Case Study #29:
Corporate Wikipedia Article Editing Case Study by Reputation X

A multi-billion dollar company was plagued by competitors masquerading as activists. They generated articles to negatively skew online sentiment toward the company. It was a perfect storm of Google manipulation that Reputation X helped turn around.


Our client is a global brand with an established online presence. Well-funded international detractors had been using our client's Wikipedia page as their playground – effectively weaponizing it. This included long-term development of negative articles in well-known publications that were not only visible in search results, but sometimes used as Wikipedia references as well. 

As the negative press continued to gain traction, it seemed as if the detractors were accomplishing their goal. Our client’s search results deteriorated to mostly negative accounts that put their online reputation at risk - until we took action by confronting and disputing the content. 

Campaign statistics

  • Base measurement (beginning): 60% negative, 20% positive, 20% neutral. Negatives were above the fold
  • Post-campaign measurement: 10% negative, 60% positive, 30% neutral. Remaining negative in position ten or off of page one
  • Duration: 12 months
  • Improvement began after 30 days of strategy and content approval

Challenges to overcome 

We researched our client’s unique situation to develop a reputation strategy that would target each of their pain points with the overarching goal of improving their search result sentiment. 

Our objective was four-fold:

  • Improve online sentiment for branded searches
  • Balance the client's Wikipedia page
  • Increase the amount of control our client had over their branded search results
  • Push down negative content hurting our client's brand

Wikipedia appears in many, if not most, search results. For brands, it often shows up in the first or second position. Detractors leveraged this by first causing negative content to be published. Their second step was to use the new negative content as references in Wikipedia. 

This had two effects: negative search results appeared in Google globally, and the Wikipedia page of the brand became worse with each passing edit. The client had tried to set the record straight on Wikipedia, but editors with an agenda (some paid by detractors, others that just didn’t like brands improving their own pages) wouldn’t allow the edits to stick and banned the company from making edits.

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Solution Set

To solve the problem, we used our client's own resources far more efficiently, encouraged like-minded entities to support them, and executed a plan to clean up and defend their branded search results.

Details of the reputation campaign

We worked with the following parties:

  • Director of Marketing (Client)
  • Public Relations Firm (Client's)
  • SEO Team (Client's internal team)
  • Wikipedia editors with whom we have relationships
  • Our own team

Campaign execution

Before beginning our client's online reputation management campaign, we executed a research program to understand the sentiment landscape. This involved measuring sentiment, measuring client control of branded search results, and much more. The steps involved are listed below:

Measure baseline sentiment and control 
Reputation X measured the sentiment (positive, neutral, negative) of the following types of search results:
  • Google searches in four archetype countries
  • Primary and secondary branded search phrases
  • Desktop and mobile

Research similar companies to help us set objectives
We queried the client about similar companies, executives with similar titles, existing relationships with journalists, newsworthy items that may arise in the future, differences and similarities between our client and similar companies, and charitable work the client has been involved in. We also asked them to identify known detractors and to educate us on their possible motivations.

Perform a gap analysis to guide development
We compared the search and social results of our client to similar companies as seen from various locations around the world. Here are some of the results of our findings:

  • Our client had one main website. Some competitors divided content among many sites to control more search results.
  • Our client's website was inefficient. A few tweaks would improve search visibility. 
  • We generated a list of publications upon which competitors were published, but our client was not for future outreach.
  • Competitors had almost certainly paid Wikipedia editors to manage their Wikipedia pages.
  • We identified the databases that search engines used to rank content about the client, enabling us to seed them with our client's information to improve Wikipedia references and search results.
  • Similar companies used the power of not only their main website, but those of seemingly unrelated subsidiaries, to enhance the online reputation of the parent entity.
Following digital clues to identify motivations of negative content authors
When a blogger, journalist, or researcher writes negative content, they usually have an honest reason to do so. Other times they are paid or have been provided other incentives to create negative content.

For example, negative content may be created for:
  • Political purposes, as was clearly seen during US elections
  • To drive share prices down to profit from short sale of stock
  • To embarrass a company or its executives
  • For this client, we needed to discover the roots of authors' motivations to create negative content, and to identify authors who might be working at the behest of others.

