Study Reveals Gaps in AI Moderation as Youth Slang Outpaces Detection Systems

Young people have always felt misunderstood by the adults around them. That’s not new. What’s changing is the gap. It’s widening. Now even artificial intelligence can’t keep up with Gen Alpha.

At a recent tech conference in Athens focused on fairness and accountability, a student named Manisha Mehta presented research that points to a surprising issue. Kids’ fast-changing slang is often completely missed by the AI systems meant to keep them safe online.

Mehta's study looked at how well kids, their parents, and professional moderators could handle modern slang, comparing them to four well-known AI language tools developed by OpenAI, Google, Anthropic, and Meta. The goal was simple that is to see if people and machines could figure out what the slang actually meant, understand when the tone changed, and catch possible hidden risks.

To put the research together, Mehta worked with 24 classmates to build a list of 100 Gen Alpha phrases. Some phrases could either support or tease, depending on how and when they were used. Others came straight from gaming and social media circles. Expressions like “let him cook” or “ate that up” could either cheer someone on or poke fun at them. Words like “got ratioed” or “secure the bag” were pulled from the fast-moving world of online chats and games.

One of the key things that stood out was how often adults completely missed what these phrases meant. Parents and moderators were often left guessing, while the AI tools weren’t much better. The study makes it clear: many of the systems meant to keep kids safe simply don’t understand the language they’re using.

When the kids were tested on meanings, shifting tones, and spotting hidden harm, they almost always got it right. Their scores stayed high across the board. Parents, though, struggled badly. They often missed key meanings and failed to notice when a friendly phrase turned hurtful. Professional moderators didn’t do much better.

What this really shows is that adults, whether they’re at home or working to keep social platforms safe, can’t fully protect kids if they don’t understand the language those kids are using. A parent might only catch one out of every three moments when their child is quietly mocked or bullied in Instagram comments.

When tested on the same slang, the four AI tools landed roughly where the parents did. This suggests the data used to train these systems probably comes from more adult-focused language. Since most of what’s written in books or online comes from older people, it makes sense that these AI tools haven’t fully absorbed the latest slang from teenagers.

There’s more at stake here than just missed meanings. Gen Alpha, born in the years after smartphones became part of everyday life, has grown up fully connected to the internet. Many of their earliest social experiences have happened online, far from the view of parents and teachers. The systems built to watch over them can’t easily keep up, especially since much of the moderation now depends on automated tools. Parents can’t watch every post or chat, and even professional moderators miss things hidden in what seems like harmless talk. Meanwhile, kids’ slang keeps moving so quickly that what’s popular today could easily sound old in just a few months.

The study points to a subtle but growing gap. It’s not just a difference in age. It’s a difference in language. And when children and the systems meant to protect them don’t speak the same language, danger can easily slip through unseen.

Image: DIW-Aigen

Read next: Human vs. AI Perception: Research Uncovers Striking Differences in Object Recognition
by Asim BN via Digital Information World

Google Chrome for Android Finally Lets You Move the Address Bar After a Decade

Google is adding something small to Chrome on Android, but for many people, it might make a real difference. The address bar, which has always sat at the top of the screen in Chrome’s mobile version, can now be moved. If you want, you can place it at the bottom, where it may be easier to reach, especially on phones with larger displays.

The update is not arriving for everyone at once. It’s starting to appear now for some people, and Google is gradually making it available more widely. When it turns up on your device, you can press and hold the address bar to bring up an option that moves it to the lower edge of the screen. There’s also a setting inside the browser’s menu where you can make the same change, if you prefer to adjust it there.

For Android users, the address bar has remained in the same place for more than a decade. Chrome first launched on Android back in 2012, and in all that time, the bar at the top has been the usual way to browse. Although Google has changed and improved some things over the years, like letting the bar disappear when you scroll upwards to create more space on the page, the position of the bar itself has stayed the same.

For some, reaching the top of the screen can be awkward, particularly when using the phone with one hand. Moving the bar to the bottom might help with that. It’s not really a new idea. Google has tried this approach before, though in those earlier tests, the feature never became a regular part of Chrome for Android.

Interestingly, people using Chrome on iPhones have already had a similar choice. On iOS, it’s possible to switch between a top and bottom bar, either by pressing and holding the bar or by using the browser’s settings. That version has offered this flexibility for a while.

