Thursday, September 10, 2026

Record Attacks on Education Disrupt Learning for Millions of Children Worldwide

By United Nations. Fact-Checked by Irfan Ahmad.

Image: Antoine Demare - Unsplash

While September traditionally marks the start of the school year across the northern hemisphere, a staggering 93 million children are not in class at all amid an unprecedented number of attacks on learning, education experts warned on Wednesday.

According to global fund Education Cannot Wait, insecurity and other factors mean that a total of 258 million children across 87 countries face severe learning disruption.

Marking the International Day to Protect Education from Attack, the fund cited a backdrop of “increasing conflict, decreasing restraint and eroding global norms” in several regions.

Easy prey for traffickers

“When a child is denied schooling for years, the consequences are immediate and profound: it means forgotten literacy, lost routines and a future dictated by recruiters or traffickers, rather than teachers,” said Maysa Jalbout, Director of Education Cannot Wait, the global fund for education in crises.

According to a report partnered by the fund, attacks on education and the use of schools for armed conflict reached a record high in 2024 and 2025. The number of students and staff killed, injured, abducted, or otherwise harmed has never been higher, either.

In the 28 countries profiled, education was targeted in attacks, or schools and universities were used for military purposes.

In all, there were at least 9,259 attacks on education (including more than 3,600 on schools), representing a more than 50 per cent increase compared to 2022-2023.

In addition, more than 10,600 students and educators were killed, injured or abducted, while the military use of schools nearly doubled.

“Education in emergencies is about more than keeping children in school. It is about protecting them at their most vulnerable: offering safety, stability, a sense of normalcy, and a path toward a future beyond crisis,” said Education Cannot Wait chief Ms. Jalbout.

Laws of war

UNESCO, the UN education and cultural agency, stressed the obligation for all parties to conflict to respect international law and spare education from attack.

The agency noted that nearly eight in 10 of all out-of-school and crisis-affected children – some 74 million – live in just 20 countries. Within these settings, the barriers to learning are steepest for the most vulnerable: 74 per cent of refugee children, 52 per cent of internally displaced children, and 45 per cent of children with disabilities are out of school.

Children in danger

Latest UN data on children caught up in conflict indicate that 24,174 children were impacted in 2025, out of 38,558 verified violations.

Killing and maiming were among the most widespread violations, affecting 14,224 children. Youngsters were also recruited and used in conflict, abducted and denied humanitarian assistance, according to the UN Secretary General’s Annual Report on Children and Armed Conflict .

The report highlights how fighting increasingly took place in densely populated areas, exposing children to airstrikes, artillery and explosive drones. Homes, schools, hospitals and other civilian infrastructure were damaged or destroyed, while children faced danger fleeing violence or seeking food, water and medical care.

The highest number of grave violations was verified in Israel and the Occupied Palestinian Territory, followed by the Democratic Republic of the Congo, Nigeria, Myanmar and Somalia.

For the first time, Government forces were responsible for the majority of verified grave violations.

The UN along with its many partners believe firmly that education protects children in crisis from harm, while giving them the skills to rebuild their communities in future.

Education Cannot Wait’s Hope Starts Here campaign seeks $600 million to ensure that 10 million children “in the world’s toughest places” can access safe, quality education.

Originally published by UN News; republished under UN Rights & Permissions.

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The True Cost of AI Without Guardrails

Large Language Models Are Pushing the Web Toward Zero Clicks
by External Contributor via Digital Information World

Wednesday, September 9, 2026

Large Language Models Are Pushing the Web Toward Zero Clicks

By UCLA Anderson Review

A picture of how chatbot adoption leads to fewer web searches — and how that affects online publishers.

Image: Compagnons - Unsplash

Fewer than 1 in 3 Google searches in the U.S. still ends with a click on a blue link. According to SparkToro’s analysis of Similarweb data, in the first four months of 2026 as much as 68% of U.S. Google searches ended without the user clicking through to a publicly available website.

You’ve probably read news accounts of the fallout. Business Insider’s organic search traffic fell 55% between April 2022 and April 2025, leading the news organization to cut 21% of staff. Vox Media began selling parts of its business in May 2026, due to the “decimation of search traffic.” And The Planet D, a travel blog, lost half its traffic in 2024 shortly after Google launched AI overviews, the AI-generated text summaries that appear at the top of some Google search pages.

“For publishers, Google Zero is already here,” Nilay Patel, editor-in-chief of The Verge, told The New York Times. Google Zero refers to a future when the search engine is no longer the gateway to the web, sending no traffic outward.

The mechanism of the web traffic collapse is clear. When people receive a summary for their query, they tend to stop searching. Pew Research Center tracked almost 70,000 Google searches by 900 adults in the U.S. in March 2025. Whenever an AI summary appeared, they found that users clicked a traditional result (a blue link) only 8% of the time. When no summary appeared, users clicked 15% of the time. They abandoned the session entirely on 26% of searches that contained a summary versus 16% without.

