Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

June 3, 2016

Newspaper Publishers Protecting Consumers (What?)

Filed under: Ad Targeting,Publishing — Patrick Durusau @ 4:28 pm

Newspaper industry asks FTC to investigate “deceptive” adblockers by John Zorabedian.

From the post:

Fearing that online publishers may be on the losing side of their battle with commercial adblockers, the newspaper publishing industry is now seeking relief from the US government.

The Newspaper Association of America (NAA), an industry group representing 2000 newspapers, filed a complaint with the US Federal Trade Commission (FTC) asking the consumer watchdog to investigate adblocker companies’ “deceptive” and “unlawful” practices.

The NAA is not alleging that adblockers themselves are illegal – rather, it says that adblocker companies make misleading claims about their products, a violation of the Federal Trade Commission Act.
…

Do you feel safer knowing the Newspaper Association of America (NAA) is protecting you from deceptive ads by adblocker companies?

A better service would be to protect consumers from deceptive ads in their publications but I suppose that would be a conflict of interest.

The best result would be for the FTC to declare you can display (or not) content received on your computer any way you like.

You cannot, of course, re-transmit that content, but if a user chooses to combine your content with that of another site, that is entirely on their watch.

Ad-blocking, transformation of lawfully delivered content, including merging of content, are rights that every user should enjoy.

January 7, 2013

…Self-Destructing Ads for Lingerie

Filed under: Ad Targeting,Advertising,Topic Maps — Patrick Durusau @ 6:41 am

Grey Uses the New Facebook Poke to Create Self-Destructing Ads for Lingerie Onetime clip for onetime sale by Rebecca Cullers.

From the post:

Facebook has redesigned its Poke feature to allow people to send their friends video clips that self-destruct 10 seconds after opening. “Hey, that would be great for safe sexting!” you probably thought immediately. So, it shouldn’t come as a shock that the first advertiser to use the new Facebook Poke is a lingerie company. Delta Lingerie crafted a campaign with Grey Tel Aviv in which a 10-second clip of a model pulling on some Delta stockings—a video that couldn’t be saved or even shared—was sent to the model’s friends. A few seconds at the end directed them to Delta’s website to claim a “one-time” discount on the stockings. Since Facebook allows you to poke only 40 people at a time—and the app deletes the video on the sender’s end, too—the model’s agent had to shoot the same clip over and over again.

Certainly an interesting idea, self-destructing messages, particularly for college football coaches and others with lots of texting time on their hands.

Rather specialized though.

And for whatever reason people keep those sorts of messages.

Rather than encryption, which always attracts attention, what about transforming messages into “box scores” for some sport?

Something that might be overlooked when looking for “sexting” messages on a coaches phone?

Particularly if the transformation was a hidden part of message management, discoverable only on examination of the source code.

1,002 uses of topic maps?

What do you think?

October 10, 2012

Fighting Spam at Twitter [Spam means non-licensed by service provider?]

Filed under: Ad Targeting,Spam,Tweets — Patrick Durusau @ 4:18 pm

Fighting Spam at Twitter by Marti Hearst.

From the post:

On Thursday, Delip Rao electrified the class with a lecture on how Twitter combats the pervasive threat of tweet spam:

The video failed but lecture notes are available.

Spam defined:

An unintended use of a service by an adversary to potentially cause harm or degrade user experience while maximizing benefit for the adversary.

On the slides, “Rate-limit avoidance” appears under “unintended use.”

Licensing by service provider means material that “degrade[s] user experience while maximizing benefit for the adversary” isn’t spam?

My experience with licensed spam on television (including cable) and online is that it all degrades my experience in hope of maximizing their gain.

We need a pull model for advertising instead of a push one.

Banning all push spam would be a step in the right direction.

March 14, 2012

No Honor Among Thieves

Filed under: Ad Targeting,Data Mining — Patrick Durusau @ 7:36 pm

Well, the original title is: 50% of the online ads are never seen by Panos Ipeirotis.

About my title: The purpose of ads is to sell you something. Whatever the consequences may be for you. A lesson well taught by US Tobacco, Big Pharma and the corn lobby (think of all the unnatural fructose products in your food).

That said, the post by Panos is a remarkable piece about investigation and data analysis.

From the post:

Almost a year back, I was involved in an advertising fraud case, as part of my involvement with AdSafe Media. (See the related Wall Street Journal story.) Long story short, it was a sophisticated scheme for generating user traffic to websites that were displaying ads to real users but these users could never see these ads, as they were never visible to the user. While we were able to uncover the scheme, what triggered our investigation was almost an accident: our adult-content classifier seemed to detect porn in websites that had absolutely nothing suspicious. While it was a great investigative success, we could not overlook the fact that this was not a systematic method for discovering such attempts for fraud. As part of this effort to make more systematic, the following idea came up:

Let’s monitor the duration for which a user can actually see an ad?

After a few months of development to get this feature to work, it became possible to measure the exact amount of time an was visible to a user. While this feature could easily now detect any fraud attempt that delivers ads to users that never see them, this was now almost secondary. It was the first time that we could monitor the amount of time that users get exposed to ads.

50% of the Ads are (almost) Never Seen.

By measuring the statistics of more than 1.5 billion ad impressions per day, it was possible to understand deeply how different websites perform. Some of the high level results:

  • 38% of the ads are never in view to a user
  • 50% of the ads are in view for less than 0.5 seconds
  • 56% of the ads are in view for less than 5 seconds

Personally, I found these numbers impressive. 50% of the delivered ads are never seen for more than 0.5 seconds! I wanted to check myself whether 0.5 seconds is sufficient to understand the ad. Apparently, the guys at AdSafe thought about that as well, so here is their experiment:

A “pull” advertising model avoids this type of fraud because advertisers could deliver directly to pre-qualified consumers. Better use of funds for psycho-sexual manipulation of pre-qualified consumers, rather than scatter-shot across demographics.

If you are tired of wasting money on “push” advertising (with the hazards and dangers of fraud), consider a different model. Consider topic maps.

March 10, 2012

Ad targeting at Yahoo

Filed under: Ad Targeting,Marketing,User Targeting — Patrick Durusau @ 8:20 pm

Ad targeting at Yahoo by Greg Linden.

From the post:

A remarkably detailed paper, “Web-Scale User Modeling for Targeting” (PDF), will be presented at WWW 2012 that gives many insights into how Yahoo does personalized advertising.

In summary, the researchers describe a system used in production at Yahoo that does daily builds of large user profiles. Each profile contains tens of thousands of features that summarize the interests of each user from the web pages they have viewed, searches they made, and ads they have viewed, clicked on, and converted (bought something) on. They explain how important it is to use conversions, not just ad clicks, to train the system. They measure the importance of using recent history (what you did in the last couple days), of using fine-grained data (detailed categories and even some specific pages and queries), of using large profiles, and of including data about ad views (which is a huge and low quality data source since there are multiple ad views per page view), and find all those significantly help performance.

You need to read the paper and Greg’s analysis (+ additional references) if you are interested in user profiles/marketing.

Even if you are not, I think the paper offers a window into one view of user behavior. Whether that view works for you, your ad clients or topic map applications, is another question.

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