It might come as a surprise that Google is only now bringing the option properly to Android. Other browsers added it quite some time ago. The Windows Phone, which has long disappeared from the market, was already giving users a bottom bar back in 2012. Apple’s Safari browser introduced a similar change in 2021, letting people move the bar for easier access.

Even so, this update means Chrome for Android is now catching up, giving people the chance to choose a layout that works better for them. For many users, having that choice may make everyday browsing feel a bit more comfortable.

Read next: AI Firms Face New Legal Boundaries as Court Differentiates Fair Use from Copyright Theft
by Irfan Ahmad via Digital Information World

AI Firms Face New Legal Boundaries as Court Differentiates Fair Use from Copyright Theft

A recent court ruling has added another layer to the growing debate over how artificial intelligence companies use copyrighted materials to train their systems. Judge William Alsup, presiding over a case involving the AI firm Anthropic, has clarified where the legal lines may be drawn, at least for now. His decision made it clear that using legally purchased books to train large language models falls under fair use, but building datasets from pirated books crosses into territory that’s still firmly against copyright law.

The ruling, which is already stirring conversation across the tech and legal communities, stems from a class-action lawsuit filed by authors who alleged that Anthropic had used their works without permission to develop the Claude series of AI models. While Alsup dismissed parts of the authors’ claims, he agreed that Anthropic’s practice of collecting vast numbers of pirated books to expand its training library cannot be justified. The company now faces a possible financial penalty for that aspect of its operations.

Alsup’s decision rests on the view that when someone buys a book and uses it to train an AI, it’s no different from a person reading that book and learning from it, and that process, in itself, doesn’t harm the author’s rights. What the judge seemed to endorse is a view that the act of transforming knowledge from purchased texts into machine learning models represents a legitimate form of learning, not an act of duplication that damages the book’s commercial value. It’s a perspective that resonates with those who see AI as just another tool capable of absorbing information and generating new content in much the same way that humans do.

Yet, there’s a hard stop when it comes to piracy. Anthropic had reportedly downloaded millions of unauthorized books to accelerate training and retain as reference material, and here the judge took a much less forgiving stance. The court didn’t buy the argument that saving costs or moving faster justified sidestepping the law. While training AI systems on pirated content might technically create transformative outputs, that doesn’t erase the fact that the underlying copies were obtained illegally. The case is now moving toward a phase where financial damages could be determined.

Interestingly, the public and experts reaction has been far from one-sided. Some critics quickly pointed out that this ruling could theoretically enable anyone to train AI systems on even the most expensive textbooks, provided they acquire them legally. Others were more cautious, reminding that piracy remains an independent violation regardless of how the materials are later used. The line between acceptable training practices and copyright infringement seems clearer now, but the moral and practical questions surrounding it have hardly disappeared.

For many, this decision raises larger concerns about whether AI companies, especially the biggest names in the field, are consistently acquiring their training data in lawful ways. There’s a lingering suspicion that while some firms cut licensing deals with publishers, others may have quietly built portions of their datasets by pulling from unauthorized sources. If proven, those practices might not unravel the models themselves, but they could still result in significant legal consequences.

The ripple effects from this ruling may not stop with Anthropic. Companies like Meta and Google, which have also been accused of using questionable data sources, could find themselves under closer scrutiny. And if those firms did rely on pirated works at scale, they might soon face similar courtroom battles.

There’s also an unresolved question about what happens when AI outputs mirror the training materials too closely. Alsup’s decision focused on the legality of the training process, but did not weigh in on whether specific outputs could infringe copyright. It’s not hard to imagine future cases where the material generated by an AI system is challenged for being too close to the original sources it ingested. This grey zone, whether AI-generated responses can themselves become a substitute for the original works, is likely to become the next major front in the copyright wars.

For those hoping that this ruling opens the floodgates for easy access to information through AI, some might have to temper their enthusiasm. While it’s now clearer that using purchased books for training is protected, the industry’s habit of mixing in pirated works remains a serious liability. It’s a significant distinction, and one that some of the most vocal online reactions seem to have missed or oversimplified. The debate about what’s fair use and what’s theft has been further complicated by this case, but the judge’s message was fairly direct: how the data is obtained still matters.