Watching as Everything Changes

Google used to be a directory that found the address where an answer lived and directed you how to get there. That trip was the business model of the open web. It spawned a gigantic industry (search engineer optimization) essentially to get websites onto the first page of Google results and forced businesses big and small to play the SEO game. But AI overviews have thrown a wrench into the works.

Just as this sea change began, four marketing researchers studied how it was happening. A working paper by London Business School’s Nicolas Padilla, UCLA Anderson’s H. Tai Lam and Brett Hollenbeck, and London Business School’s Anja Lambrecht investigated what happened as people began to adopt large language models.

The researchers tracked the desktop browsing of over 1 million U.S. users across 2022 and 2023, using the Comscore Web-Behavior Panel. In that panel, they found 2,041 users who began using ChatGPT, Bing Chat (now Copilot) or Bard (now Gemini) in the tools’ first year, and followed what became of the rest of their internet use.

This was a window into the early days of chatbots, given that ChatGPT was only introduced at the end of November 2022. The data documents use of first-generation tools, all of which invented facts freely. Nonetheless, people still began building a new habit around them.

Nothing — for Five Months

Padilla, Lam, Hollenbeck and Lambrecht defined LLM adoption as making at least one chatbot visit per week over three consecutive weeks. Because people began using the LLM-powered chatbots at different points across the year, those who had not yet started served as the comparison group for those who had.

The researchers’ first finding was noteworthy. Nothing happened.

For about five months, users’ search behavior was indistinguishable from before LLM adoption on a statistical basis. People had a new tool, used it every week, and went on googling pretty much as they always had.

The decline in their search traffic began around week 20. And it didn’t stop. Searches in traditional engines fell 21.7% relative to their pre-adoption average. This differs from the traffic reports for websites above since it measured how much less a single user searched, but a modest change per person is what becomes a severe decline per website once most of the audience has made it.

The authors interpret the 20-week delay as a learning period. It may represent time spent by users to discover which questions a chatbot handled well, and only after that experimentation did they stop double-checking answers with a web search. The data showed searches built on question words (how, what, why, where, when, which, who) fell sharply. The navigational searches where someone typed a website name to find it, held steady. This suggests users tended to keep using web search as an address book but stopped asking it questions.

Total browsing, which the researchers measured as the calls a browser makes to load pages, barely changed, but its distribution shifted enormously. If the dataset was sorted by most website traffic and cut into four equal slices, the top quarter of all web traffic included just seven websites, while the bottom quarter was spread across some 4.5 million. Traffic to the top 500 websites showed no significant decline, while calls to the generally small websites in the bottom quarter fell 41.8%.

Traffic to display-ad servers — these are the visual banners and images placed on third-party websites — fell significantly after LLM adoption. The drop was concentrated across users who shopped online the most, and this is the very audience advertisers pay premiums to reach. Search ad exposures were a different story. These ads continued riding the navigational queries that never went away, so they held steady.

Keeping the Questions

The study’s LLM adopters stopped entering their questions into Google’s search box and just entered them into the chatbot. Google noticed and, to get them back, it made its search box work like a chatbot, answering questions.

Clickstream data from Datos, a Semrush company, shows query length moving away from short keyword entries. The fastest growing query length was in the six- to-nine-word range and suggests people phrasing searches as though prompting a chatbot. With AI overviews appearing on 57.9% of question queries, they basically were prompting a chatbot. Ahrefs found that approximately 46% of queries containing seven words or more triggered an AI overview, compared with about 9.5% of single-word queries.

From this data, it’s plausible to make the interpretation that Google has been absorbing some chatbot-style behavior by answering longer, question-shaped searches directly. Since AI overviews require no special tool adoption, they may also shorten or eliminate some of the learning period observed in the study. Meaning people adopt LLM use faster. Also, the LLMs have gotten more accurate.

These changes could be why the 2023 web traffic decline looked survivable and 2024 did not. In 2023 the visible casualties were sites whose whole function was substitutable. These included Stack Overflow and the homework-help company Chegg, whose stock collapsed that May. Those websites served users who overlapped almost exactly with the tech savvy and the students who were early adopters of LLMs. For other websites, the adopters were too few and scattered to register at first.

Padilla, Lam, Hollenbeck and Lambrecht’s study noted that Wikipedia appeared to be unaffected in 2023. The authors suggested that its survivorship could be credited to being an authoritative source that a model that invents facts cannot replace. In 2025, though, Wikimedia Foundation reported human page views to be down roughly 8% year-over-year, something they blamed on generative AI and social media.

