Influencers Are Utilizing Collaboration To Win Against AI Algorithms; Here’s How

Visitors use their mobile devices to take pictures of themselves posing in a pink ball bath at the ... [+]DPA/AFP via Getty Images

It’s normal to hear the cliche, two are better than one, but this statement has been proven true among influencers in the last five years. Growing followership and influence on social media is not an easy feat, and with changing algorithms, it’s even more difficult.

Social media has become the top cultural influence on society. More people, especially Gen Z and Millennial generations, depend on social media platforms for their information, advice, and counsel, especially as regards lifestyle and fashion choices. This reality has placed social media influencers on a pedestal as some of the most relevant figures in modern culture.

The biggest question that influencers and aspiring influencers have to answer consistently is, “Can we still grow a healthy following organically in the age of changing algorithms?”

Renowned supermodel Stefanie Gurzanski seems to think that this is possible. In her words, “Value is king, the first step to doing it organically is finding your audience and determining what they consider as value. Then you have to create content around that and dish it out consistently. When all is said and done, you need help from others as well.”

As a supermodel, Stefanie Gurzanski, who is more popularly known as Baby G, has featured on the cover of some of the world’s most prestigious magazines like Vogue, Cosmopolitan, and Elle. She has also stunned at many of the world’s most prestigious fashion shows and red carpets.

However, one of her most significant accomplishments has been turning that success into a digital media empire and becoming an Instagram influencer with millions of extremely loyal followers. Stefanie believes that collaboration is one of the most powerful keys to growing followership in a world where algorithms are becoming more demanding. In this article, Stefanie shares why she thinks collaboration is so important.

According to Instagram, the main rule about Instagram algorithms is that not one but many algorithms influence a user’s Instagram experience. Different AI algorithms analyze user behavior on the different parts of the app; reels, feeds, and explore. All these algorithms work together to determine what we see or not see on Instagram.

The Instagram model is more or less the same on Facebook, Titok, and youtube, where various AI algorithms are used to achieve these exact results.

According to Stefanie, “Instagram has been a bit more transparent about how their algorithms function and this has helped influencers build systems around their brands that help them get the most out of the platform. What I have found is that the key ingredient that enables us to get the best out of all the algorithms is collaboration.”

Adam Mosseri has taken his time to summarize how the Instagram algorithm functions in a post. Four key factors influence the Instagram algorithm for feed posts.

  • First, the algorithm considers the basic information about the post; Is it a photo or a video? When was it posted? How many likes does it have?
  • Secondly, it considers the information about the poster; How interesting does the user find them to be? How often does the user engage with their content?
  • The third factor is the user’s activity; is the user a heavy video-watcher, or does the user prefer other kinds of content?
  • The final factor is the user’s interaction history; does the user typically engage with the poster’s posts by liking or commenting?

In summary, on Instagram feeds, the algorithm would typically show a user posts from creators who create the kind of content they typically engage with, and who they have previously engaged with or tend to engage with frequently.  On reels and explore, Instagram goes further by showing users the content they think they will like based on their previous activity.

Stefanie explains it with a more straightforward example; “I am coming up on the two million followers mark and one thing that is readily noticeable is that many of these followers found me because they initially followed other supermodels who do what I do.

Likewise, when my followers consistently engage with my pictures and videos, they are also likely to see posts from similar creators who do what I do on their explore page or their reels. Now imagine what we can achieve as influencers if we become more intentional about giving and getting this exposure. The results are simply stunning.”

Collaboration as a Powerful Tool For Exposure

Since 2016, when Instagram made these changes to its algorithm, collaboration has become perhaps the most effective tool for exposure.  Creators collaborate in different ways, but the end goal is the same, and the results are similar.

Live broadcasts by Sponsored Influencers have become a thing. 82% of audiences prefer live video from influencers and brands to other generic content, which has made live videos both the present and the future of video content.

Since Instagram launched its new feature, which enables creators and users to go live with a friend remotely, influencers have used this feature to invite other creators on their platform for live broadcasts. Content ranges from talk shows to casual conversations, and the results have been favorable so far.

“Live videos have such an engagement boosting effect,” Stefanie explains. “The algorithms give live videos more exposure, so when influencers can link up via this feature, it helps the algorithm make the connection between their two sets of audiences. This can easily grow the platforms of both influencers by exposing them to new audiences.”

Hosting Account takeovers is another powerful collaborative tool that creators use to force the algorithm to give more exposure to their content.  An account takeover is a strategy where an influencer can surrender their account to another influencer for some time, either by posting only content from that influencer or giving the influencer access to their platforms to post directly.

The basic idea is to create a feeling that that influencer is in charge for that period. This is a powerful way that influencers use to expose their work and brands to other influencers’ audiences. The results are usually remarkable as the algorithm is forced to make that connection.

Collaborating with Aspiring Influencers is also becoming more popular. Stefanie recently launched the Baby G Mag, a subscription-based platform where other creatives can be featured and grow their own platforms. She explains why she thinks this strategy is helpful; “With all the algorithm changes, it has become difficult to grow organically today than it was a few years ago.

So, I built Baby G Mag specifically for other girls who want to grow their brand, make money, and can be seen by a guaranteed amount of paying customers. Right now we are collating with other magazines as well. Playboy Mexico has already seen us as an interesting platform to collaborate with in just our first few weeks.”

While many influencers are looking to connect with influencers with bigger platforms, Stefanie realizes that collaborating with up-and-coming influencers is a win-win. It exposes her to new audiences as these smaller influencers usually have a smaller but fiercely loyal audience. It is also her way of helping these influencers grow faster and gain more influence.

User-Generated Content (UGC) is also becoming stronger among influencers. 85% of users find visual UGC more influential than brand photos or videos. It also drives 6.9-times-higher engagement than generic content.

