Pieces of Time, Place, Things, and Personal Connections Loosly Joined

by Thomas Vander Wal in , , , , , , , , ,

There are a lot of people wondering what to do with all the data that is being generated by social tools/sites around the web and the social tools/services inside organization. Well, the answer is to watch the flows, but the pay off value is not in the flow it is in contextualizing the data into usable information. Sadly, few systems have had the metadata available to provide context for location, conversation flow, relevant objects (nouns), or the ability to deal with the granular social network.

How many times have you walked bast a book store and thought, “Hmm, what was that book I was told I should check out?” Or, “my favorite restaurant is book filled, what was the name of the one recommended near here a month or so ago?” When the conversations are digitized in services like Twitter, in Facebook, or the hundreds of other shared services it should be able to come back to you easily. Add in Skype, or IM, which are often captured by the tools and could be pulled into a global context around you, your social connections, the contexts of interest the for the relationships, and the context around the object/subject discussed you should have capability to search to get to this within relatively easy reach.

Latency from Heavy Computational Requirements

What? I am hearing screaming from the engineers about the computational power needed to do this as well as the latency in this system. Design Engaged 2005 I brought up a similar scenario, within context of my Personal InfoCloud and Local InfoCloud frameworks called Clouds, Space & Black Boxes (a 500kb PDF). The key then as it is still is using location and people to build potential context and preprocess likely queries.

When my phone is sharing my location with the social contextual memory parser service that see I am quite near a book store (queue the parsing for shared books, favorited conversations with books, recent wish list additions (as well as older additions), etc. But, it is also at the time I usually eat or pick up food for a meal, so restaurant and food conversations parsed, food blogs favorited (delicious, rated on the blogs, copied into Evernote, or stored in Together or DevonThink on my desktop, etc.) to bring new options or remind of forgotten favorites.

Now, if we pull this contextual relevance into play with augmented reality applications we get something that starts bringing Amazon type recommendations and suggestions to play into our life as well as surfacing information “we knew” at some point to our finger tips when we want it and need it.

Inside the Firewall

I have been helping many companies think through this inside the firewall to have, “have what we collectively know brought before us to help us work smarter and more efficiently”, as one client said recently. The biggest problem is poor metadata and lack of even semi-structured data from RDFa or microformats. One of the most important metadata pieces is identity, who said what, who shared it, who annotated it, who commented on it, who pointed to it, and what is that person’s relationship to me. Most organizations have not thought to ensure that tiny slice of information is available or captured in their tools or service. Once this tiny bit of information is captured and contextualized the results are dramatic. Services like Connectbeam did this years ago with tags in their social bookmarking tool, but kept it when they extended the ability to add tagging in any service and add context.

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Explaining the Granular Social Network

by Thomas Vander Wal in , , , , , , , , ,

This post on Granular Social Networks has been years in the making and is a follow-up to one I previously made in January 2005 on Granular Social Networks as a concept I had been presenting and talking about for quite some time at that point. In the past few years it has floated in and out of my presentations, but is quite often mentioned when the problems of much of the current social networking ideology comes up. Most of the social networking tools and services assume we are broadline friends with people we connect to, even when we are just "contacts" or other less than "friend" labels. The interest we have in others (and others in us) is rarely 100 percent and even rarer is that this 100 percent interest and appreciation is equal in both directions (I have yet to run across this in any pairing of people, but I am open to the option that it exists somewhere).

Social Tools Need to Embrace Granularity

What we have is partial likes in others and their interests and offerings. Our social tools have yet to grasp this and the few that do have only taken small steps to get there (I am rather impressed with Jaiku and their granular listening capability for their feed aggregation, which should be the starting point for all feed aggregators). Part of grasping the problem is a lack of quickly understanding the complexity, which leads to deconstructing and getting to two variables: 1) people (their identities online and their personas on various services) and 2) interests. These two elements and their combinations can (hopefully) be seen in the quick annotated video of one of my slides I have been using in presentations and workshops lately.

Showing Granular Social Network

                Granular Social Network from Thomas Vander Wal on Vimeo.

The Granular Social Network begins with one person, lets take the self, and the various interest we have. In the example I am using just five elements of interest (work, music, movies, food, and biking). These are interest we have and share information about that we create or find. This sharing may be on one service or across many services and digital environments. The interests are taken as a whole as they make up our interests (most of us have more interests than five and we have various degrees of interest, but I am leaving that out for the sake of simplicity).

Connections with Others

Our digital social lives contain our interests, but as it is social it contains other people who are our contacts (friends is presumptive and gets in the way of understanding). These contacts have and share some interests in common with us. But, rarely do the share all of the same interest, let alone share the same perspective on these interests.