What Google Says

Google’s defense is that AI overviews don’t appear on every search. True, as far it goes. AI overviews appear on about a fifth of results pages. Where they don’t appear is mostly where a summary wouldn’t be helpful. For example, type in a navigational query like “reddit” and you get Reddit’s website instead of a summary. Of the keywords that do trigger AI overviews, 99.9% are informational and 0.1% navigational. Where AI overviews do appear is for the very searches the open web was built to answer.

Google’s answer-in-place may be moving into query types it once left alone. Semrush, tracking more than 10 million keywords through 2025, found the informational share of AI overview triggers falling from 91.3% in January to 57.1% by October, while navigational triggers rose from 0.84% to 10.33%.

Organic search’s share of traffic at The New York Times slid to 36.5% in April 2025 from almost 44% three years earlier; Dotdash Meredith went from about 60% of its traffic arriving from Google in 2021 to roughly a third.

AI overviews are comparatively rare on news queries, which is Google’s standing rebuttal, but Seer Interactive found that even queries without an AI overview lost 41% of organic click-through year-over-year. The cause of that change is unclear.

Corresponding to what the researchers’ study found regarding display ads falling while search ads did fine, Alphabet’s third-quarter 2025 report showed revenue declining year-over-year in Google Network, which places ads on other companies’ sites, while search advertising grew.

For small creators, the losses are becoming terminal rather than just painful. Bloomberg reported that at least three businesses invited to a Google creator summit in October 2024 have since shut down. Raptive, which represents 5,700 creators, estimates that sites will lose a quarter of their traffic to AI overviews.

Google prefers to talk about total search volume. Liz Reid, its head of search, wrote in August 2025 that click volumes remain “relatively stable” in the billions and that reports of decline rest on “flawed methodologies.”

On June 3, 2026, Britain’s Competition and Markets Authority ordered Google to attribute publisher content with clear links inside AI answers and to let publishers opt their content out of being used in Google Search’s AI features altogether. However, Google has up to nine months to implement it. The order followed the CMA’s designation of Google as having strategic market status. This is a finding that the company has substantial and entrenched market power in a strategically significant UK digital activity, not that it broke the law.

Regulators aren’t the only ones putting pressure on Google. Bloomberg columnist Parmy Olson reports that Cloudflare, the web infrastructure giant, intends to block Google’s mixed-use crawlers by default beginning in September 2026 for ad-supported customers.

Websites have been in an unfair situation for years. If they blocked Google’s bots to prevent their content from being used for AI training or AI answers, then they would vanish from search results. The mounting regulatory and private company pressure has pushed Google to offer a concession. They are now testing an opt-out feature in the UK that enables websites to block AI scraping without sacrificing their search rankings. The feature will eventually be rolled out globally.

The effect of the order and the opt-outs are yet to be seen. For now, the researchers’ work appears to have captured a glimpse of how LLM-powered chatbots substitute for the web, rather than feed it. What they discovered based on data ending in 2023 holds even stronger three years later.

Edited by Irfan Ahmad.

Read next: 

• The True Cost of AI Without Guardrails

• Apple, Samsung, Xiaomi and Google Phones Come Loaded With Apps, Here’s Which You Can Remove
by External Contributor via Digital Information World

Apple, Samsung, Xiaomi and Google Phones Come Loaded With Apps, Here’s Which You Can Remove

By Surfshark

Apple, Samsung, Xiaomi and Google phones come with numerous pre-installed apps, but many can be removed.
Image: Surfshark

Brand new smartphones are packed with pre-installed applications, but this convenience often comes at a hidden privacy cost. From manufacturer utilities to pre-installed third-party apps, many of them may never be used and remain silently active behind the scenes. The reality is that such bloatware increases data harvesting and may collect personal information you never intended to share. This study takes a closer look at what’s really lurking on your home screen.

Each new iPhone comes with up to 50 built-in apps, all developed by Apple. Across the 47 apps available for download on the App Store that disclose their data collection practices, Apple gathers an average of 8 data types per app. This includes location-category data, such as approximate location (19 apps), precise location (8 apps), or physical addresses (5 apps). It also encompasses behavioral data or user content, alongside sensitive information. The apps collecting the most data are the Apple Store app (used for shopping Apple products) and Apple Music, gathering 18 and 17 out of 35 data types respectively — more than double the average for built-in apps.

Most of Apple’s built-in apps can be deleted, though availability varies by region. EU users can remove up to 98% of built-in apps, compared to 88% in other countries. For example, Safari can be uninstalled in the EU or Japan, but not in other regions.¹ In the EU, the Phone app is the only built-in app available for download on the App Store that users cannot delete, meaning that other built-in apps can be replaced with alternatives. Settings cannot be deleted either, although it is unavailable on the App Store. Removing unnecessary apps frees up device storage, makes a cleaner interface, and helps narrow the overall threat scope. Additionally, switching to third-party apps keeps user data from being concentrated in a single ecosystem. This reduces privacy risks and limits user-profiling capabilities.