Looking for patterns in digital footprints
We first examined the editing history of Wikipedia authors to spot patterns. We looked at contributors to websites to see if they were the real thing, or "sock puppets" (shills). This involved looking at their editorial histories, social media, and profiles. We also performed searches of screen names to find references to hidden content they may have authored. Finally, we looked at the backlink profiles of negative content (and the authors of that content) to find date patterns, similar IP addresses, mentions of third-parties, and more.

This type of reputation research has unearthed dark PR firms, private blog networks designed to damage reputations, troll farms, paid Wikipedia editors (and sometimes their real names), and much more. In this case, we discovered a network of activists.

Locate Wikipedia editors to come to our client's aid
Reputation X does not employ even one Wikipedia editor. Instead, we often research the history of third-party editors working in the same space as our client. In this case, we identified problems with our client's Wikipedia page that included what might euphemistically be called "alternative facts," outright lies, references to false planted information, and more.

We then reach out to knowledgable editors asking if they believe they can help to correct the Wikipedia page. In many cases, Wikipedia editors agreed the page was in fact being vandalized and worked independently to correct and defend the page - all without pay. We believe that sometimes all that needs to be done is to highlight a problem, and the community will work to correct it.

Design a more efficient authority flow from client-controlled sites
Our client controlled dozens of websites for itself and its subsidiaries. While the sites had been in existence for years, they were often based on out-of-date technologies, badly optimized, and far less supportive of the parent brand than they could have been.

We performed SEO analysis on each site, provided a checklist to our client's technical team, and set them to the task of improving each site. This included suggesting new articles for each, adding new link references, and a social media campaign to support the improvements. We used the "five types of content" promotion plan.

After 90 days, Google began ranking many of them on the first page of search results - above negative content. This worked because while the authority of each of the client-controlled websites was good, it was not optimized for the parent brand, nor for helping other positive third-party content gain the relevance needed to perform well in search.

Design a "new mythology" for our client
Our client was an established entity with years of history documented online. In the distant past, they'd done things that may not be acceptable in today's environment. While Google does its best to provide the best possible search results, those results are often out of date and appear in search results because of popularity more than anything else.  

To add to the problem, many search results rank highly because of negativity bias or confirmation bias - in other words, people tend to click on sensational stories. Google gets signals that the popularity of stories may mean others are interested. Consequently, the stories become more visible in search results. We needed to break the cycle of negativity.

We started with a reputation management process we developed called the Reverse Wikipedia Strategy. The Reverse Wikipedia Strategy is a framework to improve a brand's online content by imagining what an ideal Wikipedia page may look like in a year or more. It exists within a family of strategies used by Reputation X as an exercise for strategists and content creators to think about the best possible branded content. 

Optimizing charity sites to better support our client
Our client has been quite generous to charities, but they weren't getting search and social visibility commensurate with their giving (they are too humble). We often advise clients to follow a set of best practices outlined in the Reputation X Playbook. It directs internal PR teams to ask for certain small things from a charity when providing donations. 

These requests involved the development and publication of press releases by the charity that were structured in a certain way, multiple timed social media posts by the charity (rather than just one), specific additional content being added to the charity's website, and more.

The result of these relatively simple actions was that the charity pages often rose in search results and benefited our client by making it clear to all stakeholders that our client was - in many ways - making a positive impact. 

End Result

Reputation X remodeled our client’s Wikipedia page and search results by calling out malicious Wikipedia editors, strengthening our client’s owned media to increase the amount of control they had over their branded search results, updating the content used as Wikipedia and Google references, and placing new positive content where it did the most good. 


We were able to achieve our client’s objectives by developing a reputation strategy that targeted specific causes of negative search results. Here are the results: 

  • Our client began to see improvement in their branded search results within 90 days of initial strategy and content approval.
  • Their Wikipedia page began to improve sporadically during the first months of the campaign, then stabilized with at least half of the negative content removed.
  • Sentiment scores which began as primarily negative improved to only 10% negative after six months of execution (after the research and strategy phases).
    • Base measurement (beginning): 60% negative, 20% positive, 20% neutral.
    • Post-campaign measurement: 10% negative, 60% positive, 30% neutral.

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