Learning the Aboutness of a Web Site or Other Other Online Text Object
February 7, 2017
Quite by accident the Beyond Search goslings came across a company offering a free semantic profile of online text objects. The idea is to plug in a url like www.arnoldit.com/wordpress. The Leiki system will generate a snapshot of the concepts and topics the content object manifests. We ran the Beyond Search blog through the system. Here’s what we learned:
The system identified that the blog covers Beyond Search. We learned that our coverage of IBM is more intense than our coverage of the Google. But if one combines the Leiki category “Google Search” with the category “Google,” our love of the GOOG is manifest. We ran several other blogs through the Leiki system and learned about some content fixations that were not previously known to us.
We suggest you give the system a whirl.
The developer of the system provides a range of indexing, consulting, and semantic services. More information about the firm is at www.leiki.com.
Stephen E Arnold, February 7, 29017
IQwest IT Steps Up Its Machine Translation Marketing
February 3, 2017
Machine translation means that a computer converts one language into another. The idea is that the translation is accurate; that is, presents the speaker’s or writer’s message payload without distortion, odd ball syntax, and unintended humor. What’s a “nus”? The name of a nuclear consulting company or a social mistake? Machine translation, as an idea, has been around since that French whiz Descartes allegedly cooked up the idea in the 17th century.
I read two almost identical articles, which triggered by content marketing radar. The first write up appeared in KV Empty Pages as “Finding the Needle in the Digital Multilingual Haystack.” The second article appeared in the Medium online publication as “Finding the Needle in the Digital Multilingual Haystack.”
Notice the similarity. Intrigued I ran a query for IQwest. I noted that the domain name IQwest.com refers to a bum domain name. I did a bit of poking around and learned that there are companies using IQwest for engineering services, education, and legal technologies. The IQwest.com domain is owned by Qwest Communications in Denver.
The machine translation write up belongs to the IQwestIT.com group. No big deal, of course, but knowing which company’s name overlaps with other companies’ usage is interesting.
Now what’s the message in these two identical essays beyond content marketing? For me, the main point is that a law firm can use software translation to eliminate documents irrelevant to the legal matter at hand. For documents not in the lawyer’s native language, machine translation can churn out a good enough translation. The value of machine translation is that it is cheaper than a human translator and a heck of a lot less expensive.
Okay, I understand, but I have understood the value of machine translation since I had access to a Systran based system years ago. Furthermore, machine translation systems have been an area of interest in some of the government agencies with which I am familiar for decades.
The write up states:
building a model and process that takes advantage of benefits of various technologies, while minimizing the disadvantages of them would be crucial. In order to enhance any and all of these solution’s capabilities, it is important to understand that machines and machine learning by itself cannot be the only mechanism we build our processes on. This is where human translations come into the picture. If there was some way to utilize the natural ability of human translators to analyze content and build out a foundation for our solutions, would we be able improve on the resulting translations? The answer is a resounding yes!
Another, okay from me. The solution, which I anticipated, is a rah rah for the IQwest machine translation system. What’s notable is that the number of buzzwords used to explain the system caught my attention; for instance:
- Classification
- Clustering
- N grams
- Summarization
These standard indexing functions are part of the IQwest machine translation system. That system, the write up notes, can be supplemented with humans who ride herd on the outputs and who interact with the system to make sure that entities (people, places, things, events, etc.) are identified and translated. This is a slippery fish because some persons of interest have different names, handles, nicknames, code words, and legends. Informed humans might be able to spot these entities because no system with which I am familiar is able to knit together some well crafted aliases. Remember those $5,000 teddy bears on eBay. What did they represent?
The write up seems to be aimed at attorneys. I suppose that group of professionals may not be aware of the machine translation systems available online and for on premises installation. For the non attorney reader, the write up tills some familiar ground.
I understand the need to whip up sales leads, but the systems available from Google and Microsoft, to name just two work reasonably well. When those systems are not suitable, one can turn to SDL or Systran, to name two vendors with workable systems.