Samsung smartphones come preloaded with apps from Samsung, Google, mobile carriers, and third-party partners. In Bayton’s database of voluntarily synced Samsung smartphones, a total of 88 user-facing apps were logged as preloaded, and 57% of those were marked as safe to remove by the Universal Debloater Alliance Next Generation project. Across the 35 apps available for download on Google Play that disclose their data collection practices, these apps gather an average of 13 data types per app. This includes location-category data — such as approximate location (14 apps), precise location (12 apps), and physical addresses (11 apps) — among many other types of information. Like Apple, some apps also use sensitive details such as sexual orientation, political or religious beliefs. Facebook and Instagram collect the most data, with each gathering 37 out of 38 data types — accounting for 97% of all data types listed on Google Play. Other third-party apps make the list too, including Netflix, Microsoft OneDrive and Link to Windows, which collect 12, 15, and 6 data types, respectively. Six apps claim they do not collect any user data.

Like Samsung, Xiaomi preloads its smartphones with apps from multiple sources as well. In Bayton’s database of voluntary synced Xiaomi smartphones, a total of 61 user-facing apps were logged as preloaded, and 57% of those were marked as safe to remove by the Universal Debloater Alliance Next Generation project. Across the 33 apps available for download on Google Play, these apps gather an average of 16 data types per app. This includes location information — such as physical addresses (17 apps), approximate location (15 apps), and precise location (11 apps) — among many other data types. The apps collecting the most data are Google Gemini and Google, each gathering 29 out of 38 data types — nearly double the average. Notably, three apps — Weather, File Manager, and Mi Browser — state that they do not collect any user data.

In Bayton’s database of voluntary synced Google smartphones, a total of 71 user-facing apps were logged as preloaded, and 38% of those were marked as safe to remove by the Universal Debloater Alliance Next Generation project. Across the 40 apps available for download on Google Play that disclose their data collection practices, these apps gather an average of 13 data types per app. Beyond other information, this includes location details such as physical addresses (15 apps), approximate location (14 apps), and precise location (11 apps). Three apps — Google Password Manager, Pixel Screenshots and Pixel Studio — declare that they do not collect any user data. The apps collecting the most data are Google Gemini and Google, each gathering 29 out of 38 data types — more than double the average. In fact, Google-owned apps fill every single spot on the list.

Methodology and sources

This study focuses on top mobile brands Apple, Samsung, Xiaomi², as well as Google, the market’s fastest-growing brand by shipment volume³. The objective is to identify preloaded apps on these devices, determine the user’s ability to remove them, and analyze their data collection practices.

To list built-in apps on Apple devices, we used official technical specifications provided by Apple, such as the iPhone 17 Pro.⁴ However, since such documentation was not equivalent for Android devices, data was sourced from the Bayton System app database. Apps were filtered by Original Equipment Manufacturer (OEM), selecting only user-facing packages, then further refined to cover only mobile devices. We recognize that this approach has limitations, as a database built on voluntary contributions may lack completeness and absolute accuracy.

The compiled lists of preloaded apps were enriched with data on whether each app could be safely removed. For Apple devices, this was determined using iPhone User Guide⁵, supplemented by online research. For Android devices, lists were enriched using data provided by the Universal Debloater Alliance Next Generation project, where a “Recommended” tag indicated that an app could be safely uninstalled without causing performance issues.

To evaluate data collection practices of preloaded apps, information on the total number of data types each app may collect was retrieved from the Apple App Store and Google Play on September 2, 2026. Because other app stores lack standardized privacy labels, this analysis focused exclusively on apps available on these two platforms; for apps hosted elsewhere, users must either review complex legal policies or remain uninformed about data handling practices. Notably, developers are required to declare which data their apps collect across 35 data types for the Apple App Store, compared to 38 for Google Play.

Built-in Apps (Apple)RemoveRemove (EU)
Apple StoreYY
App StoreNY
BooksYY
CalculatorYY
CalendarYY
CameraNY
ClockYY
CompassYY
ContactsYY
FaceTimeYY
FilesYY
Find MyYY
FitnessYY
FreeformYY
GamesYY
GarageBandYY
HealthYY
HomeYY
Image PlaygroundYY
iMovieYY
iTunes StoreYY
JournalYY
KeynoteYY
MagnifierYY
MailYY
MapsYY
MeasureYY
MessagesNY
MusicYY
NewsYY
NotesYY
NumbersYY
PagesYY
PasswordsYY
PhoneNN
PhotosNY
PodcastsYY
PreviewYY
RemindersYY
SafariNY
SettingsNN
ShortcutsYY
StocksYY
TipsYY
TranslateYY
TVYY
Voice MemosYY
WalletYY
WatchYY
WeatherYY

