First Experiment: verify Location as a signal

I am right now sitting in the city of Koblenz and I was typing in a search for weather (Wetter in German) Google returns me the weatherforcast for Koblenz. Afterwards I used a proxy tunnel to my webserver of www.rene-pickhardt.de which is located in the city of Stuttgart. After doing the exact same search on the same computer with the same browser Google returned the weather forcast of Stuttgart.

google search for weather. My location was Koblenz

Google search for weather when using a proxy tunnel to my Location Stuttgart
Since my blog is run on a shared hosting I logged in to my third webserver of www.metalcon.de which is a root server. Google coulden’t personalzie the search in this case because Google doesn’t know where the server is located. But Google asks me to enter the location where I am.

Second experiment: How to bypass the gatekeeper

Googleing the weather is kind of boaring so I did some other queries for bars and nightclubs (also in German = “bars und kneipen”) Again I got different results for Koblenz and Stuttgart. The interesting thing is that for both locations the results have been very general inlcuding also city guides from Berlin / Hamburg / Köln Munich and so on. Just the snippets from google maps have been tailored to my location.

1. Our Search History.
2. Our location – verfied -> more information
3. the browser we use.
4. the browsers version
5. The computer we use
6. The language we use
7. the time we need to type in a query
8. the time we spend on the search result page
9. the time between selecting different results for the same query
10. our operating system
11. our operating systems version
12. the resolution of our computer screen
13. average amount of search requests per day
14. average amount of search requests per topic (to finish search)
15. distribution of search services we use (web / images / videos / real time / news / mobile)
16. average position of search results we click on
17. time of the day
18. current date
19. topics of ads we click on
20. frequency we click advertising
21. topics of adsense advertising we click while surfing other websites
22. frequency we click on adsense advertising on other websites
23. frequency of searches of domains on Google
24. use of google.com or google toolbar
25. our age
26. our sex
27. use of “i feel lucky button”
28. do we use the enter key or mouse to send a search request
29. do we use keyboard shortcuts to navigate through search results
30. do we use advanced search commands  (how often)
31. do we use igoogle (which widgets / topics)
32. where on the screen do we click besides the search results (how often)
33. where do we move the mouse and mark text in the search results
34. amount of typos while searching
35. how often do we use related search queries
36. how often do we use autosuggestion
37. how often do we use spell correction
38. distribution of short / general  queries vs. specific / long tail queries
39. which other google services do we use (gmail / youtube/ maps / picasa /….)
40. how often do we search for ourself

Uff I have to say after 57 minutes of brainstorming I am running out of ideas for the moment. But this might be because it is already one hour after midnight!
If you have some other ideas for signals or think some of my guesses are totally unreasonable, why don’t you tell me in the comments?
Disclaimer: this list of signals is a pure guess based on my knowledge and education on data mining. Not one signal I name might correspond to the 57 signals google is using. In future I might discuss why each of these signals could be interesting. But remember: as long as you have a high diversity in the distribution you are fine with any list of signals.

Google is filtering and personalizing search results

Eli is pointing out a thing some people might have already noticed. If two different people search for the same thing on Google it is very probable that the search results will be very different. Google is doing this without telling the user that it is acutally filtering the results based on what the algorithm thinks the user might like. According to Eli Pariser Google is using 57 signals to determine the interest of us. Among those we find:

• Our Search History.
• Our location
• the browser we use.
• The computer we use
• The language we use
• find my list of almost 57 signals google might use to filter

Of course this kind of personalization has its good sides. When I am about to buy a new notebook computer y I definitely want to see different Websites if I live in Germany or in the US. This could be due to tax and shipping fees. Which means that I am most probably interested in local stores and not in oversea shops. Still this personalization and filtering is a huge potential for serious problems. Let me ask a few questions:

• What happens if Google misinterprets our 57 signals?
• What happens if I only receive results from a certain type?
• What if I rely to the fact that I have access to all kind of information?
• …

We might think we get all the information we need. But in reality we are becoming blinded by the filters Google is using. We have no chance to determine what other information is filtert and potentially available for a certain topic. On the other hand due to the amount of information we need filters and computers to help us. But the systems should be more transparent!

Facebook is also filtering the newsstream from your friends:

I have always been thinking Facebook’s huge success is strongly correlated to the fact that there is hardly Spam on Facebook and the information economy is very smart and user friendly. The attention of users to status updates is very high making facebook a great place for every company to do online and viral marketing. This of course contributes to Facebook’s reach. In fact the information architecture on Facebook is even so smart that your 20’000 followers on Facebook might not receive your status updates since Facebook’s EdgeRank algorithm decides it is not relevant to your fans or friends. Edgerank might not have 57 signals but it still takes into consideration:

• who your fans are friend with
• what other news they like
• how heavy they have interacted with you in the past
• the time passed since your last status update
• …

Great news isn’t it? Just compare this with my statement in a recent blog post about creating newsletters as a musician in order to communicate with your fans and not solely rely on other services like Facebook or MySpace.
You don’t believe the Facebook thing? There is a video about the EdgeRank algorithm used by Facebook to determine which status updates should reach us and which shouldn’t. Feel free to have a look and thanks to the guys from Klurig Analytics for producing such a great video resource:

So what can we do?

1. We should join the discussion in order to pursue Google, Facebook and others to become more transparent.
2. We should be aware of the fact that a lot of information might not reach us.
3. Even though more and more information is made available through the Internet we should not become lazy and rely on all these great web services.
4. Last but not least you can help to spread the information about this topic! As we have seen only if a lot of people spread the information it breaks through the filtering system. And this topic is worth to be spread!

Again thanks a lot to Eli Pariser to start this discussion!