The practical effect of this ruling could be a push for more transparency about the datasets these companies use. If corporations continue to quietly rely on pirated material to build stronger models, they may eventually face the same kinds of accountability that individual users have long endured for much smaller offenses.

Some critics are now wondering whether this will encourage companies to brazenly harvest more content under the assumption that they can settle any disputes later with relatively manageable fines. This approach could deepen the divide between major tech players, who can absorb legal costs, and smaller developers or researchers who lack those resources and may now find themselves shut out of AI innovation.

There’s also a wider cultural question forming around the idea of fairness. For years, ordinary people have faced legal threats for downloading movies or textbooks without paying, yet it appears that some of the world’s largest companies may have built parts of their AI empires on the same type of behavior, only on a far grander scale. For many, that’s a difficult contradiction to accept.

And while the ruling may seem like a green light for AI development in some respects, it doesn’t fully settle the ethical tensions at the core of this issue. Questions about how AI will reshape access to information, the boundaries of intellectual property, and the obligations of tech companies to creators remain as pressing as ever.

Looking ahead, this case is likely just one step in a much longer legal journey. Other lawsuits are already working their way through the courts, and many expect that sooner or later, the most contested issues around AI training and copyright will end up before the Supreme Court.

Until then, companies, creators, and the public will continue navigating this unsettled landscape, where the lines between innovation and infringement remain anything but clear.

Image: DIW-Aigen

Read next: The Smartphone Habit People Just Can't Stand, And It’s Not What You Think
by Irfan Ahmad via Digital Information World

The Smartphone Habit People Just Can't Stand, And It’s Not What You Think

These days, people argue about tech nonstop, yet somehow one thing still unites most of them. Some habits just seem to get under everyone’s skin.

It’s common to see people standing in grocery store lines, chatting loudly on speakerphones, or playing music from their phones on crowded subway trains, no headphones, no effort to keep it private. It happens all the time, especially in big busy cities where shared spaces never seem quiet. Still, even now, plenty of people carry on without giving it a second thought.

A recent PCMag survey (which was originally carried out by YouGov in May 2025) asked more than two thousand adults in the United States about how they feel when people use phones in public spaces. Three out of four said they believe it’s wrong to take speakerphone calls or start video chats without headphones in places like supermarkets and coffee shops. But strangely enough, almost one in four people said they’re fine with it.

Not everyone agrees on this. In fact, around 20% of people said playing music out loud in public is also totally acceptable. Some might say this shows how much public manners have loosened over time.

Age really seems to shape these opinions. For example, most older adults, especially Boomers, find this behavior completely inappropriate. Younger folks, particularly Generation Z, often seem a lot more relaxed about it. Maybe it’s because they’ve always had smartphones around them. Maybe it’s because the way they communicate feels different. Whatever the reason, they don’t seem to mind sharing their phone noise with strangers.

Older generations probably grew up thinking about public manners in a very particular way, don’t disturb people, don’t make a scene, that sort of thing. Younger people, though? They seem to care more about convenience and what feels natural to them.

But phones aren’t just about loud calls and music. There’s also the matter of privacy. Most adults in the survey said that snooping on someone’s phone, like checking a partner’s device without asking, is definitely not okay. Around 84% said this is where they draw the line. Though, interestingly, nearly one out of four Millennials said they think it’s acceptable.

And when it’s not a partner but a friend or family member? People get even stricter. About 92% said looking through someone’s phone in those situations is unacceptable.

Some people might say that in close relationships, things get blurry. People feel more entitled to look. Maybe it’s about trust. Maybe it’s just curiosity. But with friends or family, that line seems a lot harder to cross.

The survey didn’t stop there. It also asked about using AI tools (like ChatGPT) to write texts or emails. This really split people. A little more than half of those surveyed said they’re not comfortable with it. But among Gen Z and Millennials, about half said they’re completely fine with it. Maybe they just see it as a smart way to save time.

Older adults often seem to think using AI for messages feels like cheating. But this view might not stick around forever. AI is showing up everywhere, so some experts think people will get used to it. Probably sooner than we think.

The survey also revealed some other habits that make people pause. For instance, three out of four adults said it’s rude to text or email while talking to someone face-to-face. Gen Z, though, seems to feel differently, about 40% said they don’t see a problem with it.