Apps (Samsung)User-facingRemove
AccessibilityYExpert
Android SwitchYRecommended
AR Emoji EditorYRecommended
AR-zoneYRecommended
AT&T CloudYRecommended
Authentication FrameworkYRecommended
Avatar StickersYRecommended
BixbyYRecommended
Bixby VisionYRecommended
CalendarYAdvanced
CallYUnsafe
Call FilterYRecommended
CameraYRecommended
ChromeYAdvanced
ClockYAdvanced
CloudYRecommended
ContactsYExpert
ContactsYAdvanced
Device careYAdvanced
Device HelpYRecommended
Discovery HubYRecommended
DownloadsYAdvanced
Drawing assistYRecommended
EmailYRecommended
FacebookYRecommended
FilesYUnsafe
FilesYUnsafe
FinderYAdvanced
Galaxy AvatarYRecommended
Galaxy Beta ServiceY-
Galaxy Resource UpdaterYExpert
Galaxy StoreYExpert
Galaxy ThemesYAdvanced
GalleryYAdvanced
Gaming HubYRecommended
GeminiYRecommended
GmailYRecommended
GoogleYAdvanced
Google Play StoreYExpert
Health ConnectYAdvanced
InstagramYRecommended
InterpreterYAdvanced
Link to WindowsYRecommended
Live Transcribe and Sound NotificationsYAdvanced
MapsYRecommended
MeetYRecommended
MessagesYAdvanced
MessagesYAdvanced
Microsoft SwiftKey KeyboardYAdvanced
Modes and RoutinesYExpert
My FilesYAdvanced
My GalaxyYRecommended
My VerizonYRecommended
NetflixYRecommended
OneDriveYRecommended
PhoneYAdvanced
PhotosYRecommended
Private ShareYRecommended
RadioYAdvanced
ReminderYRecommended
Samsung CloudYRecommended
Samsung FreeYRecommended
Samsung InternetYRecommended
Samsung Max VPNYRecommended
Samsung NotesYRecommended
Samsung PassYRecommended
Samsung WalletYRecommended
Secure FolderYRecommended
Secure Wi-FiYRecommended
Separated AppsYAdvanced
Service provider locationYRecommended
SettingsYExpert
SFR JeuxY-
SFR My AppsY-
SIM ToolkitYExpert
SIM toolkitYAdvanced
Smart SwitchYRecommended
System TracingYRecommended
System UIYUnsafe
TasksYRecommended
TerminalY-
Verizon ProtectYRecommended
Video EditorYAdvanced
Visual VoicemailYRecommended
Voice AccessYRecommended
Wearable Manager InstallerYRecommended
WeatherYRecommended
YouTubeYRecommended

App (Google)User-facingRemove
SettingsYUnsafe
System UIYUnsafe
Google Play StoreYExpert
ChromeYAdvanced
DownloadsYAdvanced
GmailYRecommended
System TracingYRecommended
MapsYRecommended
YouTubeYRecommended
Android SwitchYRecommended
GoogleYAdvanced
FilesYUnsafe
SIM ToolkitYAdvanced
MessagesYAdvanced
MeetYRecommended
PhotosYRecommended
Digital WellbeingYAdvanced
Health ConnectYAdvanced
GboardYAdvanced
CalendarYRecommended
ContactsYAdvanced
DriveYRecommended
YouTube MusicYRecommended
Google TVYRecommended
Files by GoogleYRecommended
CalculatorYRecommended
PhoneYAdvanced
ClockYAdvanced
Personal SafetyYAdvanced
Switch AccessYRecommended
Live Transcribe and Sound NotificationsYAdvanced
Voice AccessYRecommended
GeminiYRecommended
Wallpaper and styleYAdvanced
ClockYAdvanced
Find HubYRecommended
TerminalY-
Device PolicyYRecommended
ContactsYAdvanced
Android System AngleYAdvanced
FilesYUnsafe
PhoneYExpert
Sound AmplifierYRecommended
CameraYAdvanced
RecorderYRecommended
Pixel TipsYRecommended
Pixel WeatherYRecommended
Pixel StudioYAdvanced
VPN by GoogleYRecommended
Pixel BudsYRecommended
GalleryYAdvanced
Health ConnectYRecommended
Pixel display servicesYAdvanced
Magic PortraitY-
Now PlayingY-
Theme packsY-
CalculatorYAdvanced
Android Beta FeedbackYRecommended
MusicYRecommended
CalendarYAdvanced
Health ConnectYAdvanced
Pixel ScreenshotsY-
ThermometerY-
MessagingYAdvanced
AuditorY-
App StoreY-
CameraY-
InfoY-
PDF ViewerY-
VanadiumY-
WachtwoordenY-