Mapping Interests with Contacts

But, we see when we map the interests across just six contacts that this lack of fully compatible interests makes things a wee bit more complicated than just a simple broadline friend. Even Facebook and their touted social graph does not come close to grasping this granularity as it is still a clumsy tool for sharing, finding, claiming, and capturing this granularity. If we think about trying a new service that we enjoy around music we can not easily group and capture then try to identify the people we are connected to on that new service from a service like Facebook, but using another service focussed on that interest area it could be a little easier.

When we start mapping our own interest back to the interest that other have quickly see that it is even more complicated. We may not have the same reciprocal interest in the same thing or same perception or context as the people we connect to. I illustrate with the first contact in yellow that we have interest in what they share about work or their interest in work, even though they are not stating or sharing that information publicly or even in selective social means. We may e-mail, chat in IM or talk face to face about work and would like to work with them in some manner. We want to follow what they share and share with them in a closer manner and that is what this visual relationship intends to mean. As we move across the connections we see that the reciprocal relationships are not always consistent. We do not always want to listen to all those who are sharing things, with use or the social collective in a service or even across services.

Focus On One Interest

Taking the complexity and noise out of the visualization the focus is placed on just music. We can easily see that there are four of our six contacts that have interest in music and are sharing their interest out. But, for various reasons we only have interests in what two of the four contacts share out. This relationship is not capturing what interest our contacts have in what we are sharing, it only captures what they share out.

Moving Social Connections Forward

Grasping this as a relatively simple representation of Granular Social Networks allows for us to begin to think about the social tools we are building. They need to start accounting for our granular interests. The Facebook groups as well as listserves and other group lists need to grasp the nature of individuals interests and provide the means to explicitly or implicitly start to understand and use these as filter options over time. When we are discussing portable social networks this understanding has be understood and the move toward embracing this understanding taken forward and enabled in the tools we build. The portable social network as well as social graph begin to have a really good value when the who is tied with what and why of interest. We are not there yet and I have rarely seen or heard these elements mentioned in the discussions.

One area of social tools where I see this value beginning to surface in through tagging for individuals to start to state (personally I see this as a private or closed declaration that only the person tagging see with the option of sharing with the person being tagged, or at least have this capability) the reasons for interest. But, when I look at tools like Last.fm I am not seeing this really taking off and I hear people talking about not fully understanding tagging as as it sometimes narrows the interest too narrowly. It is all an area for exploration and growth in understanding, but digital social tools, for them to have more value for following and filtering the flows in more manageable ways need to more in grasping this more granular understanding of social interaction between people in a digital space.

Life Data Stream

by Thomas Vander Wal in , , , , ,

Emily Chang posted about "My Data Stream", which brought to mind the idea of personal planets. Emily is pulling together the streaming data from her digital life that passes through feeds.  Jeremy Keith has written about his life streams and has had a nice interface to Jeremy's Life Stream for some time now.

It was a chat with Jeremy and some others this past summer at the Microlearning conference that I started thinking about playing around with a personal planet, which would use PlanetPlanet, a Python script, to pull all of my life streams together.  It works nicely on my laptop, or did until the December crash.  But, now it could be time to put it out in public.

Personal Planets

Why a personal planet? We have an incredible amount of information that passes before out eyes and that is generated by our simple actions.  Emily did a great job showing the breadth of feeds generated.  This seems a simple thin thread.  What if we could quickly scan that thread and annotate it to make it easier to refind.

Planets are relatively easy to build and it should be easy to share for others to build upon. Personally, I am really surprised there are not thousands of these out there already.  Now to start tinkering a wee bit this week.

Local InfoCloud as a Responce

by Thomas Vander Wal in , , , , , ,

Ed Vielmetti posted about neighborhoods, networks, communities, online+offline and I had the following comment. My comment seems to fit in as a follow-up post to the Local InfoCloud post (linked below). The online and offline is very important, but so is the individual and the individual interests we have.

There is a huge need for tools that can connect in the neighborhoods. The neighborhood listserve is not the solution, even if some have been successful. The UK's Up My Street was an interesting take on this. There should be potential in something like Yahoo Local, but the people connecting to people is not there.

I have been doing a fair amount of thinking around this as part of the Local InfoCloud (more than just location, but location is very important) as in the Exposing the Local InfoCloud. Each of the components of the Local InfoCloud can be mixed with others and should be mixed.

This summer I have been to more neighborhood cookouts than any time in the past. But the commonality is our kids are around the same age and they interact at the local preschool just up the block. It is the similar/common interests that bring us together. It is the "location", "near in thought" (kids interests), and "affiliation" (school) components that are the aggregation/attraction points.

Part of the problem with every social networking site is they are broad-line friend based and not focussed on facets of our lives. The social network waters are muddied by the broadlines an make it difficult to identify common bonds with people whom we may not yet know, or know from other life contexts. The digital life tools need to start bubbling up the individuals and focuss less on the popularity engines based on people with dissimilar interests.

