The Credibility Crisis in Media Monitoring
Media intelligence companies are challenged with the task of delivering accurate insights from trusted and unique sources. Now, with the rise of misinformation in the news, this competitive industry has become even harder to navigate.
The media landscape is being reshaped at an unprecedented pace by AI-generated misinformation. From synthetic news articles and deepfake videos to machine-generated propaganda, media intelligence platforms face an escalating challenge: ensuring the reliability of the insights they deliver. As a result, data reliability becomes more complicated in two ways. First, and most obvious, is the large amount of noise clogging up newsfeeds. With so much noise it is too easy to miss an insight that might be crucial. Second, more news on the internet means that every single piece of content must be investigated for biases or misinformation. Media monitoring software that captures the bigger picture of relevant misinformation campaigns being spread about their customers can provide extra insights to their customers.
Misinformation and disinformation is “ranked the #1 global risk in the next 2 years by the World Economic Forum in 2025” (World Economic Forum Global Risk Report 2025).
If clients can’t be sure that the data they’re receiving is accurate, they won’t trust the platform providing it. Protecting data integrity is paramount for building client confidence and a thriving business.
Fake mentions and real damage
AI-generated content has become very sophisticated and it travels fast. Automated tools can create entire news websites with fabricated stories, complete with fake expert quotes and realistic imagery. For example, a phony news site that publishing an article about a major product recall, complete with phony “expert” quotes and realistic-looking images could trigger widespread panic and damage a company’s reputation.
As of January 2025, there are 1,254 unreliable AI-generated news and information websites written in 16 different languages. The number of unreliable news websites is more than the number of local newspapers operating in the U.S. in 2024. With over a thousand websites generating news stories, it is hard to keep track of the large volume of AI-generated news stories circulating the web. Fake news websites are created to look identical to a real, trusted, news source. Fraudulent news sites like Washingtonpost.com.co and toronottoday.net have urls that either look like typos of real news outlets or seem innocuous enough that you need to investigate further.
AI-powered bots are capable of artificially inflating the online presence of specific narratives, regardless of their truthfulness. These bots can create the illusion of support for a particular viewpoint, even if it’s misleading or outright false. By overwhelming social media and forums with repetitive messages and fabricated endorsements, they can manipulate public perception and make these narratives appear far more credible than they actually are. This manufactured consensus can be incredibly persuasive, making it harder than ever to discern fact from fiction.
AI-generated misinformation: a product development challenge
This proliferation of synthetic content presents two critical challenges for media monitoring product development:
1. Reliability: maintaining client trust in a sea of misinformation
- The problem: AI-generated news articles, deepfake videos, and manipulated narratives muddy the waters, making it difficult to distinguish fact from fiction. This impacts the accuracy of your platform’s reports, jeopardizing client trust and leading to churn.
- The product impact: Erosion of client trust translates to decreased product adoption, lower renewal rates, and a negative impact on your product’s ROI. Addressing this challenge is paramount for maintaining a competitive edge and ensuring long-term product viability.
2. Scalability: integrating datasets without compromising performance
- The problem: The sheer volume of AI-generated content, coupled with the need for rigorous verification, puts immense strain on data pipelines and processing capabilities. Integrating this data while maintaining performance and efficiency is a significant scalability challenge.
- The product impact: Slow processing speeds, delayed insights, and data overloads can negatively impact user experience and make your product less competitive. Addressing scalability is essential for delivering a seamless and valuable user experience.
3. Verifiability: tracking datasets back to their source
- The problem: It’s hard to track coordinated misinformation campaigns when they happen all the time.
- The product impact: Effects the accuracy of insights and the product’s ability to provide valuable and time-sensitive data. This could impact your company’s reputation. In order to avoid this, you need to invest time and resources into your product to create a new functionality that can assess patterns of sources and articles to map coordinated misinformation campaigns and their effects.