App (Xiaomi)User-facingRemove
SettingsYUnsafe
System UIYUnsafe
Google Play StoreYExpert
ChromeYAdvanced
DownloadsYAdvanced
GmailYRecommended
System TracingYRecommended
MapsYRecommended
YouTubeYRecommended
Android SwitchYRecommended
GoogleYAdvanced
FilesYUnsafe
SIM ToolkitYAdvanced
MessagesYAdvanced
MeetYRecommended
PhotosYRecommended
Digital WellbeingYAdvanced
Health ConnectYAdvanced
GboardYAdvanced
CalendarYRecommended
ContactsYAdvanced
DriveYRecommended
YouTube MusicYRecommended
Google TVYRecommended
PhoneYAdvanced
Personal SafetyYAdvanced
Switch AccessYRecommended
GeminiYRecommended
Link to WindowsYRecommended
AssistantYRecommended
Sound RecorderYAdvanced
ClockYAdvanced
TerminalY
ContactsYAdvanced
DuraSpeedYExpert
Google OneYRecommended
CameraYAdvanced
Health ConnectYRecommended
ThemesYAdvanced
File ManagerYAdvanced
App vaultYRecommended
GalleryYAdvanced
Services & feedbackYRecommended
MusicYRecommended
SecurityYExpert
Game CentreYRecommended
GetAppsYRecommended
Mi VideoYRecommended
MessagesYAdvanced
Mi BrowserYRecommended
FM RadioYRecommended
CalendarYRecommended
My AppsYRecommended
GPayYRecommended
КалькуляторYRecommended
КомпасYRecommended
ЗаметкиYRecommended
Запись экранаYRecommended
ПогодаYRecommended
ShareMeYRecommended
СканерYRecommended

For the complete research material behind this study, click here.

Fact-Checked by Irfan Ahmad.

Read next: Online reviews can expose hidden social ties, increasing risks of phishing attacks
by External Contributor via Digital Information World

Tuesday, September 8, 2026

Online reviews can expose hidden social ties, increasing risks of phishing attacks

By Hope Reese, University of Texas at Austin

On the surface, posting online reviews looks like a win-win activity. It helps both businesses we like and people who might frequent them.

But although posting reviews can benefit others, it can also put us all at risk, according to new research from the McCombs School of Business at The University of Texas at Austin. It can inadvertently expose our personal connections and make us and our online friends more vulnerable to cyberattacks.

The research focuses on a particular kind of email attack called spear phishing. A bad actor impersonates someone the target user trusts. The actor tries to trick the target into sending money or disclosing confidential information, such as passwords or bank accounts.

Screenshot of Yelp homepage: DIW - CC BY

In recent years, phishing has become big business, says Yan Leng, assistant professor of information, risk, and operations management at McCombs. From 2021 to 2023, she reports, the FBI’s Internet Crime Complaint Center received nearly 1 million complaints involving $305 million in losses.

“Social influence is a good thing,” Leng says. “The problem is that network data could be leaked.”

Exploiting Connections

By network data, Leng means information about our social relationships with people we’re connected to online. They can be on social platforms such as Facebook and review platforms such as Amazon, Google, or Airbnb.

Unlike Facebook, most review platforms don’t list a person’s friends. But Leng suspected that a phisher could figure out someone’s friends from their online behavior, such as reviews they wrote and ratings they provided.

She investigated, with Yijun Chen of the University of Melbourne; Xiaowen Dong of the University of Oxford; Junfeng Wu of the Chinese University of Hong Kong, Shenzhen; and Guodong Shi of the University of Sydney.

The researchers used the business review platform Yelp and covered 4,299 reviewers from Louisiana and Pennsylvania in 2020. On Yelp, unlike many review platforms, both texts of reviews and lists of friends were publicly accessible.

That allowed Leng to cross-check her work. First, she analyzed people’s reviews to deduce their networks of connections with other users, as a cyber-attacker might do. Then, she compared those presumed connections with their actual lists of friends.

She found that an attacker could correctly identify 49% of users’ social relationships purely from their online behavior, while incorrectly labeling only 10% of unconnected pairs as connected. When that false-alarm rate rose to 20%, an attacker could correctly identify even more relationships: up to 63%.

Why? The most salient clue was the lengths of Yelp reviews. When two people follow each other, Leng found, there’s an observable relationship between the lengths of their reviews.

If my friend writes longer reviews on Yelp, she explains, I follow suit and also write longer reviews. Another kind of relationship is that, if my friend writes longer reviews, I write only a short review, which can complement theirs.

Once a spear phisher infers a user’s relationships on Yelp, they can send scam texts or emails to that person’s friends, Leng says.

Volume matters, she adds. The more connections a scammer can identify, the more impersonation attempts they can make, and the higher their returns on the costs of targeting and sending emails. For the Pennsylvania data, returns soared from 109% for 500 attempts to 1,098% for 10,000 attempts.

Adding Noise Discourages Scammers

Unfortunately, Leng says, existing privacy laws, such as the European Union’s General Data Protection Regulation, don’t fully protect against this kind of privacy risk. Even if a platform doesn’t publish users’ friend lists, bad actors can infer them from seemingly harmless behavioral data.

