"Mr Branding" is a blog based on RSS for everything related to website branding and website design, it collects its posts from many sites in order to facilitate the updating to the latest technology.
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Meta-owned WhatsApp announced Monday that users can begin reserving optional usernames ahead of the feature's launch later this year. The company said reservations are opening this week to give users an opportunity to secure a preferred username before the broader rollout.
In a post published on the WhatsApp Blog, the company said it is opening reservations early because its more than three billion users mean many names overlap, giving people an opportunity to reserve the username that matters to them.
Image: Whatsapp
According to WhatsApp, usernames are intended to let people communicate without sharing their phone numbers. Once the feature launches, users who enable a username will no longer have their phone number shown when messaging a person or business for the first time.
For creators, small businesses and organizations, WhatsApp said it has provided an option to claim an existing Instagram or Facebook username for use on WhatsApp.
Image: Whatsapp
Users with the latest version of WhatsApp can reserve an optional username by going to Settings > Account > Username. The company said usernames will roll out gradually over the coming months, and users will receive an in-app notification when the feature becomes available in their country.
In a separate development, WABetaInfo reported Tuesday that WhatsApp is developing a feature for Android that would allow users to link additional devices using a passkey as an alternative method alongside QR code-based device linking. According to the publication, the feature remains under development, is not yet available for beta testing, and WhatsApp has not announced a timeline for its release.
This year, Wikipedia is celebrating 25 years as the internet’s encyclopedia that anyone can edit. In its first decade, the quirky experiment for passionate nerds exploded in popularity. It became a ubiquitous information resource and a homework helper for schoolkids, much to the dismay of skeptical teachers.
In its second decade, amid the public’s growing dissatisfaction with the mangling of facts in popular discourse, it took on a new role as information infrastructure, helping categorize and validate information worldwide. Wired magazine deemed it “the last best place on the internet.” The hope was that the volunteer project could serve as the antidote for misinformation. Platforms from Facebook and Twitter to Alexa and YouTube began embedding Wikipedia material to ensure that users had context for what they read or saw.
That role has become more acute in recent years. Artificial intelligence developers have relied deeply on Wikipedia to train the large language models behind popular chatbots, which weight clean, reasonably reliable information sources more heavily than the rest of the web. Chatbots and AI-powered search engines have intensified Wikipedia’s significance, even as they siphon its readers by answering questions directly, with fewer people going to the source site itself.
But as Wikipedia’s importance – and size – has grown, the size of the volunteer corps that maintains it has not, and the number of volunteer administrators, a key moderation role, has shrunk.
The Wikipedia community is also sensitive to its rising importance, but not in the way you might think. Contributors are keenly aware of political rhetoric that takes aim at their project or threatens volunteers. But the chief effect on volunteers has been a sense of heightened obligation to their global readership, which has gradually increased quality standards.
As a longtime volunteer myself, I’m often taken by the community’s perseverance and the people’s desire, above all, to get on with their work of summarizing the world’s knowledge.
Wikipedia’s rich history busts myths that have risen with it.
The English language Wikipedia has maintained a reasonably steady number of contributors since 2010 – about 40,000 – yet its size and importance have grown. In 2006, it contained 1 million articles; in May 2025, it passed 7 million. A new issue is an influx of low-qualitycontent generated by large language models.
The steady decrease in administrators is especially concerning. Administrators are a subset of trusted users, elected by the community at large, who are given powers such as the ability to delete articles or block users from editing. Unlike moderators at for-profit platforms, Wikipedia cannot simply hire more administrators. There are slightly more than 800, down from almost 1,800 in 2011, and they’re not all active.
So Wikipedia’s role has grown, but it is held together by a relatively small, shrinking community of unpaid volunteers. To keep up, the community in general and administrators in particular have had to raise their efficiency, making trade-offs between maintaining open participation and raising article quality. These trends and their costs are well documented. They are clearly visible in one of the basic administrator routines: blocking.
Shown the door
Blocking is when an administrator determines that a user is so detrimental to the project that they must be prevented from making any further edits. The blocked user can still read Wikipedia, but cannot change it.
