Practical advice for managers on how the Web and social media can help them to do their jobs better

[source: Amazon]

I first heard Euan Semple speak about Social Media at a BCS (British Computer Society) ELITE event at BT Tower (in London) back in 2008. What differentiated him from others writing and speaking about the subject?

• Experience: he has a very credible background in collaboration and communications, formerly at the BBC and latterly as an ‘independent consultant’ with blue chips and niche players.
• Hype realism: a recognition of the need to drive real value from social media, delivering business outcomes, not ‘digital noise’.
• Adoption complexity: it takes ‘10 seconds’ to sign up on Twitter, and less again to start using it in an ineffective and potentially damaging way. Forces such as consumerisation and social web have created mind shifts in business. Euan sets out simple, effective, engaging and sensible advice which will inform CxOs, marketers and communications professionals alike.

If you have an interest in the social web and optimisation of communications using social media, this book is a must buy.

Further Info

[source: Amazon]

Today′s managers are faced with an increasing use of the Web and social platforms by their staff, their customers, and their competitors, but most aren′t sure quite what to do about it or how it all relates to them. Organizations Don′t Tweet, People Do provides managers in all sorts of organizations, from governments to multinationals, with practical advice, insight and inspiration on how the Web and social tools can help them to do their jobs better. From strategy to corporate communication, team building to customer relations, this uniquely people–centric guide to social media in the workplace offers managers, at all levels, valuable insights into the networked world as it applies to their challenges as managers, and it outlines practical things they can do to make social media integral to the tone and tenor of their departments or organizational cultures.

• • A long–overdue guide to social media that talks directly to people in the real world in which they work
  • Grounded in the author′s unparalleled experience consulting on social media, it features eye–opening accounts from some of the world′s most successful and powerful organizations
  • Gives managers at all levels and in every type of organization the context and the confidence to make better decisions about the social web and its impact on them

Euan Semple is one of the few people in the world who can turn the complex world of the social web into something we can all understand. And, at the same time, learn how to get the most from it.

Ten years ago, while working in a senior position at the BBC, Euan was one of the first to introduce what have since become known as social media tools into a large, successful organisation. He has subsequently had five years of unparalleled experience working with organisations such as Nokia, The World Bank and NATO.

He is a one-man digital upgrade option for us all to download.

This world is changing fast, but he makes sense of it because he understands that the core basics remain the same: community, learning, and interaction. He is a master story-teller who offers a host of practical tales about how this new world can work for real people in the real world.

Figure 1 – The Johari Window devised by Joseph Luft and Harry Ingham

The Johari Window is a model for describing personal awareness types and human interaction.

Quadrant A: encapsulates personal awareness and a wish to share information with others, for the purposes of simplicity assume this means publicly.

Quadrant B: encapsulates personal awareness of a different type. The motivations for concealment are plentiful (bad habits, competitive advantage, Machiavellianism, protection of personal interests etc.). The size of this box tends to diminish as trust relationships expand, however I contend: a) there are many types and levels of concealment implied here and b) many different levels of trust in different social circles.

Quadrant C: encapsulates weak personal awareness and misinterpretation (we assume others see us as we see ourselves, but this is not the case). This quadrant (in the context of Social Networking) provides an interesting opportunity for introspection and awareness development from social feedback, Social Network Analysis and sentiment analysis. This is a box full of brambles!

Quadrant D: Donald Rumsfeld’s infamous Known Knowns speech of 2002 sums up this quadrant.

A Prophetic View

Just under two years ago I wrote a somewhat prophetic article concerning Privacy and Social Networks in which I argued for the need for additional privacy controls and multiple walled gardens within social networks. Facebook lists were a crude approximation, but Goolge+ Circles now excel at delivering the concept. A sister post in February 2010 discussed Social Search and the Integrity of the Social Graph, concluding that Google was heading (with purpose) into the Social Networking space.

