews, sock puppet accounts, planted content, manufactured personas — may produce short-term results. But they introduce enormous legal, ethical, and reputational risk. And as the Epstein files demonstrate in painful detail, when the truth eventually surfaces, the gap between the manufactured image and reality only makes the damage worse.

What This Means for You

If you are an executive, business leader, or high-profile individual, this story is a useful reminder to ask some pointed questions about your own digital presence:

— What does your Google search actually say about you today?

— Is your Wikipedia page, if you have one, accurate — and are legitimate channels being used to maintain it?

— Are the people managing your online reputation operating transparently and ethically?

— Is your digital presence built on real content and genuine accomplishment, or on shortcuts that could unravel?

The Epstein files are an extreme case. But the underlying dynamics — the temptation to control one’s online narrative by any means necessary, the willingness to pay for shortcuts, the false sense of security that comes from temporarily buried search results — are not unique to him.

 
 
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Reputation Communications' online reputation management glossary

Online Reputation Glossary: AI Update

These are the terms that shape how people and organizations appear online today, from search rankings and social media to the AI platforms that now answer the question “Who is this person?”

Glossary A–Z

AI Hallucination. When an AI platform presents false or invented information as fact, such as a fabricated quote, lawsuit, or job title attributed to a real person.

AI Overviews. AI-generated summaries that Google displays above traditional search results, drawn from several websites at once. What they say about a person or company may be the first, and only, thing a searcher reads.

Algorithm. The set of rules a platform uses to decide what to show and in what order. Search engines use algorithms to rank results, social media platforms use them to curate feeds, and AI platforms use them to choose which sources to draw on.

Answer Engine. An AI tool, such as ChatGPT, Perplexity, Gemini or Copilot, that responds to questions with a written answer rather than a list of links.

Astroturfing. Creating the false impression of grassroots support or opposition through fake reviews, comments, posts or accounts. It is increasingly done at scale with AI-generated content. In the U.S., an FTC rule finalized in 2024 bans buying or selling fake reviews.

Authenticity. The quality of being genuine. Audiences, journalists and online communities value content that is consistent, verifiable and clearly attributable to its real author.

Black Hat SEO. Manipulative tactics that violate search engines’ guidelines in order to boost rankings. Examples include link farms (networks of sites created only to link to one another), keyword stuffing, hidden text and paid link schemes. Search engines penalize sites caught using them, often by demoting or removing them from results.

Brand SERP. The search engine results page that appears when someone searches your name or your organization’s name. It is often your first impression, which is why online reputation management focuses on what appears on page one.

Branded Content. Content that promotes and cultivates a rapport between a targeted audience and a brand’s products and/or services.

Cheapfake. Manipulated media made with simple editing rather than AI, such as slowing down a video, cropping a photo, or splicing audio out of context.

Content. Information delivered in any medium, whether text, video, podcasts or images. When two or more media are combined, it is described as “multimedia content.”

Content Aggregator. A platform or app that collects and republishes content from many sources, such as news headlines, blogs, podcasts and videos. AI answer engines act as a new kind of aggregator, summarizing sources rather than listing them.

Content Credentials. Embedded information that records where a piece of digital content came from and how it was edited. Also called content provenance, it helps verify authenticity in an era of synthetic media.

Content Farms. Operations that mass-produce low-quality content designed to rank highly in search results. Many now use AI to generate thousands of pages, sometimes including inaccurate information about real people.

Coordinated Inauthentic Behavior. Networks of fake or linked accounts working together to mislead audiences about who is behind a message or how widely it is supported.

Data Broker. A company that collects personal information, such as addresses, phone numbers, relatives and property records, from public and commercial sources and sells it.

Deepfake. AI-generated or AI-altered video, audio or images that realistically depict a person saying or doing something they never did.

Defamation. A false statement presented as fact, communicated to others, that harms the reputation of a person or organization. Written or published defamation is libel; spoken defamation is slander. Opinions and substantially true statements generally are not defamatory.

Digital Assets. The online content a person or organization owns and controls, such as websites, bios, images, videos and articles, which can be used to shape their online presence. The term is also used for cryptocurrency, which is a different meaning.

Digital Footprint. The total trail of information about a person online, including both content they created and content others have published about them.

Disinformation. False information spread deliberately to deceive. See also Misinformation.

Domain Squatting. Also known as cybersquatting. Registering or using a domain name to profit from the goodwill of someone else’s name or trademark, often by offering to sell it to the rightful owner at an inflated price.