To start with, platforms should evaluate whether they’re creating this type of risk, she says. Besides review platforms, e-commerce marketplaces and media-sharing platforms can be vulnerable.

Then, they should develop safeguards to reduce risk. One strategy, she suggests, is to add noise, which she defines as “small, carefully designed random changes to the data before it is released.”

A platform could subtly alter the text of reviews, to vary their lengths without changing their meanings, she explains. That could blur the behavioral fingerprints that reveal relationships without making the data useless for the platform’s own analytics.

In simulations, the researchers found the strategy reduced economic incentives for cyber-attackers, sometimes even making returns negative.

How much noise to add could be a challenge, she cautions. Each platform could choose a privacy budget, based on how much information it needs to retain and how much social privacy risk it is willing to accept.

One thing is clear to her: The current level of privacy risk is unacceptable. Says Leng, “The platforms need to protect not only what users disclose, but also what others can infer.”

When Behavioral Data Betray Users: A Diagnostic and Protective Framework Against Social Interaction Leakages” is published in Information Systems Research.

Fact-Checked by Irfan Ahmad.

Read next: 

• AI agents can now remember and hackers can ‘poison’ their memories — a new cybersecurity threat

• Ask for More Pay and Get Penalized, Stay Quiet and Fall Behind
by External Contributor via Digital Information World

Do AI recommendations beat a vet’s? Here’s what pet owners say

Is AI reliable when it comes to your pet’s health? According to a new survey, conducted online between April 22 - April 28, 2026, more than two in five (43%) use tools like ChatGPT for recommendations on their pet’s health and nutrition.

Nearly half (47%) mainly use AI for more unusual questions such as “Can pets see ghosts?” But the study of 2,000 pet owners, commissioned by Darwin’s pet food and conducted by Talker Research, found more health-based questions averaged seven times a week.

Pet parents will ask AI about nutrition (55%), symptoms or illness (54%), pet care products (52%) and general curiosity about behavior (50%).

AI is becoming a common pet-care tool, but veterinarians remain far more trusted for health advice.

Almost three in five (59%) have made changes to their pet’s care as a result of AI recommendations. And 90% of pet owners say AI and online resources have made them feel more informed. But 50% admit researching pet care decisions online sometimes makes them feel unsure.

Veterinarians still come in first for pet owners’ most trusted source for health and nutrition advice (71%), and just 14% trust AI tools the most. If the two contradict each other, 64% will listen to their vet over 12% that choose AI.

Overall, 30% of pet owners agree using AI increases worry for pet health, mainly when it comes to health (47%) and nutrition (33%) .

The study also asked pet owners how they try to verify info they find online, and as most trust vets the most, the majority of pet parents (57%) rank professional credentials as the best signal for trusted info. After that is personal experience (34%), scientific studies (31%) and AI-generated responses (11%).

“AI is changing how people gather information, but it doesn’t change the responsibility that comes with caring for an animal,” added Hagenson. “The most confident decisions tend to come from balancing expert advice with firsthand understanding of your pet, rather than relying on any single source.”

This firsthand understanding is shared by many as 89% said AI can’t replace a pet owner’s intuition. But trust will only become more challenging as 52% believe AI will be a normal part of pet care within the next five years.

Fact-Checked by Irfan Ahmad.

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• How AI could make us work even harder

• As Bots Gain Authority Over Workers, Does Human Oversight Still Matter?
by Guest Contributor via Digital Information World

How AI could make us work even harder

Malte Jauch, University of Essex

Employers may expect more from AI-assisted workers, potentially increasing workload rather than creating additional free time.
Image: Vitaly Gariev - Unsplash

Technological progress has taken more than its fair share of the workload from humans over the years. Washing machines clean our clothes, tractors plough the fields and computers sort out the admin.

Meanwhile, economic growth and redistribution of wealth means most workers in industrialised countries do not need to spend most of their waking hours on gainful employment. The 12 hour working day that was common during the 19th century has been largely reduced.

So will developments in AI help us gain even more free time?

Not necessarily. In fact, AI might actually cause working hours to increase.

One reason for this has to do with the effects of AI on people’s bargaining power in the workplace. If workers are fearful that their jobs might be lost to automation, research shows they may be inclined to increase their efforts as a way of signalling to employers that their labour is still valuable.

My research on the rat race suggests that this kind of response is to be expected.

Employers must continuously decide whom to hire, promote or dismiss. In making such decisions, employers seek to reward productivity. But productivity is often hard to measure, so employers rely on alternatives metrics such as the amount of time spent working.

Checking whether a work station is still occupied after everyone else has gone home is much easier than assessing the accuracy of a complex project. And from a worker’s point of view, increasing the time they spend at work seems like an effective way of setting themselves apart from other (apparently less motivated) workers.

