Sir Bedevere: What makes you think she’s a witch?
Peasant 3: Well, she turned me into a newt!
Sir Bedevere: A newt?
Peasant 3: [meekly after a long pause] … I got better.
Crowd: [shouts] Burn her anyway!
From the color-me-unsurprised department comes news from Time Magazine’s Techland that 92% of Newt Gingrich’s Twitter Followers Aren’t Real. I’m not making a statement with this post about the politician’s politics, or his character, or even an indictment of social media itself. Mainly because I think far too many people are guilty of the same thing – trying to use inflated social media stats to prove their social worth.
I discussed this with keynote marketing speaker David Dalka this morning, and he shared his thoughts in Twitter Gate â€“ Buy More Twitter Followers Free Instantly â€“ Business Marketing Strategy Implications?, digging into some of the business issues involved surrounding social media and pursuing followers on social networks:
It makes one wonder where all these non-real followers are coming from and more than a few CEOs are likely reading this article and asking the question, â€œIs all this investment in social media justified and an activity that will grow my business and improve the bottom line or are there wiser investments to be made?â€
Continue reading “Don’t Let Social Media Marketers Turn You into a Newt”
In the Google paper, Predicting Bounce Rates in Sponsored Search Advertisements (pdf), we’re told about an experiment at Google where researchers used a document classification model on sponsored advertisements and landing pages to try to predict how many people might see an advertisement in Google’s search results, and after clicking upon the ad leave the landing page very quickly. The experiment in that paper is also described in another Google paper, PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce (pdf), which tells us how Google might be able to take an extremely large amount of observational data and use it to create classifications that, amongst other things, could potentially be used to help rank pages in organic search like we’ve been told that Google’s Panda updates do.
A patent about Google Malware Detection was granted today that appears to use a similar approach to determine whether sponsored advertisements in Google might lead to malware. The patent describes malware as malicious software that might be deceptively or automatically installed on a visitor’s computer when they arrive at a page. In addition to trojan horses and viruses, this can include monitoring software. In some instances, a landing page may be the first in a series of one or more redirections, which can include malware on the page or pages being redirected to. The need for such a classification approach comes about because of the sheer volume of advertisements that Google shows.
We know that Google’s Panda updates look for features on websites that indicate “quality” in some manner. Under the document classification approach in this patent, “intrusion features” are tested and weighted on landing pages.
Continue reading “Google Malware Detection Using Document Classification”