Unlike the opaque moderation systems at the large internet platforms that I normally study as a researcher, such as YouTube or TikTok, nearly every administrative action on Wikipedia is recorded in a public log. I used these logs for a study analyzing all 20 million blocks made on the English language Wikipedia over the past two decades. I looked for patterns in frequency, duration and reasons for a block. I also assessed whether those patterns corresponded to the growing trade-offs between openness and quality.
I found that the frequency of blocks has risen sharply in recent years due to administrators using bots to preemptively block proxies. Proxies are services such as virtual private networks, or VPNs, that people use to conceal their identity, often to facilitate abuse or manipulation on Wikipedia. One of these bots, ST47ProxyBot, was so active that it accounted for the most blocks in the site’s history. Preemptive proxy blocking likely prevents damage, but it can also occasionally stop good-faith contributors. Given the increasing popularity of AI agents and their disruptive potential, this practice is likely to continue to expand.
I then removed proxy blocks from the analysis so I could focus on humans who were blocked and why. In the early years, administrators made the majority of blocks for vandalism: intentionally bad or nonsensical edits. That has shrunk to about a quarter of all blocks today. Blocks have risen for promotional editing and for sockpuppetry — when one person creates multiple accounts to manipulate content. These shifts speak to Wikipedia’s increased prominence as a target for influence.
Signs of stress
What I found most interesting was administrators’ greater use of generalized reasons for blocking, such as “disruption.” Wikipedia defines disruption as “a pattern of editing that disrupts progress toward improving an article or building the encyclopedia.” But citing this can mean nearly anything seen as counterproductive. The trend is partly explained by “disruption” being in a list of boilerplate rationales that administrators can choose from instead of entering a customized reason.
But it’s also the kind of trend I would expect to see in a labor force stretching to keep up. Administrators don’t act arbitrarily, and their actions are publicly logged and closely scrutinized. A loss of trust leads to an administrator losing their position. But to be effective, general explanations for blocks rely on shared understandings that new users may not have. Research on blocked users shows that when a sanction feels vague or unfair, volunteers are more likely to walk away – or dig their heels in – rather than reform. Good for efficiency; bad for bringing new users into the fold.
Blocks are also lasting longer on average. That, together with preemptive blocking and generalized rationales, suggests that the volunteer community is increasingly prioritizing prevention, efficiency and content quality over efforts to rehabilitate new users.
And the work is not spread evenly among the roughly 800 administrators: For many years, the most active 10% of administrators have made about 80% of the blocks. That high number dropped to 37% in 2024, largely due to changed activity by a single prolific administrator.
Bearing the cost
Wikipedia’s openness is part of how its volunteer community grew in the first place. Now that Wikipedia has become infrastructure, that community is rationing openness to preserve quality for readers. If Cory Doctorow’s zeitgeist-capturing idea of platform “enshittification” is fundamentally about ruining the experience of end users for the sake of the shareholders, Wikipedia is attempting something like the opposite. The end-user experience is being preserved, and the people behind the scenes are bearing the cost.
Wikipedia has adapted remarkably well in its evolution from early web experiment to one of the most important global sources of information. The open question, for a resource that so many humans – and now machines – rely on, is how long the volunteer system can keep enduring the cost.
Many people worry that responding too quickly to a prospective employer could make them seem too available or even desperate. New research published in Management Science suggests they may be worrying about the wrong thing.
Analyzing 11.6 million marketplace interactions and a series of experiments involving both job candidates and service providers, the researchers found no evidence that delaying a response improves hiring prospects. Instead, employers consistently preferred faster responders.
"People have this intuition that playing hard to get is somehow useful," said On Amir, a professor at the UC San Diego Rady School of Management and co-author of the study. "We find the opposite is true." Speed matters because it signals responsiveness."
The study combined real-world marketplace data from Fiverr, a platform connecting employers with freelancers, with three main experiments involving more than 3,600 participants and five supplemental studies involving another 5,000 participants.
