These words collected that create "textual disambiguation using social connections" come from social networks where the person might be a member, describes SEO by the Sea founder Bill Slawski. Describing the patent application to demonstrate the power of social graphs, he highlights the mounds of information search engines capture about someone's use of words on the Web, as well as their social connections.
The patent filing details how Google might score words used by people in a social network to decide on which ones to add to a dictionary. This score aims at predicting future words someone might use. Slawski tells us the method for deciding on words to present as suggestions could also look at information found from someone's computer, taken from word processing documents, calendar items, contacts, history from a browser, and more.
Although not a new concept, the technology has matured and ready to deploy across the Web as an alternative to behavioral targeting. Ex-Googler and Media6Degrees chief executive officer Tom Phillips calls it social targeting. And he's out to prove it works. The company has developed technology-social CRM-that will dynamically serve up display ads based on your social graphs. The non-personally identifiable (PII) data collected in browsers link to others and make a decision on the content to display within milliseconds. When I asked Phillips when we can expect to see the technology, he told me his company's board has been asking the same thing.
The technology that supports a link between search and social graph connections has gained enough maturity to work. HuoMah SEO Blog founder David Harry points to a Microsoft patent filed in September 2006.A recent update for a patent application from Google describes how the search engine might add words to a dictionary on a PC or a mobile device from a search query through textually disambiguation using social connections. The theory is that one person might likely use terms their friends often use. From this information, and more, collected across the Web, search engines, in theory, could build a social graph that maps likes and dislikes of a specific person or persons, as well as those whom they friend.
Slawski describes it this way: If someone frequently visits the baseball pages at ESPN and those files are in their browser's cache, when they start spelling b-a-s, the computer they are using might offer "baseball" as a query suggestion.
This Google patent seems to focus more on the auto-completion of words on a mobile phone than it does on providing query suggestions to a searcher. Slawski says the information located on a network that someone uses, such as their email account, might also become a source of data that could help someone fill out a text box on their phone, or in suggesting a query term. But could it also help advertisers target consumers and their friends in a social graph.
This is a little easier to understand:
Case study: Consumer Tracking on Levis.com
April 30, 2010
This is the summary of an article by Catherine Dwyer, in which she describes how users are tracked on Levis.com
You can get the PDF of the behavioral targeting article here:
Behavioral Targeting: A Case Study of Consumer Tracking on Levis.com
Behavioral targeting allows advertising networks to collect information about the online activities of a consumer. These networks gather data by observing millions of consumers, and knowing the sites visited, length of stay, pages viewed and which website is entered next by any individual.
Basically, no name, address, phone number or email is stored, so each individual web browser is anonymous, and tagged with identifier aggregates, most common of which are cookies. These technologies have become more sophisticated over the years. With behavioral targeting, online ads are customized in terms of specified characteristics. The advertising networks that do these have the technologies, clickstream data, data warehousing structures, among others, to do so. Examples of these companies are Akami Technologies, DoubleClick and Tacoda.
A browsing consumer is tagged and the behavior of that individual online is tracked. The data is then divided into two kinds: PII and non-PII. PII stands for personally identifiable information. So PII is your name, ss number. Non-PII is everything else about you, and these are collected without your consent. There are 3 kinds of methods to collect these data, namely, browser cookies, web beacons and flash cookies. Most of the time all three are implemented to get a better picture of a consumer’s browsing behaviors.
Control of information
In e-commerce, trust is important, and it can be gained with respect for privacy. Privacy is hard to describe though. Most e-commerce use Westin’s definition about privacy: a control of information. So they justify that control is not needed because of the anonymous nature of tracking customer tags.
However, anonymity is not privacy. Privacy is the preservation of free choice, independence and autonomy. Nevertheless, it’s very hard to reinforce privacy in the online environment now; software that provides information control is hard to achieve.
Advertising networks mutely collect information through behavioral targeting and this greatly influences a customer’s purchase decisions. This means no autonomy. The technique resembles that used by viruses and hackers.
Future research related to this study will tackle profiling an entire industry. This one tackled only one website. Furthermore, it will study the awareness of consumers about the risks involved. Nevertheless, there are quite a few options to protect privacy, the best but most impractical way would be to withdraw from the online world altogether. What’s best for now, is to delete cookies and temporary internet files every time after browsing.
Behavioral targeting should not have such a one sided capacity to influence consumers. They should give consent to consumers and increase clearness of these targeted ads.