Fact-Checking Techniques in Real-World Scenarios

Let us compare all the information on the Internet, television and radio to a loaf of baloney. How can we tell the meat from the fillers and casing? 🔪 Slicing the Baloney: Three Cuts to Find the Truth The process is not about a single slice, but a series of forensic cuts designed to separate the meat (fact) from the casing (spin) and the filler (misdirection). 1. The Cross-Grain Cut (Separating Spin from Fact) This cut involves slicing the baloney loaf perpendicular to the direction of its visible grain or pattern. • The Baloney: Most government and media messaging has a "grain"—the familiar narrative, the standard framing, the emotional appeal. This is the comfort zone of Confirmation Bias. • The Cut: Instead of following the narrative (the grain), you cut against it. You ask questions that directly challenge the underlying assumption: • If the headline says “genocide,” are people being killed at random based solely on ethnicity? - If the headline says “a threat to our democracy,” ask “since when do we live in a democracy?” And how is it threatened? • The Goal: To ignore the presented conclusion and focus only on the raw, supporting evidence. This cut reveals the bare facts (the bits of meat) that are suspended in the narrative binder (the fat/filler). 2. The Transparent Slice (Uncovering Omissions) This cut is an ultra-thin, almost transparent slice, held up to the light. • The Baloney: Misinformation often involves omission—the deliberate leaving out of inconvenient facts. A thick slice hides what's behind it. • The Cut: You take the story, and ask: "What is missing?" • Who is quoted, and who is conspicuously absent from the interview list? • What are the specific numbers they mention, and what is the baseline context (the previous year's or competitor's number) that would make them meaningful? • What are the source documents they summarized, and where can the original, unedited data be found? • The Goal: The transparent slice shows the shadows—the outline of the information that was intentionally left out, which is often the most important part of the truth. 3. The Reconstructive Slice (Rebuilding the Truth) This is not a traditional slice, but the act of using the scraps from your previous cuts to build a new, clean model. • The Baloney: The original loaf is a complete, manufactured item. The truth is scattered in its pieces. • The Cut: You take all the isolated facts (the cross-grain cuts) and the context of the omissions (the transparent slices) and re-assemble them outside the original casing and binder. You apply an independent logic: • How much of the loaf is fact and how much is opinion or assumption? • The Goal: To bypass the persuasive power of the original packaging (the emotional language and official framing) and create a new, unflavored, empirical conclusion that relies purely on verifiable, cross-referenced data. By slicing the baloney skillfully you may find the truth.

Misinformation isn’t just about false facts—it’s also about misleading ones. Even if a fact is 100% true, it can still be unreliable: ⚠️ A single anecdote can be paraded as proof. ⚠️ An exception can be framed as the rule. ⚠️ A correlation can be mistaken for causation. In May Contain Lies, I explain how we can protect ourselves from misinformation. Here's how: 🧠 Step 1: Recognize That You Already Have the Tools We don’t need a PhD in statistics to think critically. Whenever a study is posted on LinkedIn that people don't like, there's no shortage of comments on why correlation is not causation, or why the example may be cherry-picked. The real challenge? Ensuring we use the same discernment for a study we do like as for one we don't. 🔍 Step 2: Beware of Confirmation Bias We latch onto whatever interpretation of the facts confirms our view of the world. Take breastfeeding and IQ: 🍼 Studies show that breastfed babies often have higher IQs later in life. 🔬 One interpretation? Breastfeeding causes higher IQ. This makes sense: breastmilk is natural; formula is a UPF. 🤔 A more critical perspective? Family support might be the real factor—since breastfeeding is easier with strong family backing. ⚫⚪ Step 3: Avoid Black-and-White Thinking The world isn’t split into “always good” or “always bad.” 🥑 Fat sounds bad—because it’s called "fat." 💪 Protein sounds good—because it “builds muscle.” 🍞 Carbs? Neutral. But diets like Atkins claimed they were the enemy. 🔹 Reality? Science suggests that carbs are healthy when they make up 30–50% of daily calories. 🔹 But tracking exact percentages is tough—so simple rules gain traction, even when they’re scientifically weak. With black-and-white thinking, to sell an idea, you don’t need to be right. You just need to be extreme. ♻️ Step 4: Flip the Narrative If a claim supports your beliefs, imagine the opposite claim instead. Example: 🔴 If a study said breastfeeding lowers IQ, how would you try to debunk it? You'd appeal to alternative explanations: perhaps poorer families breastfeed (as they can't afford formula), and it's poverty not breastfeeding that causes the lower IQ. 🟢 Now ask if the same alternative explanation applies even though the results are in the direction you want. Might family background explain why breastfed babies have higher IQ? In short, challenge the evidence—not just the conclusion. 🤔 Step 5: Embrace Healthy Skepticism Questioning flawed research isn’t just intellectual nitpicking—it’s freedom. ✅ Parents can make feeding choices without guilt. ✅ People can eat carbs without fear-based restrictions. ✅ We all gain the confidence to navigate the world with clarity. We won't get it right 100% of the time—and in the book I explain many times I got it wrong. But the goal is not to be perfect, only better.