Then there’s phone use in bathrooms. Gen Z leads the way here. Almost half of them think taking selfies or mirror photos in the bathroom is totally fine, especially if the lighting’s good. Older generations? Most still say it’s a bad idea.

Another fact to note is that most people from all generations said they don’t like it when strangers get recorded or photographed without permission. Even so, around 20% of people in every age group said they’re okay with it. Opinions, as always, are mixed.

It’s pretty clear that younger people are shaping new rules for how phones fit into daily life, though most adults still expect some basic level of politeness and privacy when it comes to technology.

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• How to Find Someone Using Just a Photo: 10 Best Reverse Image Search Tools (Ranked and Explained)
by Irfan Ahmad via Digital Information World

U.S. House Staff Ordered to Delete WhatsApp Amid Rising Cybersecurity Concerns, Meta Pushes Back

Government staff working in the U.S. House of Representatives have been directed to remove WhatsApp from official devices, as internal cybersecurity teams raise red flags over the app’s data handling and security architecture. The order, issued by the House’s Chief Administrative Officer (CAO), signals a broader shift in how federal bodies evaluate the tools their employees use to communicate.

According to internal guidance sent to congressional staff, WhatsApp must be deleted from all work-related phones and computers. The Office of Cybersecurity has identified the platform as a potential threat, citing unresolved questions around data transparency, how long user information is retained, and the limited visibility into the platform’s internal security systems.

Although WhatsApp markets itself as a secure messaging service, with end-to-end encryption enabled by default, experts have pointed to gaps in how the system functions behind the scenes. Critics argue that while message contents may be protected, other forms of metadata, like communication timestamps or contact networks, could be exposed or misused. The CAO’s decision appears to be driven less by fears of message interception and more by the possibility that external actors could map out communication patterns among House staff.

• Also read: How Meta’s Four Social Media Platforms Divide Our Time Without Stealing from Each Other

Meta, which owns WhatsApp, pushed back hard against the directive. Company representatives argued that the app offers stronger security than several alternatives currently approved for official use. They emphasized that encryption remains intact, and reiterated that both House and Senate members have used the service regularly without incident.

Still, recent incidents have intensified scrutiny. Earlier this year, Malaysia’s home minister reportedly had his WhatsApp account compromised through a phishing attempt. Around the same time, state-controlled media in Iran warned citizens to delete the app, claiming, without clear evidence, that it was leaking data to foreign entities. While Meta has denied those claims and pointed to the strength of its encryption, such headlines have added fuel to an already heated conversation about digital trust.

Another sticking point for U.S. cybersecurity officials is the limited access researchers and regulators have to WhatsApp’s backend processes. Although the service is built on the well-known Signal Protocol — open-source and widely respected—the company does not offer full transparency into how it implements or modifies that framework. Critics have argued that a tool used widely in high-security environments should allow deeper independent review.

As tensions between Meta and regulators continue to rise, the timing of the ban may also carry political undertones. The company is already in the middle of a legal battle with the Federal Trade Commission, which is challenging Meta’s past acquisitions (including WhatsApp) as part of an ongoing antitrust lawsuit. At the same time, Meta is working to monetize WhatsApp more aggressively, having just rolled out ads inside the app in some markets.

For now, the House’s security teams recommend staff rely on apps like Signal, iMessage, or Microsoft Teams for official messaging. Whether WhatsApp can regain its footing in the government’s tech stack may depend on how convincingly it can address the privacy concerns at the heart of this ban.

Image: DIW-Aigen

Read next: How to Find Someone Using Just a Photo: 10 Best Reverse Image Search Tools (Ranked and Explained)
by Irfan Ahmad via Digital Information World

How to Find Someone Using Just a Photo: 10 Best Reverse Image Search Tools (Ranked and Explained)

Online profiles come and go, faces flash by on social media, and sometimes a single photo leaves you wondering who that person really is. Maybe you met at a conference, saw a familiar face on social media, or received an image from someone or a location you barely know. In any case, finding out who they are, or where else that photo appears online, is more possible today than ever before.

Reverse image search tools allow you to upload a photo and scan the internet for matches, similar visuals and locations, or linked content. But not every tool works the same way, and some are far better at identifying people than others. Below, we’ve ranked the most useful tools for finding people using just a photo, along with their key strengths, limitations, and ideal use cases.