UGC is a form of collaboration that Influencers build with their followers. This is effective because followers tend to engage with UGCs at a higher rate. Since the algorithm gives more exposure to posts and platforms with more engagement, the Influencer’s platforms usually get seen way more. It’s a win-win for most influencers.

Influencer Pods are becoming robust as well. Some influencers have decided that the best way to link their audiences is to create pods of influencers. These pods usually have 10-15 influencers from within their industry who engage authentically with each other’s content; they share, like, and leave thoughtful comments on their most recent posts.

Many see pods as a way to skew the system and game the algorithm, but so far, no social media platform has cracked down on them yet. Stefanie believes that collaboration in whatever shape or form it comes is the key to consistent growth as influencers;

“In my case, my fame didn’t necessarily come from social media, but I have been able to maintain it on social media by continuing with the things I became famous for. My merchandise, my videos, and my photos are what people value about my brand, and so I remain consistent.

However, I also believe that helping others rise has helped me rise even more. Collaboration will probably always be a successful route to go even if the algorithms change again, because it is at the heart of what social media is trying to do, which is to connect people and to help people discover other relevant people. It’s a silver bullet by every standard.”

Stefanie’s points are valid; at the heart of most social media platforms is a desire to connect people and increase engagement. Their money-making structures are built around these same values. So it is likely that finding valuable ways to collaborate will always win in the end, and It’s difficult to have any problems with it whatsoever.

Annie Brown is the founder of Lips, a feminist technology organization at the forefront of the inclusive design movement, building products

Source: Influencers Are Utilizing Collaboration To Win Against AI Algorithms; Here’s How

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Google Maps Could Promote Safe Bike Routes

 

While Google Maps increasingly offers information on where bike lanes are, its routing algorithms don’t offer the same level of nuance that drivers enjoy.  Google Maps has a suite of features to make driving easier. The app gives users options to avoid tolls and highways and even recommends low-emission routes where available.

Bikers using the app, though, have far fewer options, particularly when it comes to determining how safe a route is. Fixing that could get more people on bikes and e-bikes, two of the most accessible forms of no-carbon transit available today. Given that the transportation sector is the biggest contributor to U.S. greenhouse gas emissions, encouraging the use of alternatives to driving — especially driving gas-powered cars — is more urgent than ever.

With its abundant data and mapping resources, Google Maps is well-poised to create a powerful tool that keeps people safe while navigating their city by bike. Doing so could encourage the use of one of the most reliable zero-emissions transportation technologies, a benefit that dovetails nicely with Google’s ambitious emissions reduction goals.

That’s not to say it’s a cut-and-dried task, though. The puzzle of how to set up a mapping algorithm for driving is relatively simple compared to doing so for biking. Estimating roughly how long it will take to drive somewhere requires little more than knowing speed limits and whether or not intersections have stop signs or stop lights. For biking, though, finding the “right” route is a lot more qualitative.

Often, safety trumps speed. A quiet residential road with speed bumps but without a bike lane can feel more comfortable for bikers — especially new bikers — than a busy thoroughfare with a painted-on bike lane where delivery trucks tend to idle.

While Google Maps and other mapping apps increasingly offer information on where bike lanes are, its routing algorithms don’t offer the same level of nuance that drivers enjoy. Routing options for bikers, whose goals range from commuting to exercising, are largely missing from the platform. As a consequence, bikers have tended to rely more on crowdsourcing, either via the rider-to-rider grapevine or a patchwork of tech-focused alternatives that vary by country and city.

But the lack of a single, comprehensive bike-routing app represents an opportunity for tech companies like Google and Apple, especially given that the pandemic-related boom in biking seems to have staying power. Both companies have rolled out new features to flesh out their bike mapping features in the last year and have plans to continue improving them, but there’s still a long way to go before the mapping apps serve as a reliable alternative to crowdsourcing.

The status quo of simplistic routing options on the most popular mapping apps, said Warren Wells, the policy and planning director of the Marin County Bicycle Coalition, represents a barrier to entry for new riders. He worries that first-time riders will rely on Google Maps in the same way they do for driving and follow a route blindly; even if they use Google’s bike lane layer, it is not clear which bike lanes are fully protected by a physical barrier and which are simply painted onto the shoulder of a busy road.

“For 100 years, we have engineered every street to work fine for driving, more or less,” said Wells. “We have put just so little effort into making every street easy to bike on.”

If a new biker ends up on one of these many high-capacity roads that happen to have an often unprotected bike lane, they are liable to arrive at their destination scared or jaded and never get on a bike again. According to the Centers for Disease Control and Prevention, nearly 1,000 bicyclists are killed and over 130,000 are injured in the U.S. each year.

Some biking groups have stepped in to fill the route-mapping void, creating their own apps for cyclists. In the Netherlands, for instance, the national cyclists union, Fietsersbond, created its own mapping software that gives users qualitative options for their journeys. While the interface is not nearly as advanced as Google’s — Wells compared it to the early 2000s version of MapQuest — the app offers bikers many more ways to explore routes, including options like “easy,” “car-free,” “shortest” or “nature” routes.

Google Maps usually offers bikers three options for a biking journey, but it is generally not clear what distinguishes one from another, especially for someone new to a city or new to a bike. In 2018, the Chicago Reader’s John Greenfield created a guide to the city’s lowest-stress bike routes, dubbed the Mellow Chicago Bike Map, which incorporated the opinions of riders in a biking community forum online. (It was later updated as biking interest swelled mid-pandemic.)

Jean Cochrane, a Chicago-based civic technologist and casual biker, stumbled upon the map and turned it into a website with routing capacity, primarily for her own use. However, it has become widely used by others looking to get around Chicago.

“I think that has really been its own huge sea change in the way that I experienced biking,” Cochrane said, “where I do bike much further distances in the city between neighborhoods in a way that I never really have before because I feel like I have a way of accessing other neighborhoods safely.”