Net net: My thought is that two identical versions of the same article directed at a legal audience represents a bit of marketing wonkiness. The write up’s shotgun approach to reaching attorneys is interesting. I noticed the duplication of content, and my hunch is that Google’s duplicate detection system did as well.
Perhaps placing the write up in an online publication reaching lawyers would be a helpful use of the information? What’s clear is that IQwest represents an opportunity for some motivated marketing expert to offer his or her services to the company.
My take is that IQwest offers a business process for reducing costs for litigation related document processing. The translation emphasis is okay, but the idea of making a phone call and getting the job done is what differentiates IQwest from, for example, the GOOG. I remember Rocket Docket. A winner. When I looked at that “package,” the attorneys with whom I spoke did not care about what was under the hood. The hook was speed, reduced cost, and more time to do less dog work.
But the lawyers may need to hurry. “Lawyers Are Being Replaced by Machines That Read.” Dragging one’s feet technologically and demanding high salaries despite a glut of legal eagles may change the game and quickly.
Plus, keep in mind FreeTranslations.org. You can get voice translations as well as text translations. The increasingly frugal Google has trimmed its online translation service. Sigh. The days of pasting lengthy text into a box is gone like a Loon balloon drifting away from Sri Lanka.
There are options, gentle reader.
Stephen E Arnold, February 3, 2017
Google Semantics Sort of Explained by an SEO Expert
February 1, 2017
I know that figuring out how Google’s relevance ranking works is tough. But why not simplify the entire 15 year ball of wax for those without a grasp of Messrs. Brin and Page, their systems and methods, and the wrapper software glued on the core engine. Keep in mind that it is expensive and time consuming to straighten a bent frame when one’s automobile experiences a solid T bone impact. Google’s technology foundation is that frame, and over the years, it has had some blows, but the old girl keeps on delivering advertising revenue.
I read “Semantic Search for Rookies. How Does Google Search Work” does not provide the obvious answer; to wit:
Well enough for the company to continue to show revenue growth and profits.
The write up takes a different tact toward the winds of relevance. I highlighted this passage:
Google’s semantic algorithm hasn’t developed overnight. It’s a product of continuous work:
- Knowledge Graph (2012)
- Hummingbird (2013)
- RankBrain (2015)
- Related Questions and Rich Answers (ongoing)
The work began many years before 2012, but that is of no consequence to the SEO whiz explaining how Google search works.
The write up then brings up the idea of semantic and relevance obstacles. I won’t drag issues such as disambiguation, a user’s search history, and Google’s method of dealing with repetitive queries. I won’t comment on Ramanathan Guha’s inventions nor bring up the word in semantics which began when Jeff Dean revealed how many versions of Britney Spears name were in one of Google’s suggested search subsystems.
The way to take advantage of where Google is today boils down to writing an article, a blog post similar to this one you are reading, or any textual information to employing user oriented phrasing and algorithm oriented phrasing. The explanation of these two types of phrasing was too sophisticated for me. I urge you, gentle reader, to consult the source document and learn yourself by sipping from the font of knowledge. (I would have used the phrase “Pierian spring” but that would have forced me to decide whether I was using a bound phrase, semantic oriented phrase, or algorithm oriented phrase. That’s too much work for me.
The write up concludes with these injunctions:
If you wish to create well-optimized content, you shouldn’t focus on text in the traditional sense. Instead, you should focus on words and word formation which Google expects to see. In this day and age, users’ feedback plays a crucial role in determining the importance of content. You will have to cater to both sides. Create content with lots of synonyms and semantically related words incorporated in it. Try to be provocative and readable at the same time.
I don’t want to rain on the SEO poobah’s parade, but there are some issues that this semantic write up does not address; namely, the challenge of rich media. How does one get one’s video indexed in a correct way in YouTube.com, GoogleVideo.com, Vimeo.com, or one of the other video search systems. What about podcasts, still images, Twitter outputs, public Facebook goodies, and social media image sharing sites?