But when everyone pursues this strategy, it becomes self-defeating. No one gains a positional advantage, while everyone works more.

The introduction of AI to workplaces can turbocharge this dynamic. Existing AI tools have already automated some tasks, like data entry, creating an expectation that entire roles will be redundant soon.

Against this backdrop, the rat race becomes more cut throat. Workers reasonably expect large scale restructurings of their companies and seek to make sure that they will retain their jobs in the process. Researchers recently found evidence for this, noting that workers in AI exposed industries like financial services and IT are literally switching off their office lights later than those in less exposed industries.

Another problem is that employers can overestimate the extent to which AI boosts workers’ productivity. The availability of AI tools to improve efficiency creates an expectation among employers that workers can do more than they could before in the same amount of time. However, when these expectations are overblown, they can lead to additional work intensification or longer work hours.

Working nine ‘til…?

Finally, the adoption of AI causes significant frictions. Established routines are overhauled, existing roles change and new roles are created. Workers must be trained in the use of AI tools and must learn to work alongside them.

But what makes this particularly challenging is that existing AI tools are continuously being improved and new tools developed at a rapid pace. All of this places additional demands on workers’ time.

It may be sobering to contemplate that AI might not improve our lives by relieving us from toil. Overwork and burnout might even increase over the coming years.

But that situation is by no means inevitable or unavoidable – especially if future control over AI’s design and implementation is not left to employers and large AI firms.

It is possible in theory to design and implement AI in ways that improve workers’ skills, enhancing their areas of expertise, making their work more valuable and thereby enhancing their bargaining power with employers. An empowered workforce would be able to bargain for favourable working conditions, including working time patterns that fit with people’s lives and preferences.

And if developers were to prioritise augmentation and empowerment, AI could be the ultimate tool that helps to tackle widespread resentment and disenchantment with our working lives.The Conversation

Malte Jauch, Lecturer in Management and Marketing, University of Essex

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Fact-Checked by Irfan Ahmad.

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by External Contributor via Digital Information World

Monday, September 7, 2026

Perfectionistic leadership can affect employees beyond work, contributing to work-family conflict and presenteeism

By University of East Anglia

Image: Mushvig Niftaliyev - Unsplash

Employees under pressure to meet exceptionally high standards are more likely to experience conflict at home and work while unwell, according to a University of East Anglia study.

Researchers found that employees who perceive their leaders as perfectionistic are more likely to experience work-family conflict.

That conflict, in turn, increases the likelihood of presenteeism, where employees continue working despite being ill.

The findings suggest that leaders who impose unrealistically high standards on others can affect employees' wellbeing, both at work and at home.

The results, uncovered by academics from UEA, Sheffield Hallam University and the University of Liverpool, are based on a study of 483 employees across 102 teams in UK universities and colleges.

Prof Annilee Game, from UEA's Norwich Business School, said: “While the link between leader characteristics and employee wellbeing is well known, we uncovered what connects leader perfectionism to presenteeism.

"Our study is among the first to show how leaders' unrealistic expectations can fuel work-family conflict, driving employees to work even when unwell.”

The researchers suggest that demanding leadership behaviour can drain employees' time, energy and emotional resources, leaving them with less capacity for family responsibilities and increasing stress across both domains.

Prof Ana Sanz Vergel also from UEA's Norwich Business School, said: "This study reveals the powerful influence of leadership and the blurred boundary between work and home.

"Leaders' perfectionistic expectations spill over into employees' home lives, making it harder for them to cope with family demand, and the consequences are substantial.

"Conflict between the work and family domains depletes employees' energy to the point that they lack the resources to engage in health-protective coping strategies.

"Instead, they may turn to what appears to be the easier option but is ultimately a harmful one. We need to challenge the notion that presenteeism is the answer."

Conducted over six months, with data collected at three points in time, the study adds to growing evidence that leadership styles can influence employees' wellbeing in ways that extend beyond the workplace.

Dr Rahul Goel, Senior Lecturer in Organisational Behaviour and Human Resource Management at Sheffield Hallam University, who led the study, said: “We were interested in understanding what happens when perfectionism is directed towards others, particularly when it comes from people in positions of leadership.

"Because perfectionism is often seen as positive, our findings highlight that leader-driven perfectionistic expectations can harm wellbeing and life outside work.

“Organisations should therefore reconsider which leadership behaviours and norms they promote, rather than assuming higher standards always improve outcomes.

"I hope this research encourages organisations and leaders to reflect on how expectations are communicated and the potential consequences of creating an environment where leaders expect perfection from employees.”

'The perfect storm: How leader perfectionism fuels employee work-family conflict and presenteeism’ is published in the journal Work & Stress.

Fact-Checked by Irfan Ahmad.

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by External Contributor via Digital Information World