Mash-ups and the Model of Attraction

by Thomas Vander Wal in , , , , , , , , ,

I have been thinking a lot about web2.0 mash-ups like Housing Maps since I was on a panel with Paul Rademacher. Particularly I have been trying to make sense of mash-ups in the context of the Model of Attraction. It was not difficult to use these models as a lens to better understand what is going on in these mash-ups. The irony is I needed to do a tiny mash-up of my own to better understand what is going on.

Let us use the Housing Maps as our sole example. Housing Maps takes the housing listing information from Craigslist and displays them by location as a layer in the Google Maps. Paul had built the tool in his spare time as the result of showing up at the same location to rent twice. The visual representation of the listings on a map helped him keep from doing this again. The visual representation also helps others better discern proximity and location (next to a freeway is why it is cheap, or near playground for junior, etc.).

The interpretation of this mash-up and other web2.0 developments require using a slight mash-up of the Model of Attractions's receptors (the receptors are intellectual (cognitive), perceptual (sensory), mechanical, and physical). One uses the receptors as a whole to design and develop information/media access for people in different contexts, with different devices, varying needs, and in different contextual needs. In the case of understanding the Housing Maps we know what the mechanical receptor is, it is a desktop/laptop computer as that is what the interface requires to use the tool. Housing Maps implicitly requires full visual capabilities, and the means to control a pointing device (mouse, etc) for the physical receptors.

The two receptors we will look at are the Intellectual Receptor and the Perceptual Receptors. The Intellectual Receptor is used in the design and development phases to understand how a person thinks about the information/media by understanding vocabulary, information structures, complexity of conveyance (what level and style of writing are used to convey the ideas), level of detail used, the amount of explanation given, use of metaphors, etc. The Perceptual Receptors are used to understand what sensory elements are understood by the people using the information/media. The sensory elements are comprised of visual, auditory, motion/animation, touch (haptic), etc.

The Housing Maps requires understanding the limitations of the resources being used prior to Paul's remixing. The information that Paul was using was Craigslist to find a new place to live. Craigslist is a rich information source that has a large variety of things for sale or giving as well as social connective communities (personals pages). Paul was using the housing section in the San Francisco Bay Area as his information source. The housing entries have descriptions of the properties for rent/let/buy, much like the old classified real estate ads in the newspapers (remember those) but with a little more detail and often including photos of the property. One element that many of the properties include is a location variable (address).

While the Craigslist information is rich and robust and a fantastic resource, Craigslist has a simple interface. This interface, much like that of a classified ad is about providing the information and using the space efficiently. The reality is no mater what is done to the visual appearance of Craigslist the information in text form and the photos are just those simple elements. A map included in each of the entries would be a little more helpful, but it is still rather limiting as it does not give an idea of what is really on the market and where all of the properties of interest are located (in the given parameters of the person's query). We have the Intellectual Receptors largely sated. The Perceptual Receptors (what does the page look like how does a person interact with the information (passively/actively)) could use a little more tweaking, but within the context of the static HTML page the information interface offers little opportunity for improvement.

The missing element in the Craigslist information is not data that is missing (except where locative data is not included in the Craigslist entry). The missing element is in the Perceptual Receptor which then augments the Intellectual Receptor. The contextual framework for locative information is missing from the interface. The array of information provided in the Craigslist interface needs another vector to view the information (Craigslist limits by price, rough geographical area, type of property arrangement (rent/lease/sublet/buy/share/own), animals, and keywords). This vector is a more fine grained view of the location information and put into a context that helps make sense of the information easily. The context is a map, which works well for displaying location-based information.

The Google map is used for the visual representation layer, which provides the context to the location information. The Google map is an open interface that is available to use for the display of location relevant information from external data sources. The interface if very helpful for this type of information and it is freely available for those with the skill sets needed to parse and feed the information into the Google maps interface.

The web2.0 mash-ups extract information from one source and display that information in a different interface. Tools like Bloglines do this with feeds and display the information in an interface separate from the website's interface from which the information was posted by the content creator/owner.

These mash-ups serve to provide the person consuming the information a tool that works for their needs. In a "come to me web" this is very important. The content provider/owner would have to invest many resources to provide a broad array of interfaces to search each person and each person's needs and desires for information. Additionally, as it is with nearly everything on the web the interface that aggregates information from a broad variety of information sources provides a richer set of information for the person to use and analyze for their own needs. Not only are the Intellectual Receptors augmented by the network effect of the information, but offering the personal consuming the information a means/lens (for their Perceptual Receptor needs) to view the information/media in means that adds value for their need is required for people to better embrace the web as a source of information that is a layer woven into their life rather than technology tools that augment their lives.