Telling if a video is AI-generated is challenging—experts estimate detection rates at 70-80% for humans alone—but a methodical approach helps. Can you spot the subtle tells? The uncanny movements? The impossible physics? This challenge isn't just a game; it's a crucial skill in the age of deepfakes and misinformation. 💡 How to Systematically Check a Video ⇩ Follow these steps every time you encounter suspicious content (e.g., viral social media clips): 1) Verify the Source First (1-2 minutes) Who posted it? Reverse-image search a frame using Google Lens or TinEye. If the original is from an AI tool demo or lacks context (no credible news outlet), be wary. 2) Assess Basics: Length and Quality (30 seconds) Is it under 10 seconds or blurry? Play at normal speed first—if it feels "off" visually, proceed to details. 3) Scrub and Zoom: Hunt for Visual Tells (1-2 minutes) Pause frequently. Zoom into hands, text, faces, and backgrounds. Slow to 0.25x speed to catch motion glitches. Use your phone's zoom for extra scrutiny. 4) Listen Critically: Audio Check (30 seconds) Mute, then unmute. Note any robotic timbre or sync slips. Real audio has ambient noise; AI often feels sterile. 5) Cross-Check Context and Physics (1 minute) Does the scenario make sense (e.g., casual phone vid vs. polished ad)? Test physics by mentally tracing shadows or trajectories. 6) Stack the Evidence and Use Tools (Ongoing) One red flag? Could be real. 3+? Likely AI. Upload to detectors like Truepic or Deepware for a second opinion. Always share doubts—e.g., "This looks AI, thoughts?" Remember, AI detection tools lag behind generation tech, so skepticism is your best tool. Are your eyes sharp enough to see the difference? Take the test and find out! P.S.: Feel free to share your score. How many out of 5 did you get correct?

Article from NY Times: More than two years after ChatGPT's introduction, organizations and individuals are using AI systems for an increasingly wide range of tasks. However, ensuring these systems provide accurate information remains an unsolved challenge. Surprisingly, the newest and most powerful "reasoning systems" from companies like OpenAI, Google, and Chinese startup DeepSeek are generating more errors rather than fewer. While their mathematical abilities have improved, their factual reliability has declined, with hallucination rates higher in certain tests. The root of this problem lies in how modern AI systems function. They learn by analyzing enormous amounts of digital data and use mathematical probabilities to predict the best response, rather than following strict human-defined rules about truth. As Amr Awadallah, CEO of Vectara and former Google executive, explained: "Despite our best efforts, they will always hallucinate. That will never go away." This persistent limitation raises concerns about reliability as these systems become increasingly integrated into business operations and everyday tasks. 6 Practical Tips for Ensuring AI Accuracy 1) Always cross-check every key fact, name, number, quote, and date from AI-generated content against multiple reliable sources before accepting it as true. 2) Be skeptical of implausible claims and consider switching tools if an AI consistently produces outlandish or suspicious information. 3) Use specialized fact-checking tools to efficiently verify claims without having to conduct extensive research yourself. 4) Consult subject matter experts for specialized topics where AI may lack nuanced understanding, especially in fields like medicine, law, or engineering. 5) Remember that AI tools cannot really distinguish truth from fiction and rely on training data that may be outdated or contain inaccuracies. 6)Always perform a final human review of AI-generated content to catch spelling errors, confusing wording, and any remaining factual inaccuracies. https://lnkd.in/gqrXWtQZ

Most of our information literacy teaching assumes the first serious object of attention is a results list - That is no longer true. Many students and faculty now start with an AI #answer. That answer often shapes what feels plausible, what feels relevant, and what feels complete before they see any sources. I wrote an article in The Journal of Academic Librarianship on what this shift means for our work, and I built two practical tools you can use immediately. Answer typography Ask what kind of work the AI answer is doing: Factual. Interpretive. Constructive. Strategic. The #CARE approach A simple set of moves librarians can model in any reference or teaching moment. #Classify the answer. #Assess what is present and missing. #Review key claims against scholarly sources. #Enhance the answer through revision and co creation. Try one small change this week. When someone brings you an AI response, start by classifying it, then pick one claim to verify. The #infographic attached is meant to make this teachable in minutes. Feel free to use it in you class, LibGuides, etc., with attribution. Free access to the article (until Feb 3, 2026): https://lnkd.in/dFv79_Z3 #AcademicLibraries #InformationLiteracy #AILiteracy #HigherEducation #GenerativeAI

