Interesting ideas but I have to disagree that social search is now synonymous with realtime search. I get results to many queries (stocks, scores, weather) that are the epitome of realtime without social qualifiers.

When people search in a recovery modality (exemplified above) they want the answer. Period. There is nothing about social connections that makes delivering that answer any more precise. At the moment (and in rare occasions) social connections can deliver that answer faster but I think that is a temporary advantage.

When a searcher is in discovery mode things are different. There is a window where it is possible to develop learning that places authority around people to particular subjects in much the same way Google does with domains. However, I’m not sure that once authority is established that segmenting, filtering and ranking based on proximity to the node of “me” becomes all that valuable. In fact, it is the nature of discovery goals that they are less dependent on trusted sources than those of recovery.

For me Social Search has always been about discovering people. That is what makes it unique from other search modes and where its (large) value resides. It is also where Social Relevancy Rank is likely most valuable. LinkedIn is the clearest & best example of a Social Search Engine that I know of and not coincidentally makes great use of the ideas you have captured here.

Cheers,

Jonathan

You are right, I collapsed real-time into social.

The view of real-time that you hold, to me is really just news. I am not sure how it is different from what blogsearch or google news or any other news search has been? That is, if its coming from newspapers or bloggers its news and it needs to be fresh, but when it comes from people on Twitter or Facebook its social.

When people pass links they effectively vote/filter information much like search engine does. So when you look at this process in aggregate it really does look like what search engine is doing - voting things up.

Alex

Microsoft is trying something like this with their research project called BrowseRank (http://news.cnet.com/8301-1023_3-9999038-93.html), although they weren't the first to come up with the concepts in detail (http://appft1.uspto.gov/netacgi/nph-Parser?Sect1=PTO2&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=0&f=S&l=50&TERM1=Galvin%2C+Brian&FIELD1=IN&co1=AND&TERM2=behavioral&FIELD2=&d=PG01)...

nda

A very interesting and timely post - you raise a lot of key points. I think your Solution 101 will get us a long way and there's certainly a perfect storm brewing that should enable us to deliver this kind of functionality.

However, we need to keep an eye on what comes next once generic use of Friends and Followers, or even Friends of Friends, no longer scales due to ever increasing amounts of information being available. In fact there are cases already where this doesn't work well enough, particularly outside of taste domains.

What I found in my PhD research in this area was that people make sophisticated decisions about who to ask for information, and this varies according to the nature of the task. For example, there's no guarantee that you'll ask the same person to recommend both movies and financial products, however well you know them: these two examples depend to varying degrees on taste overlap and the expertise of the information source. It's this dynamic we'll need to understand in greater detail.

If you'll forgive the element of self-promotion then there's a full discussion of this in my thesis: http://tomheath.com/thesis

Cheers, Tom.

I would expand Johnathan's premise about social search beyond merely discovering people to include discovering people, their passions, their opinions, their expertise, their assistance, etc. Jud Valeski at Gnip posted a rather elegant framework for the interplay of self-expression and discovery that pivots on connectedness in a social environment ( http://bit.ly/connectedness ). Assisting online discovery with criteria from connectedness and the aggregated experience of my social graph not only serves my "logos", but, my "pathos" and "eros" too.

I think an interesting fourth possibility is refining the Influencer Rank concept you outlined so it's topic-specific instead of a measure of generic popularity. I touched on this concept briefly in my PageRank for People article a month ago, and the more I think about it the more it seems like the most likely solution for general-topic searches. The challenge is finding a social search algorithm that can provide meaningful results for all types of queries. If I'm searching a topic that my social graph doesn't already follow, I still want to get returned meaningful results and I suspect some form of universal Influencer Rank score is required to achieve this.

Great post - I really think this cross-breeding of Search and Social Graph is going to give us the next big web/nav innovation.

Cheers,
/-Marshall

If I could see restaurant ratings based solely on "friends & following", it would dramatically I would actually use it again.

On a side note (though it may be obvious), it seems that this system would most benefit the growing community of online creators - bloggers, designers, thought leaders - who are actively engaging with a variety of online platforms.

From tupperware parties and water cooler conversations to the next online revolution. Good stuff.

Thanks,

MC

http://blog.wowd.com/wowd_blog/2009/07/short-wowd-demo-screencast.html

I'm not sure if this is the future, but it is very cool.

Thinking static search such as the one proposed above, we are still only considering linked pages and people, forgetting the impact that linked data will induce on the way we use the web. In fact, the web will not be anything like the web in the future. It will be something different, and thus it becomes very difficult to know whether or not search in the form of question and answer will take place in the web.

Think about it. How did you use the web before the search engines?

I believe the future web is no host for influencers or naturals or social medias or serps. I believe the future web understands us individually and then translates our wishes from how we use the web, putting us in contact with the best possible person or database to talk to in order to solve our situation in a way that suits us the best.

I don't think we will search the web, I think the web will search us, answering our questions even before we ask them.

//Jesper

Although you could score tweets with links higher than those without, you definitely don't want search results to reflect votes on how politically correct information is (RTs often reflect this) and lots of people with huge Twitter followings are charlatans who used steroid software to get that "audience" which is itself largely made up of spammers.

Even for non-charlatans on social media, having a huge following may mean you don't have a day job and don't ultimately know what you are talking about on a variety if not majority of subjects. Furthermore, RT'd tweets are often the shorter ones about Twitterers with short names or they are jokes...making them suspect as to relevance for a serious researcher.

I delete Twitter spam followers constantly, which has halved what my follower count could have been. I don't want get-rich-quick or see-my-cam spammers unfollowing and refollowing all the time so this is why I block (anyone who tries to tell others how to get more followers gets blocked).

Like the influence metering sits, search algorithms should reward people for having a better concentration of real followers who might read any given tweet.

So, sure, if algorithms can identify that such and such a person has 750 followers few of whom score as spammers + this person seems to be constantly commenting with a given keyword combo...then this person should be considered more of an expert on the subject than @APlusk with 2,404,049 followers who just mentioned the keyword for the first time.

What’s missing in these techno-sym models is the human element of trust. We must build in the trust layer into all these processes if it is to have any value. Add the filter of trust to the Social Relevancy Rank (which can have definable algorithmic attributes) and the complex picture becomes clearer.

Trust is the glue that holds societies together – that goes for the virtual world too. Real time or not.

Judy Shapiro
Sr. VP, Paltalk

What a concept! Why don't computer scientist bother studying history?

TV

but der is more on this...

follow this..

http://u.voizle.com/socialrank

hope u found it helpful

thanks for the great post and the important issues you raised here. I totally agree with you that social ranking will build the base for future search. In this context, I would like to share with you and your readers a service that goes in the same direction.

The service is called PLEM and is available at http://eiche.informatik.rwth-aachen.de:3333/PLEM/

The main aim of PLEM is to harness the collective intelligence to rank and recommend learning elements. The idea is quite simple, each filtering action on a learning element from the Web (e.g. comment, link, save, like, rate, vote, view, share) counts as one "vote" for that learning element. The aggregation of the "votes" would then reflect the popularity of that element.

You can find more information about this service on my blog at http://mohamedaminechatti.blogspot.com/2009/07/plem-mashup-driven-long-tail-aggregator.html

Looking forward to receiving your valuable feedback...