What I said back in January 2010:

Visualisation of Social Network privacy controls is poor. The granularity of access controls is too coarse. My solution would be creation of (either my privacy “Onion model”) or perhaps more simply a ‘radar’ or quadrant model on which connections could be placed within ‘trust zones’ (by dragging and dropping them onto the appropriate region). Configuration is half the battle, and visualisation of the resultant privacy controls effect is essential. This is where current controls are weakest. I also want multiple walled gardens to play with (where I could segregate user groups) and ensure no (uncontrolled) information leakage between…

A trust and privacy ‘radar’ would be equally interesting, with those closest to the centre having the greater trust relationship and access to more personal data.

The Johari Window and Google+ Circles

Figure 2 – The Google+ Circle Model

I have a number of Circles within Google+: Friends, Family, Acquaintances, Scientific Community, Social Media, Politics, Techies etc. There is also a ‘Public category’ which maps neatly onto Quadrant A of the Johari Window.

Quadrant B maps neatly to the different circles (Friends, Family etc.). This creates controlled separation, where I can isolate various topic discussions. This helps prevent Family members from being bored by discussions about Social Network Analysis or Social Psychology! Equally it saves Scientific Community colleagues reading my latest views on the European Union. There is a great deal more depth to this than simple ‘separation of interests.’ Despite what we may think, as multi-dimensional beings, we do not necessarily want everyone in cyberspace or our social sphere having a complete 360 degree view of our personality, interests or social connections.

Quadrant C could make for a ‘fun’ social network game – tell me something about myself that I don’t know, but you do know. Play at your own risk!

Quadrant D is ripe for Reality Mining as long as there is a digital footprint.

The Johari Window provides an interesting thinking framework on which to base an approach to online privacy protection and information sharing across social groups.

Extending the Johari Window for Privacy and Reputation Protection

I propose an extension to the Johari Window (as depicted in Figure 3). As information flows into a Circle we lose control of it. We must assume that we have chosen Circle members well and that each member will understand (and abide) by our privacy wishes in respect of that information. The obvious drawback however is that there is no adequate meta-data associated with the shared information to indicate to Circle members what is ‘allowable’. Perhaps Google will introduce ‘Circle Contracts’ to stipulate between parties what is acceptable!

Adding an A+ box (Figure 3) recognises that there will be information which I am happy to be disclosed by people acting as relays between Circles with no restrictions.

Box B+ recognises information disclosed to certain Circles must stay within that Circle or may be selectively disclosed to other Circles (not under my ownership) which meet certain membership/privacy criteria. There is currently however no way to express this (or manage disclosure across ‘logically chained Walled Gardens’).

Box C+ recognises that there is information about myself of which I am unaware, and would be happy about being disclosed. If it is information which may be publicly disclosed, it fits within box A. If it requires restriction per ‘Walled Garden’ or Circle, it fits within box B.

Box C++ recognises that there is information about myself of which am I unaware, and would be unhappy about being disclosed. This box is ripe for Reputation Protection.

Boxes C+ and C++ are interesting as I would be theoretically unaware of my privacy requirements until the information is disclosed (of course heuristics could be employed).

Boxes B, B+, C, C+ and C++ all have potential for information leakage. As Circles and Networks are highly interconnected, chances are the information could reach parties which you would rather not see it.

Extending the Johari Window and applying this thinking technique to online privacy within Social Networks is useful in terms of surfacing complexity and also challenging personal views of requirements for information management.

Figure 3 – Extending the Johari Window

[source: Steve Nimmons]

The “Elemental Web” was a connection of machines, then a connection of sites, now it is a complex amalgam awash with traditional links and millions of ‘inter-personal’ connections defined by the Social Graph. But what exactly is the Social Graph, is it open to manipulation and how might this affect experimentation in Social Search? How shall we seek to vanquish the Social Chimera?