The website distinguishes between off-street bike paths, mellow streets (which are largely residential and often have infrastructure like speed bumps or traffic circles to slow down cars), main streets (which usually have bike lanes) and other streets. The simple routing software that Cochrane incorporates into the site prioritizes bike paths and mellow streets in suggesting routes to users.

Demand is high for this kind of resource. Cochrane said people have reached out to her about creating a version of the same webpage for other U.S. cities, but absent the crowdsourcing that Greenfield did initially for Chicago’s “mellow” streets, redoing the project from scratch is a heavy lift. This is in part because determining which streets are “mellow” is harder than it might appear: Cochrane characterized it as a “data problem.”

At least in Chicago, installing infrastructure to slow traffic is a largely decentralized process, and public data is hard to find. The open-source project OpenStreetMap has some of that information, Cochrane said, but it’s incomplete and user-generated, and thus difficult for her to rely upon.

“I know exactly what I would build, if I could know where every speed bump is in the city of Chicago,” she said. “I would love to be able to restrict my directions to residential streets or to streets with traffic-calming infrastructure.”

But, Cochrane said, if anyone is able to cobble together the data that’s relevant to bikers, it’s Google, which she described as a “data leviathan.” The company confirmed that it uses a combination of imagery and data from both government authorities and community contributions, and has partnerships with more than 10,000 local governments, transit agencies and other organizations globally. Google also has access to data on road type and quality, stairs, hills, and elevation.

“The value proposition of Google is that they have this omniscient understanding of all of the streets and businesses in so many different places in the U.S.,” Cochrane said.

In July, the company outlined plans to offer more bike route information. The routing sample included in the post illustrates a detailed breakdown of the type of road bikers encounter, from major thoroughfares to shared paths, and gives riders choices between routes with descriptors like “more bike lanes” and “less turns.” This functionality is slated to roll out “soon” in cities where Google Maps already offers biking directions, including New York, London and Tokyo; the company did not respond to questions from Protocol about a more precise timeline, though.

The Intergovernmental Panel on Climate Change’s latest report emphasizes that walkable communities — including protected pedestrian and bike pathways — can help cities reduce their emissions by encouraging low- or no-emission transportation options. With a user base that is more than 1 billion strong, Google Maps is uniquely positioned to effect a virtuous cycle: If more people are comfortable navigating their city by bike, that’s more people with a stake in improving low- and no-carbon infrastructure for getting around.

Source: Google Maps could promote safe bike routes – Protocol

Critics By  Alexis C. Madrigal

Behind every Google Map, there is a much more complex map that’s the key to your queries but hidden from your view. The deep map contains the logic of places: their no-left-turns and freeway on-ramps, speed limits and traffic conditions. This is the data that you’re drawing from when you ask Google to navigate you from point A to point B — and last week, Google showed me the internal map and demonstrated how it was built.

It’s the first time the company has let anyone watch how the project it calls GT, or “Ground Truth,” actually works. Google opened up at a key moment in its evolution. The company began as an online search company that made money almost exclusively from selling ads based on what you were querying for. But then the mobile world exploded. Where you’re searching from has become almost as important as what you’re searching for.

Google responded by creating an operating system, brand, and ecosystem in Android that has become the only significant rival to Apple’s iOS. And for good reason. If Google’s mission is to organize all the world’s information, the most important challenge — far larger than indexing the web — is to take the world’s physical information and make it accessible and useful.

“If you look at the offline world, the real world in which we live, that information is not entirely online,” Manik Gupta, the senior product manager for Google Maps, told me. “Increasingly as we go about our lives, we are trying to bridge that gap between what we see in the real world and [the online world], and Maps really plays that part.”

This is not just a theoretical concern. Mapping systems matter on phones precisely because they are the interface between the offline and online worlds. If you’re at all like me, you use mapping more than any other application except for the communications suite (phone, email, social networks, and text messaging).

Google is locked in a battle with the world’s largest company, Apple, about who will control the future of mobile phones. Whereas Apple’s strengths are in product design, supply chain management, and retail marketing, Google’s most obvious realm of competitive advantage is in information. Geo data — and the apps built to use it — are where Google can win just by being Google. That didn’t matter on previous generations of iPhones because they used Google Maps, but now Apple’s created its own service.

How the two operating systems incorporate geo data and present it to users could become a key battleground in the phone wars. But that would entail actually building a better map. The office where Google has been building the best representation of the world is not a remarkable place. It has all the free food, ping pong, and Google Maps-inspired Christoph Niemann cartoons that you’d expect, but it’s still a low-slung office building just off the 101 in Mountain View in the burbs.

I was slated to meet with Gupta and the engineering ringleader on his team, former NASA engineer Michael Weiss-Malik, who’d spent his 20 percent time working on Google Mars, and Nick Volmar, an “operator” who actually massages map data. “So you want to make a map,” Weiss-Malik tells me as we sit down in front of a massive monitor. “There are a couple of steps. You acquire data through partners.

You do a bunch of engineering on that data to get it into the right format and conflate it with other sources of data, and then you do a bunch of operations, which is what this tool is about, to hand massage the data. And out the other end pops something that is higher quality than the sum of its parts.” This is what they started out with, the TIGER data from the US Census Bureau (though the base layer could and does come from a variety of sources in different countries).

.

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Is Google Dying? Or Did the Web Grow Up?

A few weeks in the past my home had a septic-tank emergency, which is as terrible because it sounds. As unspeakable issues started to burble up from my bathe drain, I did what any smartphone-dependent individual would: I frantically Googled one thing alongside the strains of poop coming from bathe drain dangerous what to do. I used to be met with a slew of cookie-cutter web sites, most of which appeared rapidly generated and had been choked with sufficient repetitive buzzwords as to be barely readable.