Doxxing. Gathering information about an individual from online sources, then publishing their private information with malicious intent.

E-E-A-T. Short for Experience, Expertise, Authoritativeness and Trustworthiness, the qualities Google’s search quality guidelines use to assess content. Strong E-E-A-T signals help credible content perform well in search.

Entity. A person, company, place or concept that search engines and AI platforms recognize as a distinct “thing” with associated facts, rather than just a string of keywords.

Generative Engine Optimization (GEO). Shaping online content so that AI platforms find it, trust it and cite it accurately when answering questions. Also called answer engine optimization (AEO).

Knowledge Panel. The information box Google displays beside search results for well-known people and organizations. It draws on sources such as Wikipedia, Wikidata and official websites.

Large Language Model (LLM). The type of AI system behind chatbots like ChatGPT, trained on vast amounts of text to generate human-like responses.

Link. A clickable connection from one web page to another. The number and quality of links pointing to a page (“inbound links” or “backlinks”) is one of many signals search engines use to rank it.

Misinformation. False information shared without intent to mislead. See also Disinformation.

Online Audit. An assessment of how a person or organization appears online, across search results, social media, news, review sites and AI answer engines.

Online Communities. Online spaces where people with shared interests hold discussions, including forums, Reddit communities, Discord servers, Facebook groups and comment sections. Also called “virtual communities.” Their discussions are often surfaced by search engines and cited by AI platforms.

Online Monitoring. Real-time tracking of the information available about a person, business or topic across the Internet, including social media, news, review sites and AI-generated answers.

Online Reputation Management (ORM). Establishing, improving, monitoring and protecting the publicly available information about a person or organization, in search results, on social media and in the answers AI platforms generate.

Review Gating. Soliciting reviews only from customers expected to leave positive ones. The FTC has warned that the practice can be deceptive.

Right of Publicity. The legal right to control commercial use of your name, image and likeness, and in some jurisdictions your voice.

Right to Be Forgotten. A legal right, recognized in the European Union and some other jurisdictions but not broadly in the U.S., to request the removal of certain outdated or irrelevant personal information from search results.

Search Engine Optimization (SEO). Improving a website or page so search engines can find, understand and rank it, attracting more visitors. See also White Hat SEO, Black Hat SEO and Generative Engine Optimization.

Section 230. The U.S. law, part of the Communications Decency Act, that generally shields websites and platforms from liability for content posted by their users.

Sentiment Analysis. Software that classifies online mentions as positive, negative or neutral, used to monitor reputation at scale.

Social Media. Platforms where people create, share and interact with content, such as LinkedIn, Facebook, Instagram, X (formerly Twitter), TikTok, YouTube and Reddit. A person’s connections on these platforms form their social network.

Sock Puppet. A fake online identity, such as an email, social media, review or forum account, used to praise oneself, attack others, or create the illusion of independent support. See also Astroturfing.

Synthetic Identity. A fabricated persona built from a mix of real and invented details, used for fraud or impersonation.

Synthetic Media. Any image, video, audio or text generated or substantially altered by AI. Deepfakes and voice clones are types of synthetic media.

Training Data. The text, images and other material used to build an AI model. Information about you in that data can shape what AI platforms “know” about you.

Transparency. Openness and honesty in online communications, including disclosing who is behind content and any paid relationships.

Troll. A person who sows discord online by posting inflammatory, provocative or off-topic messages.

Viral Content. Content that spreads rapidly as people share it across social networks and messaging apps, gaining wide visibility within hours or days. Virality can help or harm a reputation, and is difficult to reverse.

Voice Cloning. Using AI to replicate a person’s voice from short audio samples. It is increasingly used in executive impersonation and wire-fraud schemes.

White Hat SEO. Search engine optimization that follows search engines’ guidelines and relies on quality content and legitimate techniques rather than deception.

Zero-Click Search. A search in which the user gets an answer directly on the results page, from a snippet, Knowledge Panel or AI Overview, without clicking through to any website.

For more in-depth information, read The Essentials: Online Reputation Management FAQs.

 
 
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Reputational Risk of Being A Man

Recently, leaked documents obtained by Forbidden Stories revealed the inner world of Eliminalia, a Spanish reputation management company. Forbidden Stories and partners investigated the company’s manipulation tactics to remove public-interest information from the internet.