My point is that defining semantics in terms of a particular content type suggests that Google has a limited repertoire of indexing, metatagging, and cross linking methods. Perhaps a quick look at Dr. Guha’s semantic server would shed some light on the topic? Well, maybe not. This is, after all, SEO oriented with semantic and algorithmic phrasing I suppose.
Stephen E Arnold, February 1, 2017
Semantic Search and Old Style Marketing
January 27, 2017
I read “It Used to Be So Easy to Get Google to Love You Now Not So Much.” I find it amusing that marketing methods which are ineffectual are still used in Google’s mobile oriented, buy-ad world. Here’s a great example from a small company trying to become a headliner.
Years ago I worked on a US government project. I developed a system which manipulated certain Web search systems’ indexing. It seems to me that one outfit has tried to emulate the DNA of my method. You can see the example of content marketing which is designed to polish a halo for a company involved in indexing. Yep, I know indexing is not exactly what makes the venture capitalists’ heart pound. But indexing has a long tradition of being
- Expensive
- Labor intensive if one wants to deliver precision and recall in search results
- Intellectually demanding, particularly when smart software goes off the rails so often
- Tough to make magnetic.
The write up “Searching with Semantic Technology” summarizes a write up in a “thought leader” publication. There is a parental reminder to remember how important indexing is. There is a concluding statement which explains that natural language processing plays a role in delivering search results. The buzzword “semantic” is repeated.
The only hitch in the git along is that the effort to trigger a Web search system using this abstract, keyword, and allegedly critical comment is that it is old and no longer works very well.
Why? Let me point out:
- Queries come from mobile device users. Some topics don’t lend themselves to mobile methods. It follows that methods based in whole or in part on the methods I developed and explained in my articles over the years are a bit like multiple Xerox copies of an original document. Faded and often useless.
- The jargon problem plagues those with niche capabilities. I pointed out in my cacaphone write up and compilation of buzzwords that most folks don’t have a clue what words mean. A good example is “semantic,” a term which has been devalued and applied to everything from marketing search engines to metasearch engines and more.
- The Web indexing systems have shifted over the years from reliance of a handful of proven indexing methods to wrappers of code which act “smart.” Results lists are essentially unpredictable today. Spoofing with words is a bit like shooting a handgun at the ocean in the hope of killing a fish.
For more information on an old system which doesn’t work very well anymore, navigate to www.augmentext.com. For more examples of marketing material which uses an ineffectual method to add razzle dazzle to a capability which is at best boring and more often of minimal interest, read the blog which serves as the home to this “insight.”
Kenny Toth, January 27, 2017
Textkernel: Narrowing Search to an HR Utility
January 5, 2017
Remember the good old days of search? Autonomy, Convera, Endeca, Fast Search, and others from the go go 2000s identified search as a solution to enterprise information access. Well, those assertions proved to be difficult to substantiate. Marketing is one thing; finding information is another.
How does a vendor of Google style searching with some pre-sell Clearwell Systems-type business process tweaking avoid the problems which other enterprise search vendors have encountered?
The answer is, “Market search as a solution for hiring.” Just as Clearwell Systems and its imitators did in the legal sector, Textkernel, founded in 2001 and sold to CareerBuilder in 2015, , is doing résumé indexing and search focused on finding people to hire. Search becomes “recruitment technology,” which is reasonably clever buzzworking.
The company explains its indexing of CVs (curricula vitae) this way:
CV parsing, also called resume parsing or CV extraction, is the process of converting an unstructured (so-called free-form) CV/resume or social media profile into a structured format that can be integrated into any software system and made searchable. CV parsing eliminates manual data entry, allows candidates to apply via any (mobile) device and enables better search results.