Virtually all the things I discovered was unhelpful, so we did the old school factor and known as an expert. The emergency got here and went, however I stored desirous about these middling search outcomes—how they typified a zombified web wasteland. Like many, I exploit Google to reply most of the mundane questions that pop up in my day-to-day life. And but that first web page of search outcomes feels prefer it’s been surfacing fewer satisfying solutions currently. I’m not alone; the frustration has develop into a persistent meme: that Google Search, what many contemplate an indispensable instrument of recent life, is useless or dying.

For the previous few years, throughout varied boards and social-media platforms, folks have been claiming in viral posts that Google’s flagship product is damaged. Search google dying on Twitter or Reddit and you’ll see folks grousing about it going again to the mid 2010s. Lately, although, the criticisms have grown louder. In February, an engineer named Dmitri Brereton wrote a weblog publish about Google’s search-engine decay, rounding up main theories for why the product’s “results have gone to shit.”

The publish rapidly shot to the prime of tech boards similar to Hacker News and was extensively shared on Twitter and even prompted a PR response from Google’s Search liaison, Danny Sullivan, refuting one among Brereton’s claims. “You said in the post that quotes don’t give exact matches. They really do. Honest,” Sullivan wrote in a series of tweets.

Brereton’s most intriguing argument for the demise of Google Search was that savvy customers of the platform not kind instinctive key phrases into the search bar and hit enter. The finest Googlers—the ones on the lookout for actionable or area of interest data, product critiques, and attention-grabbing discussions—know a cheat code to bypass the sea of company search outcomes clogging the prime third of the display. “Most of the web has become too inauthentic to trust,” Brereton argued, due to this fact “we resort to using Google, and appending the word ‘reddit’ to the end of our queries.”

Brereton cited Google Trends knowledge that present that persons are looking the phrase reddit on Google greater than ever earlier than. Instead of scrolling via lengthy posts plagued by pop-up adverts and paragraphs of barely coherent search engine optimization chum to get to a evaluation or a recipe, intelligent searchers received vigorous threads with testimonials from actual folks debating and interacting with each other. Most who use the Reddit hack are doing so for sensible causes, but it surely’s additionally a small act of protest—a technique to stick it to the Search Engine Optimization and Online Ad Industrial Complex and to aim to entry part of the web that feels freer and extra human.

Google has constructed wildly profitable cellular working methods, mapped the world, modified how we e mail and retailer pictures, and tried, with various success, to construct vehicles that drive themselves. This story, for instance, was researched, partly, via numerous Google Search queries and a few Google Chrome searching, written in a Google Doc, and filed to my editor through Gmail. Along the manner, the firm has collected an unfathomable quantity of information on billions of individuals (regularly unbeknownst to them)—however Google’s mother or father firm, Alphabet, remains to be primarily an promoting enterprise.

In 2020, the firm made $147 billion in income off adverts alone, which is roughly 80 p.c of its whole income. Most of the tech firm’s merchandise—Maps, Gmail—are Trojan horses for a gargantuan personalized-advertising enterprise, and Search is the one which began all of it. It is the fashionable template for what the know-how critic Shoshana Zuboff termed “surveillance capitalism.” The web has grown exponentially and Google has expanded with it, serving to usher in a few of the internet’s greediest, most extractive tendencies. But scale shouldn’t be all the time a blessing for know-how merchandise.

Are we wringing our fingers over nothing, or is Google a sufferer of its personal success, rendering its flagship product—Search—much less helpful? One can’t actually overstate the manner that Google Search, when it rolled out in 1997, modified how folks used the web. Before Google got here out with its purpose to crawl the complete internet and set up the world’s data, search engines like google and yahoo had been reasonably helpful at finest. And but, in the early days, there was far more search competitors than there may be now; Yahoo, Altavista, and Lycos had been common on-line locations.

But Google’s “PageRank” rating algorithm helped crack the downside. The algorithm counted and listed the quantity and high quality of hyperlinks that pointed to a given web site. Rather than use a easy key phrase match, PageRank figured that the finest outcomes can be web sites that had been linked to by many different high-quality web sites. The algorithm labored, and the Google of the late Nineteen Nineties appeared virtually magical: You typed in what you had been on the lookout for, and what you bought again felt not simply related however intuitive. The machine understood.

Most folks don’t want a historical past lesson to know that Google has modified; they really feel it. Try trying to find a product in your smartphone and also you’ll see that what was as soon as a small teal bar that includes one “sponsored link” is now a hard-to-decipher, multi-scroll slog, crammed with paid-product carousels; a number of paid-link adverts; the dreaded, algorithmically generated “People also ask” field; one other paid carousel; a sponsored “buying guide”; and a Maps widget exhibiting shops promoting merchandise close to your location. Once you’ve scrolled via that, a number of display lengths beneath, you’ll discover the unpaid search outcomes. Like a lot of the web in 2022, it feels monetized to dying, soulless, and exhausting.

There are every kind of theories for these ever-intrusive adverts. One is that the cost-per-click charges that Google costs advertisers are down, due to competitors from Facebook and Amazon (Google is rolling out bigger commerce-search advert widgets in response this 12 months) in addition to a slowdown in paid-search-result spending. Another problem could stem from cookie-tracking modifications that Google is implementing in response to privateness legal guidelines similar to Europe’s General Data Protection Regulation and the California Consumer Privacy Act.

For the previous two years, Google has been planning to take away third-party cookies from its Chrome browser. And although Google Search received’t be affected by the cookie ban, the glut of search adverts may be an try to recoup a few of the cash that Google stands to lose in the modifications to Chrome. If so, that is an instance of fixing one downside whereas creating one other. But after I instructed this to Google, the firm was unequivocal, arguing that “there is no connection” between Chrome’s plans to part out assist for third-party cookies and Search adverts.