It's impossible to overstate how crucial visual investigation skills are in the current information ecosystem — the Good and Pretti killings, the Guthrie kidnapping, ICE's brutal tactics against innocent bystanders. I don't care where you work in the newsroom, you MUST know how to track the down the source of an image, verify a video frame-by-frame, use AI tools to unveil watermarks and anomalies, and on and on. That's why I was lucky to get advice for any newsroom of any size from some of the best in the business: Henk van Ess, Loreben Tuquero, Malachy Browne, Alexios Mantzarlis, and so much more from a The Minnesota Star Tribune livestream featuring Amanda Anderson, Anna Marie Boone and Jake Steinberg. A quick preview: • Provenance: Where did this first appear? Can you trace the original upload? • Time and date: Do the weather, lighting and shadows match the claimed time and place? • Location: Are landmarks, signage and street layouts consistent with the claimed location? • Technology: Are devices and infrastructure consistent with the time and place? Is the video quality consistent with the camera that allegedly captured it? • Behavioral patterns: Do people move and interact naturally, or does something feel scripted? • Physics: Do reflections, shadows and fine details — fingers, teeth, text — hold up? I have mostly gotten away from teaching OSINT and verification to focus on product and generative AI. But, damn, are those skills needed WAY more than prompting right now. The Poynter Institute, the International Fact-Checking Network and PolitiFact have — and are — teaching these skills all the time, so let me know if you want us to come to your newsroom. https://lnkd.in/e7fPjkkp

When a screenshot can be faked in seconds and AI can generate convincing evidence from thin air, how do we separate truth from trickery? 🔎 "Evaluating Evidence in the Age of AI" – Johanna Drayton & Jordan Boyle (Dyhrberg Drayton Employment Law) reminded us that credibility still rests on fundamentals like corroboration, consistency, and plausibility. But in an era of deepfakes, manipulated screenshots, and synthetic voices, investigators must treat digital evidence with healthy skepticism, verify directly from source systems, and transparently document reliability concerns. 💻 "The Digital Trail: Collecting and Analysing Evidence from Social Media and Online Platforms" – Caroline O'Connor & Emma K. (Q Workplace Solutions Pty Ltd) brought case studies that showed how seemingly small digital crumbs like conflicting texts, CCTV, Google timelines, and Facebook posts, can converge into compelling evidence. Their practical takeaway: think beyond the obvious, as digital evidence can serve as both direct and corroborative support. Both sessions underscored that while technology is reshaping the evidentiary landscape, the investigator’s role is more vital than ever—to verify, contextualize, and critically assess. Key takeaways for fellow investigators: ✅ Always go to the source - don't rely on screenshots alone ✅ Metadata is your friend (timestamps, geolocation, IP logs) ✅ Cross-verification is critical - one piece of evidence should support another ✅ Document your verification process transparently ✅ Think creatively about digital evidence sources (weather data, Google timelines, etc.) A big thank you to Association of Workplace Investigators - Australia & New Zealand and the presenters for equipping us with sharper tools to navigate investigations in the digital age.

>> Can We Trust What We See? The Future of AI-Generated Content 🤖✨ In a world where AI can generate lifelike videos of public figures—like President Biden and former President Trump portrayed as friends—how do we determine what's real? AI is reshaping our perception of reality, making it crucial to develop new skills in digital literacy and critical thinking. Here’s how you can spot AI-generated content: 🔍 Look for Visual Oddities: AI may struggle with fine details like inconsistent lighting, odd reflections, or unnatural facial expressions. 🎙 Analyze Speech Patterns: Voice generation often lacks natural rhythm, emotion, or pauses, making speech sound overly smooth or robotic. 📑 Verify Sources: Trust reputable sources and cross-check content across multiple reliable platforms. If something seems too sensational, dig deeper. 📱 Use AI Detection Tools: Tools like Deepware Scanner and Sensity can help identify AI-manipulated media by analyzing pixel inconsistencies and metadata. 💡 Check for Missing Background Details: AI might blur or simplify backgrounds and may miss subtle context cues in a scene. This isn’t just technology for entertainment—it has the power to impact public opinion, security, and even democracy. As we move forward, digital literacy will be essential. Question: As AI blurs the line between reality and illusion, how can we ensure we’re building a future where technology enhances trust rather than erodes it? ---- 👉 Found this helpful? Share it! ♻️ Don’t miss out! For exclusive AI and tech insights trusted by over 365,000 professionals at Microsoft, Google, Amazon, and beyond—subscribe to my free newsletter for cutting-edge strategies to keep you ahead in AI. 🔗 Subscribe now: https://lnkd.in/eFNvmcYa 🚀 Leverage AI, boost your career, and master the future with 80+ bestselling AI eBooks. 🔗 Get your eBooks here: https://lnkd.in/emSWFxrN 👉 Love my content? ☑ Follow me Dr. Joerg Storm ☑ Hit the 🔔 on my profile for instant updates!