The Textkernel Web site provides more details about the company’s use of tried and true enterprise search functions like metadata generation and report generation (called a “candidate profile”).
In 2015 the company had about 70 employees. Using the Overflight revenue estimation tool, Beyond Search pegs the 2015 revenue in the $5 million range.
The good news is that the company avoided the catastrophic thrashing which other European enterprise search vendors experienced. The link to the video on the Textkernel page is broken, which does not bode well for Web coding expertise. However, you can bite into some text kernels at this link.
Stephen E Arnold, January 5, 2016
Alleged Google Loophole Lets Fake News Flow
January 1, 2017
I read a write up which, like 99 percent of the information available for free via the Internet, is 100 percent accurate.
The write up’s title tells the tale: “Google Does a Better Job with Fake News Than Facebook, but There’s a Big Loophole It Hasn’t Fixed.” What’s the loophole? The write up reports:
…the “newsy” modules that sit at the top of many Google searches (the “In the news” section on desktop, and the “Top stories” section on mobile) don’t pull content straight from Google News. They pull from all sorts of content available across the web, and can include sites not approved by Google News. This is particularly confusing for users on the desktop version of Google’s site, where the “In the news” section lives.Not only does the “In the news” section literally have the word “news” in its name, but the link at the bottom of the module, which says “More news for…,” takes you to the separate Google News page, which is comprised only of articles that Google’s editorial system has approved.
So why isn’t the “In the news” section just the top three Google News results?
The short answer is because Google sees Google Search and Google News as separate products.
The word “news” obviously does not mean news. We reported last week about Google’s effort to define “monopoly” for the European Commission investigating allegations of Google’s being frisky with its search results. News simply needs to be understood in the Google contextual lexicon.
The write up helps me out with this statement:
So why isn’t the “In the news” section just the top three Google News results? The short answer is because Google sees Google Search and Google News as separate products.
Logical? From Google’s point of view absolutely crystal clear.
The write up amplifies the matter:
Google does, however, seem to want to wipe fake news from its platform. “From our perspective, there should just be no situation where fake news gets distributed, so we are all for doing better here,” Google CEO Sundar Pichai said recently. After the issue of fake news entered the spotlight after the election, Google announced it would ban fake-news sites from its ad network, choking off their revenue. But even if Google’s goal is to kick fake-news sites out of its search engine, most Google users probably understand that Google search results don’t have carry the editorial stamp of approval from Google.
Fake news, therefore, is mostly under control. The Google users just have to bone up on how Google works to make information available.
What about mobile?
Google AMP is not news; AMP content labeled as “news” is part of the AMP technical standard which speeds up mobile page display.
Google, like Facebook, may tweak its approach to news.
Beyond Search would like to point out that wild and crazy news releases from big time PR dissemination outfits can propagate a range of information (some mostly accurate and some pretty crazy). The handling of high value sources allows some questionable content to flow. Oh, there are other ways to inject questionable content into the Web indexing systems.
There is not one loophole. There are others. Who wants to nibble into revenue? Not Beyond Search.
Stephen E Arnold, January 1, 2017
Study of Search: Weird Results Plus Bonus Errors
December 30, 2016
I was able to snag a copy of “Indexing and Search: A Peek into What Real Users Think.” The study appeared in October 2016, and it appears to be the work of IT Central Station, which is an outfit described as a source of “unbiased reviews from the tech community.” I thought, “Oh, oh, “real users.” A survey. An IDC type or Gartner type sample which although suspicious to me seems to convey some useful information when the moon is huge. Nope. Nope.Unbiased. Nope.
Note that the report is free. One can argue that free does not translate to accurate, high value, somewhat useful information. I support this argument.
The report, like many of the “real” reports I have reviewed over the decades is relatively harmless. In terms of today’s content payloads, the study fires blanks. Let’s take a look at some of the results, and you can work through the 16 pages to double check my critique.