The firm additionally stated that the variety of adverts it exhibits in search outcomes “has been capped for several years, and we have not made any changes.” Google claims that, “on average over the past four years, 80 percent of searches on Google haven’t had any ads at the top of search results.” Any hunt for solutions about Google’s Search algorithms will lead you into the world of search engine optimization consultants like Marie Haynes. Haynes is a marketing consultant who has been learning Google’s algorithms obsessively since 2008. Part of her job is to maintain up with each small change made by the firm’s engineers and public communication by Google’s Search-team weblog.

Companies that may divine the whims of Google’s continuously up to date algorithms are rewarded with coveted web page actual property. Ranking excessive means extra consideration, which theoretically means more cash. When Google introduced in October 2020 that it will start rolling out “passage indexing”—a brand new manner for the firm to drag out and rank discrete passages from web sites—Haynes tried to determine how it will change what folks finally see after they question.

Rather than reverse engineer posts to sound like bot-written babble, she and her workforce try to stability sustaining a web page’s integrity whereas additionally interesting to the algorithm. And although Google offers search engine optimization insiders with frequent updates, the firm’s Search algorithms are a black field (a commerce secret that it doesn’t need to give to rivals or to spammers who will use it to govern the product), which implies that realizing what sort of data Google will privilege takes a whole lot of educated guesswork and trial and error.

Haynes agrees that adverts’ presence on Search is worse than ever and the firm’s determination to prioritize its personal merchandise and options over natural outcomes is irritating. But she argues that Google’s flagship product has truly gotten higher and far more advanced over time. That complexity, she suggests, may be why looking feels totally different proper now. “We’re in this transition phase,” she advised me, noting that the firm has made vital developments in synthetic intelligence and machine studying to decipher consumer queries.

Those technical modifications have precipitated it to maneuver away from the PageRank paradigm. But these efforts, she instructed, are of their infancy and maybe nonetheless figuring out their kinks. In May 2021, Google introduced MUM (brief for Multitask Unified Model), a natural-language-processing know-how for Search that’s 1,000 instances extra highly effective than its predecessor.

“The AI attempts to understand not just what the searcher is typing, but what the searcher is trying to get at,” Haynes advised me. “It’s trying to understand the content inside pages and inside queries, and that will change the type of result people get.” Google’s concentrate on searcher intent may imply that when folks kind in key phrases, they’re not getting as many direct phrase matches. Instead, Google is attempting to scan the question, make which means from it, and floor pages that it thinks match that which means. Despite being a bit sci-fi and creepy, the shift may really feel like a lack of company for searchers.

Search used to really feel like a instrument that you simply managed, however Google could begin to behave extra like, effectively, an individual—a concierge that has its personal concepts and processes. The problematic results of elevated AI inference over time are simple to think about (whereas I used to be writing this text, a Google researcher went viral claiming he’d been positioned on administrative depart after notifying the firm that one among its AI chatbots—powered by totally different know-how—had develop into sentient, although the firm disagrees).

Google may use such know-how to proceed to steer folks away from their meant searches and towards its personal merchandise and paid adverts with higher frequency. Or, much less deviously, it may merely gently algorithmically nudge folks in sudden instructions. Imagine all the life selections that you simply make in a given 12 months primarily based on data you course of after Googling. This implies that the stakes of Google’s AI deciphering a searcher’s intent are excessive.

But a few of Google’s lifeless outcomes are made by people. Zach Verbit is aware of what it’s prefer to serve at the pleasure of Google’s Search algorithms. After school, Verbit took a freelance-writing gig with the HOTH, a advertising firm that makes a speciality of search-engine optimization. Verbit’s “soul crushing” job at the HOTH was to jot down weblog posts that might assist purchasers’ websites rank extremely. He spent hours composing listicles with titles like “10 Things to Do When Your Air-Conditioning Stopped Working.”

Verbit wrote posts that “sounded robotic or like they were written by somebody who’d just discovered language.” He needed to write as much as 10 posts a day on topics he knew nothing about. Quickly, he began repurposing previous posts for different purchasers’ blogs. “Those posts that sound like an AI wrote them? Sometimes they’re from real people trying to jam in as many keywords as possible,” Verbit advised me.

That his rapidly researched posts appeared excessive in search outcomes left him dispirited. He stop the job after a 12 months, describing the business of search-gaming as a home of playing cards. His time in the search engine optimization mines signaled to him the decline of Google Search, arguably the easiest, simplest, and most revolutionary product of the fashionable web. “The more I did the job, the more I realized that Google Search is completely useless now,” he stated.

HOTH’s CEO, Marc Hardgrove disputed the notion that its shopper weblog posts had been “over-optimized” for search engine optimization functions and that the firm discourages jargony posts as they don’t rank as excessive. “Overusing keywords and creating un-compelling content would be detrimental to our success as an SEO company, he wrote in an email. “That’s why The HOTH does not require, or even encourage, the writers we work with to overuse keywords into their blog posts to help with optimization.”

Google is nonetheless helpful for a lot of, however the tougher query is why its outcomes really feel extra sterile than they did 5 years in the past. Haynes’s principle is that that is the results of Google attempting to crack down on misinformation and low-quality content material—particularly round consequential search matters. In 2017, the firm began speaking publicly a couple of Search initiative known as EAT, which stands for “expertise, authoritativeness, and trustworthiness.”

The firm has rolled out quite a few high quality rater pointers, which assist choose content material to find out authenticity. One such effort, titled Your Money or Your Life, applies rigorous requirements to any pages that present up when customers seek for medical or monetary data. “Take crypto,” Haynes defined. “It’s an area with a lot of fraud, so unless a site has a big presence around the web and Google gets the sense they’re known for expertise on that topic, it’ll be difficult to get them to rank.”