1. Google Lens

Website: lens.google

What it does best:

Google Lens analyzes the visual elements of an image and connects them to relevant search results. While it’s not built strictly for reverse face image searching, it’s remarkably effective, especially when the person in the photo appears on public websites or social media.

Why it's first on this list:

Unlike standard image search tools, Lens uses AI to interpret context. If you upload a selfie, it won’t just look for visually identical pictures, it may identify where the photo was taken, spot background elements, or even pull up social media profiles tied to that face.

Best used on:Android devices, Chrome browser, or Google Photos.

Pros:

• Excellent at recognizing people, places, and objects
• Connects to Google's full search index
• Handles partial images and screenshots well

Cons:

• Results vary with obscure or private individuals
• Better on mobile than desktop
• Doesn’t guarantee facial recognition accuracy

Start here if you’re using a phone or want context-based results. It’s smart, fast, and surprisingly accurate when the photo is public or shared online.

2. Google Image Search

Website: images.google.com

What it does best:

This is the traditional reverse image search engine. Upload a photo or paste a URL, and Google scans the web for exact matches and visually similar images.

Why it ranks high:

It’s broad and free. If the person in the photo has been featured online — through blog posts, media coverage, public directories, or forums, this tool can often surface it.

Pros:

• Indexes billions of web pages
• Works well with clear, high-res photos
• Easy to use on desktop

Cons:

• Can struggle with profile photos used only in private accounts
• Doesn’t interpret context beyond pixels

It’s a classic tool. For finding where an image has been reposted, or for spotting duplicates, it’s still one of the best — just don’t expect deep context.

• You might like: Our free Image Format Converter: PNG To JPG, WebP or AVIF to PNG and JPEG Tool

3. Yandex Image Search

Website: yandex.com/images

What it does best:

Yandex, Russia’s largest search engine, is renowned for its facial matching capabilities. It often finds matches that Google misses — especially when it comes to faces reused across obscure platforms or less-indexed parts of the web.

Why it deserves a spot near the top:

Its facial recognition strength makes it particularly useful when other tools fail. Even if someone changes their profile picture slightly, cropping, filters, or minor edits, Yandex can sometimes still detect it.

Pros:

• Superior facial matching compared to Western search engines
• Finds matches on non-English platforms
• Effective for older or repeated photos

Cons:

• Interface partly in Russian
• Results may include content from unrelated domains
• Not ideal for users concerned about data jurisdiction

If you’ve tried Google and come up short, Yandex is often your best fallback. It’s surprisingly sharp at identifying people, even when the photo is lightly altered or buried on foreign-language sites.

4. TinEye

Website: tineye.com

What it does best:

TinEye specializes in finding exact image matches. It's not a face-finder, but if you're trying to trace where a specific image has been used online - or whether it’s been stolen or misused - TinEye is ideal.

Why it’s valuable in people searches:

If a person’s photo has been copied or shared across different websites, TinEye will find each instance. This is especially helpful when trying to identify the origin of a professional headshot or checking for impersonation.

Pros:

• Finds exact matches quickly
• Useful for spotting photo misuse
• Doesn’t save uploaded images

Cons:

• No facial recognition
• Doesn’t detect altered or similar images
• Smaller index compared to Google or Yandex

Use TinEye if you want to track how and where a particular photo has been used, not to find someone’s identity directly, but to trace image reuse.

5. Baidu Image Search

Website: image.baidu.com

What it does best:

Baidu is China’s dominant search engine, and its image search feature is particularly effective for Chinese-language content. If you suspect a photo originated from platforms like WeChat, Douyin, or local news sites, Baidu is essential.

Why it’s on this list:

It provides access to regions and sources that Google doesn’t index well. That includes Chinese social media, marketplaces, and local blogs.

Pros:

• Searches Chinese platforms that Western tools miss
• Good for regional content discovery
• Can reveal original photo usage in East Asia

Cons:

• Interface is in Chinese (can be translated, but clunky)
• Results are limited to China-based websites
• Useless for Western or global searches

Highly valuable for regional queries, especially if the image ties to China, but not practical for global users or English-language searches.

6. Bing Visual Search

Website: bing.com/visualsearch

What it does best:

Bing’s visual search allows users to upload a photo and get results ranging from similar images to product matches and contextual pages.

Where it fits in:

It’s not as sophisticated as Google or Yandex, but it’s still worth checking — especially for product photos or public content.