First, who are the “top” vendors? This list reads quite a bit about the basic flaw in the “peek.” The table below presents the list of “top” vendors along with my comment about each vendor. Companies with open source Lucene/Solr based systems are in dark red. Companies or brands which have retired from the playing field in professional search are in bold gray.
| Vendor | Comment |
| Apache | This is not a search system. It is an open source umbrella for projects of which Lucene and Solr are two projects among many. |
| Attivio | Based on Lucene/Solr open source search software; positioned as a business intelligence vendor |
| Copernic | A desktop search and research system based on proprietary technology from the outfit known as Coveo |
| Coveo | A vendor of proprietary search technology now chasing Big Data and customer support |
| Dassault Systèmes | Owns Exalead which is now downgraded to a utility with Dassault’s PLM software |
| Data Design, now Ryft.com | Pitches search without indexing via propriety “circuit module” method |
| Data Gravity | Search is a utility in a storage centric system |
| DieselPoint | Company has been “quiet” for a number of years |
| Expert System | Publicly traded and revenue challenged vendor of a metadata utility, not a search system |
| Fabasoft | Mindbreeze is a proprietary replacement for SharePoint search |
| Discontinued the Google Search Appliance and exited enterprise search | |
| Hewlett Packard Enterprise | Sold its search technology to Micro Focus; legal dispute in progress over alleged fraud |
| IBM Ominifind | Lucene and proprietary scripts plus acquired technology |
| IBM StoredIQ | Like DB2 search, a proprietary utility |
| ISYS Search Software | Now owned by Lexmark and marginalized due to alleged revenue shortfalls |
| Lookeen | Lucene based desktop and Outlook search |
| Lucidworks | Solr add ons with floundering to be more than enterprise search |
| MAANA | Proprietary search optimized for Big Data |
| Microsoft | Offers multiple search solutions. The most notorious are Bing and Fast Search & Transfer proprietary solutions |
| Oracle | Full text search is a utility for Oracle licenses; owns Artificial Linguistics, Triple Hop, Endeca, RightNow, InQuira, and the marginalized Secure Enterprise Search. Oh, don’t forget command line querying via PL/SQL |
| Polyspot, now CustomerMatrix | Now a customer service vendor |
| Siderean Software | Went out of business in 2008; a semantic search outfit |
| Sinequa | Now a Big Data outfit with hopes of becoming the “next big thing” in whatever sells |
| X1 Search | An eternal start up pitching eDiscovery and desktop search with a wild and crazy interface |
What’s the table tell us about “top” systems? First, the list includes vendors not directly in the search and retrieval business. There is no differentiation among the vendors repackaging and reselling open source Lucene/Solr solutions. The listing is a fruit cake of desktop, database, and unstructured search systems. In short, the word “top” does not do the trick for me. I prefer “a list of eclectic and mostly unknown systems which include a search function.”
The report presents 10 bar charts which tell me absolutely nothing about search and retrieval. The bars appear to be a popularity content based on visits to the author’s Web site. Only two of the search systems listed in the bar chart have “reviews.” Autonomy IDOL garnered three reviews and Lookeen one review. The other eight vendors’ products were not reviewed. Autonomy and Lookeen could not be more different in purpose, design, and features.
The report then tackles the “top five” search systems in terms of clicks on the author’s Web site. Yep, clicks. That’s a heck of a yardstick because what percentage of clicks were humans and what percentage was bot driven? No answer, of course.
The most popular “solutions” illustrate the weirdness of the sample. The number one solution is DataGravity, which is a data management system with various features and utilities. The next four “top” solutions are:
- Oracle Endeca – eCommerce and business intelligence and whatever Oracle can use the ageing system for
- The Google Search Appliance – discontinued with a cloud solution coming down the pike, sort of
- Lucene – open source, the engine behind Elasticsearch, which is quite remarkably not on the list of vendors
- Microsoft Fast Search – included in SharePoint to the delight of the integrators who charge to make the dog heel once in a while.