What this implies, although, is that Google’s outcomes on any matter deemed delicate sufficient will probably be from established sources. Medical queries are way more prone to return WebMD or Mayo Clinic pages, as a substitute of non-public testimonials. This, Haynes stated, is very difficult for folks on the lookout for homeopathic or alternative-medicine treatments. There’s a wierd irony to all of this. For years, researchers, technologists, politicians, and journalists have agonized and cautioned towards the wildness of the web and its penchant for amplifying conspiracy theories, divisive material, and flat-out false data.

Many folks, myself included, have argued for platforms to floor high quality, authoritative data above all else, even at the expense of revenue. And it’s potential that Google has, in some sense, listened (albeit after far an excessive amount of inaction) and, perhaps, partly succeeded in exhibiting higher-quality leads to a variety of contentious classes. But as a substitute of ushering in an period of excellent data, the modifications may be behind the complainers’ sense that Google Search has stopped delivering attention-grabbing outcomes.

In principle, we crave authoritative data, however authoritative data could be dry and boring. It reads extra like a authorities kind or a textbook than a novel. The web that many individuals know and love is the reverse—it’s messy, chaotic, unpredictable. It is exhausting, endless, and all the time just a little bit harmful. It is profoundly human. But it’s price remembering what that humanity appeared like inside search outcomes. Rand Fishkin, the founding father of the software program firm SparkToro, who has been writing and desirous about search since 2004, believes that Google has gotten higher at not amplifying conspiracy theories and hate speech, however that it took the firm far too lengthy.

“I don’t know if you searched for holocaust information between 2000 and 2008, but deniers routinely showed up in the top results,” he advised me. The similar was true for Sandy Hook hoaxers—in reality, campaigns from the Sandy Hook households to combat the conspiracy theories led to a few of the search engine’s modifications. “Whenever somebody says, ‘Hey, Google doesn’t feel as human anymore,’ all I can say is that I bet they don’t want a return to that,” Fishkin stated.

Google Search may be worse now as a result of, like a lot of the web, it has matured and has been ruthlessly commercialized. In an try to keep away from regulation and be corporate-friendly, elements of it may be much less wild. But a few of what feels useless or dying about Google may be our personal nostalgia for a smaller, much less mature web. Sullivan, the Search liaison, understands this eager for the previous, however advised me that what seems like a Google change can be the search engine responding to the evolution of the internet.

“Some of that blog-style content has migrated over time to closed forums or social media. Sometimes the blog post we’re hoping to find isn’t there.” Sullivan believes that a few of the latest frustrations with Google Search truly replicate simply how good it’s develop into. “We search for things today we didn’t imagine we could search for 15 years ago and we believe we’ll find exactly what we want,” he stated. “Our expectations have continued to grow. So we demand more of the tool.” It’s an attention-grabbing, albeit handy, response.

Google has rewired us, reworking the manner that we consider, course of, entry, and even conceive of knowledge. “I can’t live without that stuff as my brain is now conditioned to remember only snippets for Google to fill in,” one Reddit consumer wrote whereas discussing Brereton’s “Google Is Dying” publish. Similarly, Google customers form Search. “The younger generation searches really differently than I do,” Haynes advised me. “They basically speak to Google like it’s a person, whereas I do keyword searching, which is old-school.” But these quirks, tics, and ranging behaviors are simply knowledge for the search large.

When youthful generations intuitively begin speaking to Google prefer it’s an individual, the instrument begins to anticipate that and begins to behave like one (that is a part of the purpose behind the rise of humanized AI voice assistants). Fishkin argues that Google Search—and plenty of of Google’s different merchandise—can be higher with some competitors and that Search’s high quality improved the most from 1998 to 2007, which he attributes to the firm’s must compete for market share. “Since then,” he stated, “Google’s biggest search innovation has been to put more Google products up front in results.”

He argues that this technique has truly led to a slew of underwhelming Google merchandise. “Are Google Flights or Google Weather or Google’s stocks widget better than competitors? No, but nobody can really compete thanks to the Search monopoly.” “Is Google Search dying?” is a frivolous query. We care about Search’s destiny on a sensible stage—it’s nonetheless a major technique to faucet into the web’s promise of limitless data on demand. But I believe we additionally care on an existential stage—as a result of Google’s first product is a placeholder to discover our hopes and fears about know-how’s place in our life.

We yearn for extra comfort, extra innovation, extra chance. But after we get it, usually we are able to solely see what we’ve misplaced in the course of. That loss is actual and deeply felt. It’s like shedding a bit of our humanity. Search, due to its utility, is much more fraught. Most folks don’t need their data mediated by bloated, monopolistic, surveilling tech firms, however additionally they don’t need to go all the manner again to a time earlier than them. What we actually need is one thing in between. The evolution of Google Search is unsettling as a result of it appears to recommend that, on the web we’ve constructed, there’s little or no room for equilibrium or compromise.

By: James Crugnale

Source: http://theatlantic.com

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Why Expensive Social Media Monitoring Has Failed To Protect Schools

There’s a set of questions that comes up with grim hindsight after a shooting like the one in Uvalde, Texas: Were there signs? Did we miss them? Could we have caught this? An entire industry has sprung up claiming that it has the answer: software that scans social media for threats.

Ari Sen, a computational journalist for the Dallas Morning News, has reported that the Uvalde Consolidated Independent School District purchased one of these social media monitoring services, called Social Sentinel, a few years ago. Right now, Ari says it’s hard to know if the software was active in Uvalde at the time of the shooting—the school district hasn’t answered that yet.

But the bigger question is whether the posts on the gunman’s now-removed Instagram page—including lots of photos of AR-15-style rifles and weapons—would even have been flagged by the software. Why then are schools spending millions of dollars on this software, and why does the industry claim it helps protect students?

On Friday’s episode of What Next: TBD, I spoke with Ari Sen about what threat surveillance software promises and how it falls short. Our conversation has been edited and condensed for clarity.