In the Fiverr data, a one-hour delay was associated with a 46% reduction in hiring likelihood, while a full-day delay reduced hiring likelihood by roughly 90%.
The effect persisted even when participants had access to other information, including ratings and the content of a response.
The experiments suggested that faster responders made better first impressions. They were judged to be warmer and more competent – and most importantly, as likely to be more responsive in the future.
People use reply speed, the researchers conclude, to infer what someone might be like to work with.
"Speed is a signal. People see a quick response as a sign that you'll be attentive to their needs in the future, not just right now," said co-author Einav Hart of George Mason University.
Interestingly, the researchers found a gap between what people said about response speed and what they did. Participants reported that same-day responses would be just fine, yet consistently preferred much faster responders when making their actual hiring decisions.
Authenticity matters, too
The researchers caution against reducing the findings to a simple rule about replying as quickly as possible.
"Speed matters because people use it as information," Amir said. "But there isn't an equal sign between speed and responsiveness. Authenticity matters, too."
The researchers found that while response speed influenced hiring decisions, people also paid attention to whether a response appeared personalized and attentive.
That distinction may become increasingly important as AI makes instant responses easier to generate. While automated replies can eliminate delays, they may not convey the thoughtfulness or engagement that people ultimately value. In the experiments, faster replies lost their appeal when recipients believed them to be generated automatically or by an AI.
The takeaway is straightforward: Once someone reaches out, there appears to be little advantage in making them wait. But a quick response is most effective when it is also genuine.
Parents who come down hard on their children for telling lies or misbehaving may believe that they are teaching the child right from wrong. But new research by NUS suggests that both overly strict or punitive parenting could be part of what drives the behaviour in the first place.
Drawing on two long-term studies of Singaporean families, researchers from NUS Psychology found that ‘authoritarian parenting’ and ‘harsh punishments’ were associated with greater dishonesty in children across early and middle childhood. The studies suggest that this is not out of defiance, but a way for the children to cope with self-criticism, the pressure to perform and the fear of making mistakes.
The first study, published in the academic journal Child Development, tracked preschoolers and found that those whose fathers were stricter and enforced rules with little explanation were more likely to cheat later on. The researchers observed that these children also tended to be harder on themselves.
The second study, published in Developmental Psychology, followed school-going children over three years and found that children subjected to physical punishment like spanking were more likely to cheat and lie over time.
The studies were led by NUS Psychology’s Associate Professor Ding Xiao Pan and doctoral student Ms Liwen Yu. The second study was also led by Associate Professor Ryan Y. Hong from NUS Psychology.
Authoritarian parenting promotes cheating through self-criticism
The first study examined 479 families who participated in the Growing Up in Singapore Towards Healthy Outcomes (GUSTO*) birth cohort study, one of Singapore’s largest and most comprehensive birth cohort studies.
Researchers assessed parenting styles via a parental questionnaire when children were four and a half years old and measured cheating behaviour a year and a half later using a dart game.
The study found that 61 per cent of children cheated, with strict paternal parenting at age four and a half years significantly predicting this behaviour.
“Authoritarian parenting is characterised by high control, low warmth and harsh discipline without explanation. While parents may believe this approach instils discipline, our research shows it may actually undermine children’s internalisation of moral values,” said Assoc Prof Ding.
Researchers found that children’s self-criticism helped explain this link. Children with stricter and more controlling fathers were more self-critical in a sketching task done as part of the study, which predicted a greater likelihood of cheating.
“Self-critical children may feel intense pressure to maintain a flawless image and cheating becomes a maladaptive coping strategy. It is a way to avoid feelings of inadequacy and secure external validation," Ms Yu explained.
“To our knowledge, this is the first study to investigate the developmental mechanisms linking a discipline-oriented family environment to cheating behaviour,” she noted.
Harsh punishment breeds deception in school-going children
The second study followed 302 Singaporean families with school-going children aged seven to nine years, examining whether negative parental control predicted children’s deceptive behaviours over time.
Negative parental control comprises harsh punishment, discipline and ignoring. Of the three, only harsh punishment, which includes physical punishment like slapping and spanking, was found to increase children’s lying and cheating over time.