Pros:

• Integrated with Microsoft Edge browser
• Works well for object and product recognition
• Easy to use

Cons:

• Weaker facial recognition
• Less comprehensive index compared to Google
• Limited accuracy with obscure images

Bing Visual Search isn’t groundbreaking, but it’s a decent secondary option if other tools don’t work. Best used when you’re looking for photos tied to public-facing websites.

7. PimEyes

Website: pimeyes.com

What it does best:

PimEyes offers AI-powered facial recognition. You upload a photo, and it tries to find other photos of that face across the web, even if the images are edited, cropped, or embedded in articles.

Why it's controversial:

It’s powerful, but also privacy-sensitive. It’s been criticized for potentially enabling misuse, especially in the absence of consent. It’s also not free, searches are limited without a subscription.

Pros:

• Highly accurate facial recognition
• Finds edited, cropped, or low-res matches
• Good for journalists, investigators, or fraud detection

Cons:

• Paid subscription required for full access
• Privacy concerns due to scope and accuracy
• Not suitable for casual users

This is a professional-grade tool, not a casual search engine. Use it carefully and ethically, it can find what other tools miss, but comes with serious responsibility.

8. Pinterest Lens on mobile (or Visual Search on PC)

Website: pinterest.com

What it does best:

Pinterest’s visual search is built for discovering similar images inside the Pinterest ecosystem. It’s not designed for identifying people, but it can still help you find related styles, some popular celebrities, fashion looks, or settings.

Why it makes the list:

If you're trying to trace an image that looks like it came from a design blog, fashion shoot, or Pinterest board, this tool might locate it.

Pros:

• Great for aesthetic and style-related image matching
• Finds source boards and visually similar pins
• Easy mobile experience

Cons:

• No facial recognition
• Doesn’t connect to external websites
• Useless for identity searches

This is more for visual inspiration than people search. Only use Pinterest Lens if the image looks like something from a mood board or a lifestyle blog.

9. ChatGPT (with image input)

Website: chatgpt.com

What it does best:

While ChatGPT isn’t a traditional reverse image search engine, the newer versions (with image input enabled) can analyze a photo and help identify locations, landmarks, languages on signs, and sometimes even contextual clues that suggest where a photo was taken.

It doesn’t crawl the internet for visual matches like Google or Yandex, but it’s extremely useful for interpreting what’s inside a photo, especially if you’re dealing with scenery, architecture, or street-level details and want to narrow down a location.

Use cases include:

• Identifying where a photo was taken based on architecture, terrain, or signage
• Reading visible text or symbols in the image (e.g., street signs, storefronts)
• Suggesting likely regions or countries based on visual cues (cars, languages, styles)
• Extracting details for further searching in Google Maps or other tools

Pros:

• Can analyze and describe photo contents in detail
• Useful for narrowing down a location, especially with no metadata
• Helpful as a first-pass before deeper manual searches

Cons:

• Doesn’t search the web for matches
• May not provide exact location without recognizable features
• Not a replacement for a proper reverse image search engine

ChatGPT with image input is best used as a visual assistant — it won’t tell you who someone is, but it can give strong hints about where they were when the photo was taken. That makes it a powerful companion tool alongside other reverse image platforms.

10. FaceCheck.ID

Website: facecheck.id

What it does best:

FaceCheck.ID is a facial recognition search engine designed to match a person’s face against a large index of publicly available images from the internet. The tool is geared toward helping users verify identities and uncover potential online presence linked to a specific face, even across forums, news sites, adult platforms, and public social content.

It works by scanning the facial features in your uploaded image and comparing them to its massive image database. While not as well-known as PimEyes, it offers a similar type of visual face-matching technology, often surfacing image matches from sites that aren’t always well indexed by mainstream engines like Google.

Use cases include:

• Identifying whether a profile photo is linked to multiple online identities
• Investigating potential impersonation or fraud
• Discovering whether someone’s face appears in unexpected or questionable places online

Pros:

• Strong facial recognition engine, even with small or low-quality images
• Searches across less mainstream platforms, forums, and websites
• Emphasizes public safety and fraud prevention use cases
• Free searches available with optional premium access

Cons:

• Privacy concerns, very sensitive tool if misused
• Paid tier required for full resolution image results
• Results are limited to faces already visible online

Bottom line:

FaceCheck.ID is a serious tool for facial search and digital footprint discovery. It’s particularly effective for safety checks, verifying unknown contacts, or researching online presence across multiple platforms. Like all facial recognition tools, though, it should be used responsibly, not to intrude, harass, or overstep privacy boundaries.

