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Radar identifying objects of interestIn a previous post I presented the challenges involved in deriving value from Big Data and in particular unstructured Big Data, which increasingly dominates the Social sphere.  The tools that will enable us to make sense of the plethora of conversations, contributions and observations are still in their infancy and the likelihood that we will have to rely on human scanning and analysis for the short to mid term appears inevitable.

In his book, “The Shallows“, about the effect of the internet on our brains, author Nicholas Carr  discusses the demands on our working memory or, as he puts it, “our mind’s scratch pad”.  Working memory is our CPU and the agent that identifies and transfers information to our long term memory, which is our data store.  It is therefore a pivotal component in our ability to seek out and retain knowledge.

The information flowing into our working memory at any given moment is called our “cognitive load“.  When the load exceeds our mind’s ability to store and process the information… we are unable to retain the information or to draw connections with the information already stored in our long-term memory”

The internet and particularly the social web is constantly bombarding our working memory with stimuli that are intentionally distracting (this post is a representational example with multiple hypertext links in the first two paragraphs).  The pervasiveness of mobile connectivity means that we are always on-line, and never at rest from the interrupting nature of the medium. This suggests we need assistance in processing the social media stream, firstly in being able to recognize important and relevant information, and secondly to earmark that information for further analysis, refinement or augmentation.  The first requirement is for a social media radar, the second for a social information refinery.

Search remains our favorite tool that we use to seek out information.  Google dominates with 4.7 billion searches a day, but Twitter is not far behind (considering its size) at 1.6 billion, and both services are growing fast (Google at 30% increase per year – Twitter at 50% per year). However there are certain limitations in both search functions evidenced by the changes announced by both companies in the past 12 months. Google has recognized the effects of Search Engine Optimization and the fact that we demand results that are more contemporary.  Both companies have added semantic search elements to their  armory, an acknowledgement that searches need to be relevant to time, location, context and searcher’s intent.  However such elements are far from comprehensive. Furthermore because the definitions, rules and algorithms are unpublished the searcher is dependent on Google’s and Twitter’s interpretation of what was intended.  We are still a code generation or two away from being able to parametrize semantic search using our personal or group definitions and meanings.

Turning now to the need for refinement, which is the ability to analyze what we have found, understand its value and relationship to other captured information, and to provide single or collaborative commentary on the discovery itself.  Once again the technology has provided some rudimentary tools, commonly called curation tools.  Related to museum curation, these tools capture and display information of interest.  There are over 40 such applications and each provides a web page in which curators can display their captured content.  In many cases the tools allow for comments to be added as separate components, listed in historic order with the most recent first.  Some curation tools, such as Pearltrees,  support content linking, allowing curators to provide insight into relationships between islands of information.  Many tools provide a browser add-on that will enable the curator to save browsed/searched content to the curated web site.  The tools are improving but there is still a small disconnect between the radar and the refinery functions.

Until now.  SeeSaw is still a fledgling product yet it offers to bridge that gap between scanning and curation, and holds great promise in being able to map  content relevancy  and provide a lens on both active streams and refined content.  What is particularly appealing about SeeSaw is that it is built for visual scanning. as opposed to lexical scanning.  “See”, the radar component of the tool, filters live social media streams and displays the visual content of links and embedded graphics.  For Twitter this is a vast improvement on current viewing dashboards such as Hootsuite and Tweetchat. where speed reading is essential in keeping up with fast moving activities and events such as chatrooms.  SeeSaw not only displays the images but can also stream the video links within individual messages.

This visual facility has three immediate advantages.  Firstly the participant can remain in the chat stream without having to hyperlink to a new page, which has been a major distraction and an extended opportunity for further interruptions and distractions.  Staying in the same window enables continuous contact with the flow of active conversations.   Secondly it enables the viewer to see trends within the stream, connections (ie who is talking to whom) and tangential conversations.  Thirdly, and most importantly, it enables the participant to save active content in the stream to the Saw side of the product, and the bridge to the product’s curation functionality.  A simple toggle button allows the reader to change between the active stream (See Board) and the curated site (Saw Board). In practice this allows the participant to remain in contact and context with the conversation, as opposed to the multi-windowed, heavy interrupt laden environment to which we have been constrained.  Reflection and analysis can now occur after the scan or chat, ensuring that focus and attention can be appropriately applied to both.

SeeSaw is more than welcome to my active toolkit, it allows me to “Embrace, Extend and Expand” (via EMC, via Microsoft) my social media environment.  It is more than a helpmate – it has the potential to be my primary Social Media Assistant.

The See part of Saw – watching the Social Stream for relevant information

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