Harsh parental punishment at age seven significantly predicted increased deceptive behaviour at age eight, with this pattern continuing into age nine. The relationship also worked both ways: children’s deceptive behaviour at age eight predicted harsher parental punishment at age nine, suggesting a troubling cycle.
The study also identified children’s dysfunctional attitudes, like believing they must do well to be liked, as an important pathway linking harsh punishment to dishonest behaviour.
“Children exposed to higher levels of negative parental control were more likely to internalise dysfunctional beliefs such as ‘I have to do well to be liked’ or ‘I shouldn’t make mistakes’. They may then resort to lying to meet these unrealistic expectations or avoid further punishment,” said Ms Yu.
Cultural context and practical implications
Singapore is a useful setting for the studies because strict, obedience-oriented parenting and physical discipline remain relatively common.
However, even in Singapore, where authoritarian parenting is more culturally accepted, findings suggest it still poses risks for children’s moral development.
“What both studies reveal is that strict parenting doesn’t directly cause dishonesty. Rather, it changes how children see themselves, and it’s this altered self-view that leads to cheating and lying,” said Assoc Prof Hong.
The research team acknowledges that dishonest behaviour in children is multifaceted and influenced by cognitive development, social factors and individual differences. However, these studies provide crucial evidence that parenting practices play a significant role during critical developmental periods.
Ms Yu said, “Understanding these developmental pathways is essential for designing effective interventions. Rather than responding to children’s dishonesty with harsher punishment, which our research shows may actually worsen the problem, parents and educators need to address the underlying psychological mechanisms.”
This post was originally published by the National University of Singapore (NUS). It has been edited for style and length and is republished here with permission.
Children are innately curious, and throughout any given day they come up with all manner of questions: Why don’t fish have hair? Why do flowers wilt so quickly? Their need to understand the world – and develop their language skills and ideas – makes them tireless conversationalists.
While their inquiries would usually be directed at parents or teachers, in modern homes even the youngest kids might now talk to a digital interface like Siri or Alexa. These AI systems are fast becoming part of many children’s everyday lives, as kids ask them to play music, help with their homework, answer questions, or just chat to them.
These kinds of interactions are no longer strange, but we need to ask what happens when they become completely routine. Do they change the way children learn to communicate? Do they change the words they use? And are they a threat to kids’ cognitive abilities?
Language learning
Learning to speak has never been a question of just learning words. Children acquire language through human relationships, and by building emotional ties to other people. They learn to take turns, how to interpret silence and context, and how to tell when someone is tired, annoyed or distracted. They also discover that conversations do not need to be perfect – there will always be interruptions, misunderstandings, and off-the-cuff explanations.
But AI does not think like a human. Think about your interactions with ChatGPT or Gemini. We rarely lose our patience while talking to these virtual assistants, partly because these interactions are, by their very nature, governed by a very different logic to human conversation. These tools are built for quick responses and infinite patience, and this changes the experience of communication.
AI and politeness
In many homes, something very curious is becoming increasingly common: some children (and even adults) are adapting their speech so that virtual assistants will understand them better. They speak in simple sentences, and give direct instructions: “play cartoons, open YouTube, tell me a joke”. This kind of speech – known as instrumental language – aims to get immediate results.
This shift does not necessarily mean children are becoming ruder or less empathetic, but it may influence their expectations of conversation in general. Human interactions are usually slow and ambiguous, and require patience, attention and negotiation. Chatbots, on the other hand, are designed to give quick, fluid responses, or even create a sense of virtual empathy with the user.
This all leads us to a question that may seem minor, but reveals a lot: should we teach children to say “please” and “thank you” to Alexa? Beyond the surface-level question of whether we should be polite to machines, this debate forces us to think about the communicative habits that children develop through daily interactions with technology that always obeys them. The wider question for families and educators alike is: what idea of “conversing” will children construct in this context?
Alongside these doubts, we should not lose sight of the opportunities that these systems present. Many children feel freer to ask questions when they do not fear judgement, and a chatbot will repeat an explanation as many times as is necessary, adjust the level of complexity, or support them as they learn new languages or concepts.