4. False signals and noise: Media monitoring tools use algorithms to track trends, but AI-generated misinformation can create false signals. A flood of synthetic content can distort sentiment analysis, skew trend detection models, and lead to misinterpretations of market or political landscapes. Imagine trying to gauge public opinion on a new product when AI-generated bots are artificially inflating both positive and negative sentiment, making it nearly impossible to get a clear picture. Misinterpretations of market or political landscapes.
5. Challenges in fact-checking: Fact-checking models typically rely on historical credibility scores, cross-referencing trusted sources, and network analysis of citations. However, AI-generated misinformation can bypass these safeguards by creating new sources that appear legitimate.
6. Regulatory and reputational risks: While there are no universal legal consequences for failing to detect AI-generated misinformation, regulatory scrutiny is increasing. The EU’s Digital Services Act (DSA) now holds platforms accountable for curbing misinformation. Additionally, companies using AI-generated content without clear labeling could face reputational damage if exposed.
Why is this problem difficult to solve?
A report by OECD states: “the sheer volume of data and information has grown exponentially, making it more difficult to sift through and verify sources. In 2025, 43% of organizations list data volume as a top concern, compared to 35% in 2023.” And the amount of fake information eclipses the amount of real news data.
As AI-generated misinformation evolves, media intelligence platforms face mounting pressure to provide accurate, reliable insights. Failing to address these challenges can erode customer trust, damage brand credibility, and weaken competitive advantage.
1. Limited resources for detecting misinformation at scale
Identifying misinformation requires substantial investments in time, technology, and human oversight. Analyzing vast datasets to detect patterns is resource-intensive, yet many companies struggle to allocate the development capacity needed. Without dedicated resources, misinformation can go undetected, leading to unreliable insights and loss of customer confidence.
2. Balancing accuracy without sacrificing valuable insights
- Strict filtering can remove legitimate sources along with misinformation, limiting depth and diversity in analysis.
- Traditional credibility scoring depends on human-reviewed sources, but AI-generated content is becoming increasingly difficult to distinguish from legitimate news.
When filtering systems fail, media intelligence platforms risk either amplifying false narratives or providing incomplete insights—both of which can damage their credibility.
3. The cost vs. performance dilemma
- Bringing in diverse, high-quality sources without increasing costs or slowing platform performance is a persistent challenge.
- Reliable sources, including those on platforms like Telegram, often come at a high price. Companies seeking cost-effective solutions may end up with incomplete or lower-quality data, reducing the accuracy of their intelligence.
- Poor data quality directly impacts user trust—if customers consistently encounter misinformation or gaps in coverage, they will turn to competitors.
4. Data validation and the limitations of existing solutions
- Platforms rely on third-party tools like NewsGuard and Factmata, but these often fail to detect AI-generated misinformation.
- Without comprehensive monitoring across industries, regions, and languages, platforms risk missing critical insights or introducing bias into their reports.
- When media intelligence platforms provide incomplete or biased data, their clients—journalists, analysts, and businesses—make decisions based on flawed information, eroding trust and weakening long-term retention.
5. Preventing blind spots and maintaining consistency
- Misinformation spreads across diverse sources, from fringe websites to encrypted messaging apps. Without broad coverage, platforms risk missing key narratives.
- Fluctuations in data availability or inconsistent validation processes can undermine credibility.
If platforms cannot provide continuous, high-quality insights, leadership teams, data analysts, and product managers will question their reliability, creating internal frustration and driving customers toward more trusted solutions.
Trusting your data provider: the ingredients for success
AI-generated misinformation evolves faster than detection tools can keep up, making it harder for media intelligence platforms to verify their data. The flood of AI-generated content, lack of universal credibility standards, and advanced manipulation tactics make it harder than ever to separate fact from fiction. To maintain credibility, gain competitive edge, and protect your product’s ROI media intelligence platforms must work in partnership with their data providers. A trusted data provider, like Webz.io, sources data from sites you can trust, like top-tier news sites, and provides tools to assess the credibility of your data, like fake and satire news tags.
Read our case study to discover how Webz.io’s trusted data helped Keyhole to monitor brand mentions across the web.