Can you guess the world’s top short-term risk in 2025 according to the World Economic Forum? Misinformation. Social media is overtaking traditional news channels. Generative AI is flooding our feeds. Fake news is everywhere. It´s increasingly hard for anyone to distinguish fact from fiction. I firmly believe our best defense against misinformation is a scientific mindset—skepticism, evidence, and humility. Science is more than a body of knowledge—it’s a way of thinking and an essential foundation for a resilient, informed, and free society. But you don´t need to be a scientist to think clearly, question boldly, and stay grounded in truth. If you want to practice your critical thinking skills, here are some truth-seeking tips. They´re inspired by Carl Sagan’s “baloney detection kit” published 30-years ago in his prophetic book The Demon Haunted World: Science as a Candle in the Dark. 1. Verify Independently: Truth isn’t truth until it’s confirmed. Always look for independent evidence before accepting a claim. 2. Welcome Debate: If an idea can’t survive open discussion, it probably shouldn’t. Truth thrives on challenge. 3. Question Authority: Experts matter—but evidence matters more. Don’t accept claims just because someone “important” said so. 4. Consider Alternatives: Always ask: What else could explain this? The best thinkers explore multiple possibilities. 5. Occam’s Razor: The simplest explanation that fits the facts is usually best. 6. Let Go of Bad Ideas: If the facts change, so should your mind. Clinging to a disproven belief is not strength—it’s bias. 7. Use Numbers: Quantifying helps clarify. Vague claims are harder to validate—data brings precision. 8. Ask: Can It Be Tested? If a claim can’t be proven wrong, it can’t be proven right. Testability is the heart of science. Save or bookmark these tips — they’re timeless tools for clear thinking. #Misinformation #CriticalThinking #ScienceMindset #Leadership #AIethics

A lesson for me and maybe for you. 👇 In cybersecurity we talk a lot about zero trust — but what we don’t talk enough about is about applying that mindset to information itself. Recently, I got caught out. Not by malware. Not by a phishing email. But by information that looked credible, and was shared by a trusted cybersecurity source. Sadly, it turned out to be inaccurate and misinformed. I don’t blame this person. As I said, it was a timely reminder to do better and to understand that: ✅ Trust is not a substitute for verification ✅ Cognitive bias affects all of us — even those trained to detect deception ✅ We all need to slow down and check. So, here’s my curated list of tools and resources to help spot misinformation, scams, and dodgy websites. I highly recommend taking a look — and please feel free to add others you trust in the comments. I’ll be checking them out! 😆 A course in how to find reliable info online: https://lnkd.in/e4rG8sfb Fact checker tools: https://www.factcheck.org/ https://lnkd.in/eUKBcRB6 StopagandaPlus (browser extension) https://lnkd.in/eJui5ijZ Tools like Full Fact, ClaimBuster, and Chequeado are at the forefront of automated fact checking. They cross-reference claims against databases of verified information, flagging potential falsehoods in near real time. However, they’re not infallible. These systems struggle with context, nuance, and rapidly evolving situations. They’re best used as a first line of defence, not as the final arbiter of truth. Check a website & find out how likely it is to be legitimate. Just put the url in and it will tell you: https://lnkd.in/eDSjP3S7 Ask Silver to check to see if a message is a scam. Upload a screenshot on WhatsApp and it will tell you & report it to the right authorities : https://lnkd.in/evG545Nn Virus Total (similar to check a website) https://lnkd.in/eYyhWMNU Can you detect these deepfakes? https://lnkd.in/ejf2c95U https://lnkd.in/e5etYRET ⸻ No matter how experienced you are, never let trust replace due diligence. Disinformation (fake news, deliberate spreading usually for a political agenda) and misinformation (mistake/ misinformed) are rife and scaling thanks to AI. Even the most well-intentioned sources can get it wrong. Stay curious, stay cautious, and keep learning. Got more tools or techniques you use to verify info? Share them below — let’s build better digital habits together. 💬👇 #CyberSecurity #Misinformation #MediaLiteracy #FactChecking #DigitalHygiene #CriticalThinking #ZeroTrust #Scams #OnlineSafety