These tools provide a safe space for trial and error, free from the social pressures that often accompany human conversation. This is not just the case for children. Many of us now resort to AI to ask ordinary questions, from “Alexa, how do I recover my password?” to more embarrassing queries that we would rather not voice out loud.
Responding is not understanding
Current AI systems produce extremely convincing answers, but they do not understand the world the way a person does. They do not have experiences, emotions, or intentions – even if they talk like they do. Just like many adults, young children tend to attribute human qualities to the things they interact with. If something can converse, it is easy to presume that it also has understanding or knowledge.
However, a lot of the information in human conversation is unspoken. An adult can tell when a child’s question is the product of curiosity, fear, or a simple need for attention. This pragmatic dimension – consisting of gestures, tone, looks, feelings – is crucial for children’s development. It is difficult to replicate in a machine, which can only offer an answer without capturing any of this nuance.
Humans are not machines
When children grow up surrounded by a particular kind of linguistic exchange – one that consists of quick responses and having every single request obeyed – it ends up shaping habits, expectations and ways of interacting. This can lead children to always expect clear, quick, effortless answers, as though any conversation were something to be resolved on the spot.
The adults who live with young children therefore have a vital role to play. They are the ones who mediate daily use of these tools both at home and at school, who understand their limitations, and who are able to integrate these conversations into general learning.
A child asking Alexa to answer their questions or tell them a joke is not, in and of itself, detrimental to their language development. But we should guide these conversations so that they understand they are dealing with a machine that responds to them, not a person.
We need to show children what separates us from machines, how we should interact with them, and in what situations it is alright to use them. We should accompany them in these everyday interactions, commenting on them and helping them to understand AI’s limitations.
AI can be useful as a support, but under no circumstances should it take the place of replace conversation between people. Despite rapid developments in technology, human interaction remains at the heart of the way we exist in the world.
Research by Associate Professor Sky Marsen, an applied linguist and Communications course director at Flinders University, and Professor Robert Biddle, a computer scientist based from Carleton University, Canada, suggests a surprising reason for this gap: the language used to explain cybersecurity may be part of the problem.
In an experimental study comparing “figurative” cybersecurity language (terms such as phishing, virus, or trojan) with more literal explanations, the authors found that people understood incidents significantly better when the language was clearer and less metaphorical.
This challenges a widespread assumption in science communication – that metaphors help non-experts grasp complex ideas. In cybersecurity, the opposite may be true.
“These terms weren’t designed for the public in the first place,” explains Associate Professor Marsen. “They emerged from inside hacker culture, and terms that may sound creative and playful within expert communities, are often opaque to outsiders. When they are used in public communication, they can obscure rather than clarify what’s happening.”
Given the rise of cybersecurity concerns, Associate Professor Marsen says it’s timely to understand how non-experts understand cybersecurity words and metaphors – especially the figurative language created by computer scientists to describe cybersecurity incidents.
A lack of accurate information makes cybersecurity an issue that is difficult to clearly explain to the public – and this can lead to major losses for individuals and serious reputational damage for organizations.
“Organisations routinely tell customers they’ve been hit by phishing or a malware attack, but if people don’t fully understand what that means, they may not know how to respond or protect themselves,” says Associate Professor Marsen. “Worse is that unclear communication can downplay the responsibility of organisations, or leave users vulnerable.”
Using a set of cyberattack stories composed with figurative words and a set composed with more literal versions, and an online survey, the study examines whether the use of metaphor and neologism clarifies or obfuscates the technical aspects of cybersecurity for non-experts.
The results showed participants in the literal set scored significantly better in comprehension. However, participants made important errors in both literal and figurative versions. This underlines the need for organizations to employ language strategically and provide more effective explanations of cybersecurity situations.
Associate Professor Marsen says a key takeaway from this research is that paying attention to language choices in professional communication is not just a stylistic choice but a public safety issue.
