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Social media is sucking the life out of our holidays - here's how to travel without Instagram and TikTok ruining your next trip

Daily Mail - Science & tech

You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. 'Quite appalling': The UK's WORST airport revealed - with two-hour queues, nowhere to sit and'excessive' walking distances Best foot forward: The UK's best late-summer walks - from an expert who's hiked across the country for more than 50 years Paws for a pint: Britain's most dog-friendly pubs revealed, from'bark bangers' to kibble machines and canine menus Beyond the Mid-Autumn magic: The electrifying festivals that prove Hong Kong is Asia's most vibrant cultural capital The'walk of shame', fish-bowl cocktails and 5am airport runs with a hangover: Holiday reps from the 80s and 90s reveal what really went on The world's longest ever cruise sets sail on epic 371-day voyage - with stops all around the globe including Zanzibar, Alaska and even Japan Where should Brits move to in 2027? The £16-a-night youth hostels that beat five-star hotels hands down on location...from the Yorkshire YHA that overlooks Whitby Abbey to the 800-year-old Norman Castle in the Wye Valley As the back-to-school blues kick in, what's the best holiday you've ever had? A nightmare property with a killer view: Couple snap up 18th-century Lake Como home before realising the 300-year-old property needed'more work than we ever imagined' 'Unsettling... then incredibly cool': Self-driving taxis can now be booked in the UK, and we've had a first ride - this is what it was like (and why it's not completely driverless) Tourists in Spain brace for 38 degrees this weekend after late summer heatwave hits - with night temperatures no lower than a'torrid' 25 degrees One of Spain's most expensive homes - with its own bowling alley, 42-car garage and hair salon - goes up for sale for £60million You might actually get some sleep on this plane! Ranked by our travel expert, Britain's best to worst holiday parks: From the'perfect' posh lodges no one seems to know about to the only Center Parcs worth considering - and'tired' resort to avoid like the plague We've tested the best baby travel accessories - here are the parent-approved products you need when travelling with a newborn, from carriers to cots Social media is sucking the life out of our holidays - here's how to travel without Instagram and TikTok ruining your next trip Social media is a huge driver for tourism - it's an easy and instant way to share, view and find popular spots to visit.


One town's scheme to get rid of its geese

MIT Technology Review

One town's scheme to get rid of its geese Public officials in one California burgh spent nearly $400,000 on tech to flush out waterfowl. Some geese, like the one on the left, wear GPS trackers as part of the Foster City goose management plan. Our target is in sight: a gaggle of Canada geese, pecking at grass near the dog park. As I approach, tiptoeing over their grayish-white poop, I notice that one bird wears a white cuff around its slender black neck. It's a GPS tracker--part of a new tech-centered campaign to drive the geese out of my hometown of Foster City, California. About 300 geese live in this sleepy Bay Area suburb, equal to nearly 1% of our human population--and some say this town isn't big enough for the both of us.


Enhancing Online Support Group Formation Using Topic Modeling Techniques

arXiv.org Machine Learning

Online health communities (OHCs) are vital for fostering peer support and improving health outcomes. Support groups within these platforms can provide more personalized and cohesive peer support, yet traditional support group formation methods face challenges related to scalability, static categorization, and insufficient personalization. To overcome these limitations, we propose two novel machine learning models for automated support group formation: the Group specific Dirichlet Multinomial Regression (gDMR) and the Group specific Structured Topic Model (gSTM). These models integrate user generated textual content, demographic profiles, and interaction data represented through node embeddings derived from user networks to systematically automate personalized, semantically coherent support group formation. We evaluate the models on a large scale dataset from MedHelp, comprising over 2 million user posts. Both models substantially outperform baseline methods including LDA, DMR, and STM in predictive accuracy (held out log likelihood), semantic coherence (UMass metric), and internal group consistency. The gDMR model yields group covariates that facilitate practical implementation by leveraging relational patterns from network structures and demographic data. In contrast, gSTM emphasizes sparsity constraints to generate more distinct and thematically specific groups. Qualitative analysis further validates the alignment between model generated groups and manually coded themes, showing the practical relevance of the models in informing groups that address diverse health concerns such as chronic illness management, diagnostic uncertainty, and mental health. By reducing reliance on manual curation, these frameworks provide scalable solutions that enhance peer interactions within OHCs, with implications for patient engagement, community resilience, and health outcomes.