I find it fascinating that DataGravity (1,273) garnered almost 4X the “votes” as Microsoft Fast Search (404). I think there are more than 200 million plus SharePoint licensees. Many of these outfits have many questions about Fast Search. I would hazard a guess that DataGravity has a tiny fraction of the SharePoint installed base and its brand identity and company name recognition are a fraction of Microsoft’s. Weird data or meaningless.
The bulk of the report are comparison of various search engines. I could not figure out the logic of the comparisons. What, for example, do Lookeen and IBM StoredIQ have in common? Answer: Zero.
The search report strikes me as a bit of silliness. The report may be an anti sales document. But your mileage will differ. If it does, good luck to you.
Stephen E Arnold, December 30, 2016
Smarter Content for Contentier Intelligence
December 28, 2016
I spotted a tweet about making smart content smarter. It seems that if content is smarter, then intelligence becomes contentier. I loved my logic class in 1962.
Here’s the diagram from this tweet. Hey, if the link is wonky, just attend the conference and imbibe the intelligence directly, gentle reader.
The diagram carries the identifier Data Ninja, which echoes Palantir’s use of the word ninja for some of its Hobbits. Data Ninja’s diagram has three parts. I want to focus on the middle part:
What I found interesting is that instead of a single block labeled “content processing,” the content processing function is broken into several parts. These are:
A Data Ninja API
A Data Ninja “knowledgebase,” which I think is an iPhrase-type or TeraText type of method. Not familiar with iPhrase and TeraText, feel free to browse the descriptions at the links.
A third component in the top box is the statement “analyze unstructured text.” This may refer to indexing and such goodies as entity extraction.
The second box performs “text analysis.” Obviously this process is different from “the analyze unstructured text” step; otherwise, why run the same analyses again? The second box performs what may be clustering of content into specific domains. This is important because a “terminal” in transportation may be different from a “terminal” in a cloud hosting facility. Disambiguation is important because the terminal may be part of a diversified transportation company’s computing infrastructure. I assume Data Ninja’s methods handles this parsing of “concepts” without many errors.
Once the selection of a domain area has been performed, the system appears to perform four specific types of operations as the Data Ninja practice their katas. These are the smart components:
- Smart sentiment; that is, is the content object weighted “positive” or “negative”, “happy” or “sad”, or green light or red light, etc.
- Smart data; that is, I am not sure what this means
- Smart content; that is, maybe a misclassification because the end result should be smart content, but the diagram shows smart content as a subcomponent within the collection of procedures/assertions in the middle part of the diagram
- Smart learning; that is, the Data Ninja system is infused with artificial intelligence, smart software, or machine learning (perhaps the three buzzwords are combined in practice, not just in diagram labeling?)
- The end result is an iPhrase-type representation of data. (Note: that this approach infuses TeraText, MarkLogic, and other systems which transform unstructured data to metadata tagged structured information).
The diagram then shows a range of services “plugging” into the box performing the functions referenced in my description of the middle box.
If the system works as depicted, Data Ninjas may have the solution to the federation challenge which many organizations face. Smarter content should deliver contentier intelligence or something along that line.
Stephen E Arnold, November 28, 2016
Creativity for Search Vendors
December 18, 2016
If you scan the marketing collateral from now defunct search giants like Convera, DR LINK, Fulcrum Technologies or similar extinct beasties, you will notice a similarity of features and functions. Let’s face it. Search and retrieval has been stuck in the mud for decades. Some wizards point to the revolution of voice search, emoji based queries, and smart software which knows what you want before you know you need some information.
Typing key words, indexing systems which add concept labels, and shouting at a mobile phone whilst standing between cars on a speeding train returns semi-useful links to what amount to homework: Open link, scan for needed info, close link, and do it again.
Eureka, California is easy to find. Get inspired.
Now there is a solution to search and content processing vendors’ inability to be creative. These methods appear to fuel the fanciful flights of fancy emanating from predictive analytics, Big Data, and semantic search companies.