Lizzie O’Leary: Can you explain what Social Sentinel is and who uses it?

Ari Sen: Social Sentinel is a social media monitoring technology. It’s used by dozens of colleges and hundreds of school districts all around the country. What they claim to do is to scan billions of social media posts with really sophisticated AI to identify threats of potential violence or self-harm. Now, some of the reporting that I’ve done suggests that these models may not be very sophisticated or that this might be a really hard problem to solve even if the models are very sophisticated.

In your reporting, you found that several school districts that bought this software, spending between $1 and $2 per student, weren’t getting all that much for their money.

Most of the school districts that we talked to that had used Social Sentinel did not find the service to be useful. I contacted every school that we could find that had used Social Sentinel and the three other social media monitoring services that we studied in the state of Texas. Over 200 school districts had used one of these four services since 2015. Most of them did not respond to my questions, but there were a handful, maybe five or six, who did actually respond. I would say four or five of those said that “We canceled the service after a year. We didn’t find it to be useful. Or we found something, an anonymous reporting tool, a team of humans to monitor this stuff. We found that to be as good or better than the Social Sentinel service.”

One thing that I’ve heard a lot, not only from school districts but from colleges, is that 90 percent, 99 percent of the stuff that they were getting from the Social Sentinel service was false alerts. I’ve seen stuff like song lyrics, Bible verses, obvious jokes. If you just think about the way that people talk on social media, it’s a lot of sarcasm. It’s a lot of irony. It’s a lot of hyperbole. That can be really difficult for machine learning models to catch in general and particularly the less sophisticated stuff.

Do you have any examples of posts that got flagged where you thought, “Oh, come on. That’s someone tweeting lyrics”?

There is a college in Florida that I was able to get some flagged tweets from. Somebody tweeted the lyrics to the 2010 B.o.B song “Airplanes.” I think it picked up on the phrase “shooting stars.” Obviously, we’ve seen people tweeting about their favorite characters on TV shows: “If X character doesn’t get together with Y character, I’m literally going to die,” things like that. There’s a really funny tweet from one of these Florida colleges about Hamburger Helper and how Hamburger Helper needs to accept that it needs help.

They thought that was a mental health problem?

Evidently. Like I said, it’s hard to inspect these machine learning models. We don’t know for sure what exactly is going on behind the scenes there. But I am able to look at some of the things that they have flagged, and they don’t seem to be threatening at all. What we’ve heard anecdotally from schools and colleges is like, “Yeah, most of what we were getting is just not actionable.”

Is the algorithm searching for keywords? Does it look for shoot, kill, stab?

If you looked at Social Sentinel, the way they talked about the service early on, it very much sounds like a keyword-based service. They talk about how they have thousands of terms that they’re able to flag to school districts. The company now says that they have very sophisticated machine learning models. They have these eight different machine learning models that are able to classify text appropriately.

It’s also unclear exactly how these models work because the companies treat their algorithms as proprietary. They also say it would defeat the purpose of their work to disclose too much.

We don’t know what sorts of training data they have to go into the models, whether that training data has been audited for racial bias. All of that stuff is opaque to us. It really raises questions about, if schools are going to use this for such a serious and important purpose, should there be some transparency about the models, the training data, and how effective they are?

Moreover, machine learning models often struggle with slang and the way kids talk. That can mean posts from students of color are disproportionately flagged by the algorithms.

There was a really interesting paper by some UMass Amherst researchers a couple years ago where they took African American Vernacular English and they plugged it into language identification machine learning models. Obviously, what it should spit out is that this is English. In actuality, one of these models flagged that language as Dutch with 99 percent confidence. So these models do poorly on non-Anglicized English text in general and may exhibit the biases from the training data which they were trained on. If you look at Social Sentinel’s claims, for example, on their website, they say, “We don’t perpetuate any biases.” The experts that I’ve talked to have said that’s very difficult to do if the underlying models you’re using behind the scenes have these sorts of biases built in.

While Social Sentinel claims it covers almost all of social media, your reporting and work from BuzzFeed News suggests it mostly just monitors Twitter. Do you think these services can even keep up with how students use social media as they jump from platform to platform?

Obviously, you have the problem with young people hopping between different services. The big thing now is TikTok, for example. Maybe 10 years ago, it was Facebook. It’s hard for these platforms to keep up. Then you also have the ways in which language changes naturally over time. Then, as we were just talking about, language differs very widely across groups and geographies. The way people talk in California is not the same way that people talk in North Carolina.

I saw that the Uvalde shooter was using a service called Yubo, which I suspect these companies are not monitoring.

I hadn’t even heard of Yubo. One of the things I’ve seen in my reporting on Social Sentinel is that police chiefs going back to 2015 were constantly bugging Social Sentinel: Can you add this platform? Can you add this platform? Sometimes they did, and sometimes they didn’t. But it’s very, very difficult to keep up with the fast-paced nature of how young people are acting online.

Listening to you, there seems to be a pretty substantial body of evidence that these services are largely ineffective. Do you think that’s a fair assessment? And if it is, why are they still being touted as a solution?

We haven’t really been able to identify a clear case of this service working. We have heard some anecdotes about maybe some of the other services preventing kids from harming themselves. But I think the question we have to ask is, is it worth the privacy invasion? I found in my reporting last year that most of the time students and parents weren’t told at all that these services were in place and had no way to opt in or opt out. What needs to happen is a more open conversation about, “This is the service that we’re using, this is why we’re using it, and this is the things that it looks for.”

If these schools and universities are under such pressure to do something, but the debate around guns is either a nonstarter where they are or completely out of their hands, maybe this software feels like a reed they can grasp.