Final Thoughts

Reverse image search can be surprisingly powerful, but it isn’t foolproof. The best strategy is to use a combination of tools. Start with Google Lens or Google Image Search, then try Yandex if those fail. If you’re working with Chinese content, Baidu can open new doors. For tracing where a photo has been reposted, FaceCheck still delivers. And if you're operating in investigative or professional settings, PimEyes is worth considering, with care.

Just remember: while technology can reveal a lot, it also comes with ethical boundaries. Use these tools responsibly. They're best used to verify identities, reconnect with people, or understand context, not to violate privacy.

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• These Are the Best AI Video Generators for Creating Stunning Content in Minutes

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• Want to Edit Videos Like A Pro? Use these Best Free and Paid Video Editing Tools in 2025

by Irfan Ahmad via Digital Information World

How Meta’s Four Social Media Platforms Divide Our Time Without Stealing from Each Other

In Meta’s Android ecosystem, four apps compete for attention, but not in the way most think. They don’t cannibalize one another. Instead, each carves out its own niche in the rhythm of daily life, serving different instincts, connection, curiosity, habit, and history.

Start with WhatsApp. The numbers here aren’t just large, they’re consistent. Every month, 1.41 billion people use it on Android devices. That drops only slightly across the week (1.36B) and barely shifts each day (1.25B). That kind of retention, sitting at 88.86% daily stickiness, doesn’t come from features, it comes from necessity. With 20.99 sessions per user and an average session lasting just under three minutes, it’s quick, frequent, and woven into life’s in-between moments.

Instagram, by contrast, moves slower, but holds tighter. While its user base (on Android) is smaller (937.54M monthly, 840.17M weekly, 666.27M daily), each visit pulls longer. An average session clocks 5 minutes and 28 seconds, and users average 12.38 sessions per day. Cumulatively, that builds to 1 hour and 7 minutes of daily presence. If WhatsApp feels like a hallway conversation, Instagram is a lounge, users stay, browse, linger.

Facebook, the original titan, operates on legacy momentum. It has 1.08B monthly users, 970.72M weekly, and 774.49M daily. But while fewer sessions occur (9.39 per user), those who arrive don’t rush. An average session stretches nearly seven minutes, longest of the group, and adds up to over 65 minutes of daily usage. At 71.68% stickiness, it’s not as addictive as WhatsApp, but it holds a loyalty rooted in familiarity. For many, Facebook remains the internet’s waiting room.

Messenger seems caught in a different story. It still draws a sizable crowd (746.58M monthly, 579.08M weekly, 348.65M daily), but the numbers tell of an app fading quietly. Stickiness lands at just 46.69%, well below the others. Session count sits at 9.32 per day, with the shortest average span, just over two minutes. Daily total? Barely crosses 18 minutes. That’s not a collapse, but it is drift. People still use it, but less with urgency, more out of leftover habit.

The spread tells a bigger truth. Meta didn’t build one all-powerful app. It built four that cover different tempos. WhatsApp thrives on rapid-fire messages, Instagram on visual wanderings, Facebook on deeper scrolls, and Messenger... well, it endures.

What matters isn’t just who logs in, it’s how they move. Across these four apps, time splits cleanly, shaped by what users seek, not what Meta forces. And that, more than any growth chart, shows just how tightly these platforms still grip the modern day.

Metric	WhatsApp	Facebook	Instagram	Messenger
Weekly Active Users	1.36B	970.72M	840.17M	579.08M
Daily Active Users	1.25B	774.49M	666.27M	348.65M
Monthly Active Users	1.41B	1.08B	937.54M	746.58M
Daily Stickiness	88.86%	71.68%	71.06%	46.69%
Sessions per User	20.99	9.39	12.38	9.32
Avg. Session Time	00:02:51	00:06:59	00:05:28	00:02:01
Total Session Time	00:59:48	01:05:34	01:07:39	00:18:47

Data H/T: Similarweb.

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by Irfan Ahmad via Digital Information World