Interfor International, the investigative firm helmed by our Advisory Board member Don Aviv,  blogged about Forbidden Stories’ findings. The excerpts below raise awareness of the dirty tactics used by some reputation management agencies, and why it may pay to steer clear of them. That’s especially true if they promise to remove online content, a challenge we have written about here (and here).

Weaponizing Data Protection Regulations

Those who have studied Eliminalia’s strategy identified a pattern. When an article that included unpleasant truths about one of their clients appeared, the company began by sending takedown requests to the journalist, usually through a team member employing a false persona. If the journalist refused to remove their article, Eliminalia went after hosting providers, often weaponizing data protection laws such as the DMCA (Digital Millennium Copyright Act), which was created in 2002 to protect copyrighted content, and the GDPR (General Data Protection Regulation), an EU privacy and human rights law, to push the provider to take down the material.

To exploit the DMCA, for example, they would copy an article, publish it on a third-party website with a falsified earlier date than the original and then claim the real article infringed on the law. Contesting a false claim of DMCA is not easy, leading to long and expensive legal battles many journalists are unable to afford.

Investigators found that if these methods did not work, Eliminalia would then try to hide the material through “deindexing,” which attempts to fool Google into hiding search terms from web results.

Throwing ‘Digital Atomic Bombs’

Eliminalia has used the strategy of “open redirects,” links that appear to drive traffic to legitimate websites but redirect to other fake sites.

At least 622 such websites have been identified. To make the sites appear legitimate, the company mixes content from real sources with positive information about individuals with the same names as their clients.

This method seems to have been successful at influencing Google’s search results, effectively making articles that include allegations against the company’s clients disappear, while replacing them with positive spin.

Now,  Eliminalia and their clients are in the news, and many of their “removed” links and content are back on Google.

Eliminalia is far from the first reputation management firm to create fake news sites to post fake content on. That has been done from the onset of this industry, along with a myriad of ways to post links in places where unwitting internet users would click on, thinking they were clicking on something else.

The problem with using reputation management providers who game the system using what are known as “black hat” methods is that their handiwork is often discovered and undone by Google. You also risk the chance of being identified as a client in investigative articles about them (check out the 2020 Wall Street Journal article, Google Hides News, Tricked by Fake Claims.)

For more insights, read The Washington Post’s article, Leaked files reveal reputation-management firm’s deceptive tactics. And our ultimate guide, The Essentials: Online Reputation Management FAQs.

 
 
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Right to be Forgotten on Google

Google has just launched an attack on “fake news and problematic content,” including “rumors, urban myths, slurs or derogatory topics.” That is good news for anyone (and any organization) plagued by such issues. It is bad news for low-quality content, fake links and other tactics used to trick Google into suppressing as well as raising online content.

We like it. Now you, the consumer, can flag false, biased, offensive and inaccurate content that Google brings up on search suggestions. You can also include a note that explains why Google should remove it. Equally beneficial, the Internet can potentially become a fairer playing field.

Last week Danny Sullivan, a leading search engine expert, wrote the defining explanation of what this means. These are excerpts from his article on Search Engine Land :

Google knows it has a search quality problem. It’s been plagued since November with concerns about fake news, disturbing answers and offensive search suggestions appearing at the top of its results. “Project Owl” is an effort by the company to address these issues, with three specific actions being announced today.

In particular, Google is launching:

  • a new feedback form for search suggestions, plus formal policies about why suggestions might be removed.
  • a new feedback form for “Featured Snippets” answers.
  • a new emphasis on authoritative content to improve search quality.

“Problematic searches” is a term I’ve been giving to a situations where Google is coping with the consequences of the “post-truth” world. People are increasingly producing content that reaffirms a particular world view or opinion regardless of actual facts. In addition, people are searching in enough volume for rumors, urban myths, slurs or derogatory topics that they’re influencing the search suggestions that Google offers in offensive and possibly dangerous ways.

“These are problematic searches, because they don’t fall in the clear-cut areas where Google has typically taken action. Google has long dealt with search spam, where people try to manipulate its results outside acceptable practices for monetary gain. It has had to deal with piracy. It’s had to deal with poor-quality content showing up for popular searches.

“Problematic searches aren’t any of those issues. Instead, they involve fake news, where people completely make things up. They involve heavily-biased content. They involve rumors, conspiracies and myths. They can include shocking or offensive information. They pose an entirely new quality problem for Google, hence my dubbing them “problematic searches.”

Read his full article: Google’s ‘Project Owl’ — a three-pronged attack on fake news & problematic content.