The research – “Grok hackspeak? Communicating cybersecurity with figurative language”, by Sky Marsen and Robert Biddle – has been published by the International Journal of Business Communication. https://journals.sagepub.com/doi/10.1177/23294884251329160.
This post was originally published on Flinders University News and republished here with permission.
Six months on from Australia’s under-16s social media ban taking effect, the early verdict from headlines and children themselves has been blunt: it isn’t working.
A new study published today in the British Medical Journal appears to add even more weight to this judgement.
Led by University of Newcastle public health researcher Courtney Barnes, the study found very little evidence that kids had stopped accessing restricted social media platforms such as TikTok, X, Facebook and Instagram.
But the question “are children evading social media age checks?” might be the wrong one to ask when considering the long-term success of Australia’s world-first experiment.
Isolating the effect of the ban
The team behind the new study followed 408 adolescents aged 12–16, surveying them just before the law took effect in December 2025 and again three months later. They compared teenagers just under the age cutoff with those just over it to isolate the law’s effect.
They found more than 85% of under-16s were still using restricted platforms at follow-up, mostly through their own accounts.
Two thirds had encountered age verification, but the most common form was simply being asked to state their age. A minority used fake accounts or private browsing to access social media. But VPN use to evade the ban was rare.
When the researchers checked whether under-16s used social media any less than the just-over-16s who were free to keep their accounts, they found no meaningful gap at the age cutoff.
The researchers were transparent about the study’s limitations. The analysis was underpowered (which means the study may not have had enough participants to detect an effect if one existed). The sample sizes either side of the cutoff were also small.
Nevertheless, these results square with recent research from the eSafety Commissioner that showed roughly 7 in 10 children kept their accounts after the law came into effect.
So, case closed, right? The ban is a failure? Not quite.
An unrealistic pipe dream
It was an unrealistic pipe dream that the ban would stop all of today’s under-16s from using social media overnight. All online technology comes with inherent capacities to be exploited or its features circumvented.
The ban should be considered over a longer timeframe. Its logic is more aligned with another form of public health law: the generational approach now being applied to tobacco control.
Britain’s Tobacco and Vapes Act, which received royal assent in April 2026, bars anyone born on or after January 1 2009 from ever being sold tobacco.
The aim is not to make today’s smokers quit but to raise a generation for whom smoking never becomes normal. Australia’s social media law makes a similar bet: that if access is delayed long enough, social media might lose its grip on childhood the way cigarettes slowly did.
That is the measure that matters, and it’s a far slower and less certain test than counting how many teens still have Instagram six months after the ban took effect.
A benighted idea for future generations
Granted, there’s a catch to this framing.
Tobacco use has been denormalised with a public health approach for decades, and its supply has been squeezed from multiple directions: higher prices, plain packaging, advertising bans.
It’s hard to put pressure on social media use in the same way. Effectively, social media is “free”, practically infinite, and engineered to maximise engagement.
Shifting a generation’s social media norms this way only works if the pressure on platforms is relentless and sustained for years, not abandoned the moment the first headlines call it a failure.
My research into social media use and risk-taking found the same difficulties: norms are sticky. Social media rewards risky content and changes what is deemed as normal or acceptable. Changing norms like these overnight is unlikely.
But viewed in the long term, or even generationally, we can see how social media use for children may become a benighted idea for future generations.
Effects not clear for a decade
Naturally, laws that “ban” things often have unintended or even detrimental consequences. When mandatory bicycle helmet laws were introduced in Australia in the early 1990s, one result was that some people simply cycled less.
The new study in the British Medical Journal reflects this, with small numbers of young people turning to fake accounts, private browsing or messaging apps. Some may drift to less visible corners of the internet that are harder to watch than the mainstream platforms.
We shouldn’t take this to mean the ban is a failure. It means we are judging it on a timeline that does not fit its design.
The researchers make the point themselves: the greatest opportunity may lie with children under eight who have not yet started using social media, rather than teenagers whose habits are already set, whose norms are to use social media.
By their estimate, the full effects may not be clear for a decade.
Australia has volunteered to be the world’s test case, with other countries now following. To do the social media age restrictions justice, we should test the right thing.