Navigate to “8 Tried-and-Tested Ways to Unlock Your Creativity.” Now you too can emulate the breakthroughs, insights, and juxtapositions of Leonardo, Einstein, Mozart, and, of course, Facebook’s design team.
Let’s take a look at these 10 ideas.
- Set up a moodboard. I have zero idea what a moodboard is. I am not sure it would fit into the work methods of Beethoven. He seemed a bit volatile and prone to “bad” moods.
- Talk it out. That’s a great idea for companies engaged in classified projects for nation states. Why not have those conversations in a coffee shop or better yet on an airplane with strangers sitting cheek by jowl.
- Brainstorming. My recollectioin of brainstorming is that it can be fun, but without one person who doesn’t get with the program, the “ideas” are often like recycled plastic bottles. Not always, of course. But the donuts can be a motivator.
- Mindmapping. Yep, diagrams. These are helpful, particularly when equations are included for the home economics and failed webmasters who wrangle a job at a search or cotnent processing vendor. What’s that pitchfork looking thing mean?
- Doodling. Works great. The use of paper and pencils is popular. One can use a Microsoft Surface or a giant iPad thing. Profilers and psychologists enjoy doodles. Venture capitalists who invested in a search and content processing company often sketch some what dark images.
- Music. Forget that Mozart and fighter pilot stuff. Go for Gregorian chants, heavy metal, and mindfulness tunes. Here in Harrod’s Creek, we love Muzak featuring the Whites and John Lomax.
- Lucid dreaming. This idea is popular among some of the visionaries working at high profile Sillycon Valley companies. Loon balloons, solar powered Internet aircraft, and trips to Mars. Apply that thinking to search and what do you get? Tay, search by sketch, and smart maps which identify pizza joints.
- Imagine what a great innovator would do. That works. People sitting on a sofa playing a video game can innovate between button pushes.
Why are search and cotnent processing vendors more creative? Now these folks can go in new directions armed with these tips and the same eight or nine algorithms in wide use. Peak search? Not by a country mile.
Stephen E Arnold, December 18, 2016
Oh, Canada: Censorship Means If It Is Not Indexed, Information Does Not Exist
December 8, 2016
I read “Activists Back Google’s Appeal against Canadian Order to Censor Search Results.” The write up appears in a “real” journalistic endeavor, a newspaper in fact. (Note that newspapers are facing an ad revenue Armageddon if the information in “By 2020 More Money Will Be Spent on Online Ads Than on Radio or Newspapers” is accurate.)
The point of the “real” journalistic endeavor’s write up is to point out that censorship could get a bit of a turbo boost. I highlighted this passage:
In an appeal heard on Tuesday [December 6, 2016] in the supreme court of Canada, Google Inc took aim at a 2015 court decision that sought to censor search results beyond Canada’s borders.
If the appeal goes south, a government could instruct the Google and presumably any other indexing outfit to delete pointers to content. If one cannot find online information, that information may cease to be findable. Ergo. The information does not exist for one of the search savvy wizards holding a mobile phone or struggling to locate a US government document.
The “real” journalistic endeavor offers:
A court order to remove worldwide search results could threaten free expression if it catches on globally – where it would then be subject to wildly divergent standards on freedom of speech.
It is apparently okay for a “real” journalistic endeavor to prevent information from appearing in its information flows as long as the newspaper is doing the deciding. But when a third party like a mere government makes the decision, the omission is a very bad thing.
I don’t have a dog in this fight because I live in rural Kentucky, am an actual addled goose (honk!), and find that so many folks are now realizing the implications of indexing digital content. Let’s see. Online Web indexes have been around and free for 20, maybe 30 years.
There is nothing like the howls of an animal caught in a trap. The animal wandered into or was lured into the trap. Let’s howl.
Stephen E Arnold, December 8, 2016
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