We’re obviously having a larger political conversation about what gun restrictions we do and we don’t want. But I think for schools, they’re desperate to do something to protect their kids, whether that’s school safety drills, monitoring services, like the ones we’ve been talking about here, whether it’s physical security, like metal detectors or other sorts of physical security measures. But I think one of the things that the Uvalde shooting shows us is that even school districts that have all of those things in place and have all of the training and all the officers, these things can fail and do fail. So there needs to be a different conversation that’s happening about what measures are effective and whether new approaches are needed to tackle this problem.

These kinds of programs ingest a tremendous amount of information and data. To work best, they need to do that. It does make me wonder for what other reasons a school or a school district or university might want to have this information or might utilize this information?

Some of the things that I’ve been seeing in my reporting, particularly at the college level, is that colleges are adopting these services to monitor protests and activism. Obviously, that’s very chilling. In 2016, there was a company called Geofeedia that got caught monitoring Black Lives Matter protesters. But Geofeedia is not the only player out there obviously. My reporting has suggested that these other services, particularly Social Sentinel, at the college level may be used to monitor protests and activism.

What did the schools say when you asked them about this?

I have contacted every college that we know of that’s used Social Sentinel and asked about this question specifically. A lot of them don’t want to talk about this. We haven’t really heard a full-throated defense of, “We’re monitoring this protest to keep students safe.” A lot of them are very tight-lipped, so we have to rely on documents and whistleblowers inside of the company to give us information.

Does the company say, “Yeah, we know our stuff is being used to monitor protesters”?

The company fervently denies any ability or use of the service to monitor protesters in any way, and they have since the beginning. But that claim is very dubious.

It’s worth remembering that most of these services are being paid for with public money. An investigation by BuzzFeed News examined contracts from 130 schools and found that they collectively spent $2.5 million on social media monitoring over five years. If you are listening to this and you’re a parent or a teenager or a college student, what other kinds of questions you think you should be asking your educators, your administration about these services?

Well, first of all, I think it’s just important to know whether the service is in place or not. For example, when I was reporting on these four social media monitoring companies last year, I discovered that my high school had used Gaggle, one of the monitoring services. I knew from previous reporting that my undergraduate institution, UNC Chapel Hill, used Social Sentinel. First of all, we should just ask the campus police department, the school administrators, “What service are you using? What does it monitor for, and why are you using it?”

The next questions are, is it effective? Is it doing what it’s set out to do, what they claimed it could do when they were marketing the service to you? If it’s not, then I think people really have to raise questions about, why are we still using this thing if it doesn’t work for the thing that they said it works for?

More contents:

What An Ethical Data Strategy Can Look Like

That’s according to Angela Benton, the founder and CEO of Streamlytics, a company that collects first-party consumer data transparently and aims to disrupt the current model of third-party mining of data from cookies and other methods that raise privacy and ethics concerns. Most recently, she was named one of Fast Company‘s Most Creative People for helping consumers learn what major companies know about them and paying them for the data they create while using streaming services like Netflix or Spotify.

In the latest Inc. Real Talk streaming event, Benton explains that she founded the company with minorities in mind, particularly the Black and Latinx communities, because of the disproportionate way they’ve been affected by data and privacy. For example, she points to the recent controversy over facial recognition data being sold to the police, which has a much higher error rate when comparing data of Black and Asian male faces, which could potentially lead to wrongful arrests.

“That becomes extremely important when you think of what artificial intelligence is used for in our day-to-day world,” she says, noting that AI is used for everyday interactions like loan applications, car applications, mortgages, and credit cards. Using her company’s methods, Benton says, clients can secure ethically sourced data, so that algorithms won’t negatively affect communities that have historically suffered from discriminatory practices.

Here are a few suggestions from Benton for finding data ethically without relying on third-party cookies.

Do your own combination of data sets.

“How [Streamlytics] gets data is very old school,” Benton says. Instead of relying on tech to combine data points, she says, you can manually compare data you already own and make assumptions using your best judgment. You may have data from a Shopify website, for example, about the demographic of your customers, and then you can go to a specific advertiser, like Hulu, for instance, to then target people that fit that profile.

Use your data to discover new products.

You can also look to your data to find common searches or overlapping interests to get ideas for new products, Benton says. Often, she says, she receives data requests from small business owners to discover ideas that aren’t currently on the market, for example, a vegan searching for a vitamin.

This combination method surprised Benton when she presented clients with data. “I thought it was going to be more focused on just like, “How can I make more money?” she says. “But we are hearing from folks that they want access to data to use it in more creative ways.”

Don’t take social media data at face value.

Benton and her company purposely do not source social media data because she thinks the data leave too much out of the full picture. You may get a customer’s age and “likes” from a social media page, but that doesn’t tell you what they’re searching for or what their habits are.

Related:

Data Privacy: 4 Things Every Business Professional Should Know

5 Applications of Data Analytics in Health Care

Data Science Principles

“That’s not, to me, meaningful data. That’s not where the real value lies,” she says. “We’re not focused on what people are doing on social media, we’re focused on all of the activities outside of that.” She gave a scenario where a consumer is watching Amazon Prime, while also scrolling on Uber Eats to find dinner.

Data signals are happening at the same time, but they’re not unified. It’s up to businesses to connect the dots. To Benton, that’s more meaningful than what you’re posting and what you’re liking on social media.

Source: What an Ethical Data Strategy Can Look Like | Inc.com

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References:

“Datafication and empowerment: How the open data movement re-articulates notions of democracy, participation, and journalism”.

“Who Owns the Data? Open Data for Healthcare”.

“Note – The Right to Be Forgotten”.

“Big Data ethics”.

“Data workers of the world, unite”.

“Challenges and Opportunities of Big Data in Health Care: A Systematic Review”

Personal Data trading Application to the New Shape Prize of the Global Challenges Foundation

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences

“Methodology – Global Open Data Index

The Open Knowledge Foundation

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