First let me define the Social Graph and the fundamentals of Social Search. The former is the connection of people and the defining relationships. It is built on new algorithms such as the Social Graph API as well as “older” technologies such as XFN and FOAF. It puts the ‘human face on linking’. Its emergence is driven by the uptake of Social Platforms (blogs, microblogs, Social Networks) the platforms on which the Social Graph is expressed and lives. Every participant has their own Social Graph (to whom and how they are connected), and the superset is a connectivity mesh of immense complexity (around the world in Six Degrees). Social Search seeks to leverage information within the Social Graph to provide improved relevance of results. Simplistically, we are influenced by our connections, therefore recommendations from trusted ‘friends and digital acquaintances’ have an obvious relevance and appeal. Trust, relevance and measurable contribution are key areas on which to focus. For an excellent introduction to the topic I recommend “Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives” by Dr. Nicholas Christakis and Dr James Fowler, as well as “Trust Agents” by Chris Brogan and Julien Smith.

A range of start-ups (including some notable failures) have been active in Social Search experimentation (Sproose, Mahalo, Jumper 2.0, Wikia Search, Qitera, Scour, Wink, Eurekster, Baynote, Delver, OneRiot and SideStripe). Of ‘more’ interest however are ongoing trails in Google Labs…

Travelling back through the annals of time we recall a “Web of Machines” an interconnected backbone and fabric, the foundations of the modern Internet. Search Engines arose, linking strategies developed; soon links between sites became tradable commodities. Reciprocal linking remains a popular SEO (search engine optimisation) strategy (you link to my domain, I link to yours). A standard Webmaster to Webmaster behaviour was soon under significant ‘manipulation’ (aka gaming) by link spammers and link farms. This targeted link popularity at a domain level, but the ‘take home’ is that once search algorithms are (at least partially) understood, and there is benefit in higher placement in search engine results, then ‘algorithmic gaming’ is ever present.

The Social Web has given rise to a new form of linking, inter-personal links within the Social Graph. “You follow me, I follow you” is a personification of ‘old school’ reciprocal linking. The domain is no longer the ‘back link’; it is the ‘personal’ connection in the Social Graph. It could be argued that this is just good social graces, I’m interested in you, and therefore you should express and reciprocate the same interest. As with link farms and link spamming in the “pre-Social Web”, we are of course seeing a volume of similar misbehaviour affecting the Social Graph across today’s Social Platforms.

My concern and what I want to highlight in this piece is the potential to skew emerging Social Search algorithms, and how they must account for ‘hyper-connected gaming’. Naturally what motivates this (mis)behaviour is ’short cutting’, in other words rather than build up a following organically through ’service to the connected community’, you simply ’snowball’ a following using automated techniques such as ‘mass following’. Twitter is the ultimate sandpit. If not fuelled by it, it could certainly be argued that it is well lubricated with snake oil. This is not in itself a criticism of Twitter’s model, but rather recognition that auto-pilot users (often “mavens” or “work at home” marketing specialists) are ’swamping’ the platform with all manner of affiliate schemes which they promote through mass communication and mass following techniques. This is not what I classify as pro-social behaviour.

The great joy of the Social Web of course is that people and behaviours can be ignored and dismissed. Un-friend, un-follow, block are all readily available choices. Surprisingly however, research shows that we rarely do much housekeeping on our online networks and hence the Social Graph is additive rather than truly reflective.

With that précis of how I view some online social or really anti-social behaviour, I return to my concern of how the Social Graph is open to manipulation. I’ve written on numerous occasions about proactive Social Networking and how I feel this is often beneficial.

Connections extend possibilities, but there is a value to those connections and indeed how we behave in the social context of those connections, be that by social graces (etiquette) or through positive contribution to group and community dynamics. I very much view proactive or speculative networking on sites like Twitter to be very useful. Indeed my metaphor for such is a “tap on the shoulder”; Twitter being particularly valuable in this regard due to its non-invasive nature.

I am currently participating in the Social Search experiment on Google Labs, and it is through this that we must seek to vanquish the Social Chimera’s influence. The principle of the experiment is “more easily find relevant blogs, reviews and other public content from your social circle”. The social circle is determined by the Social Graph, for the purposes of this experiment being links and connections found within Google Profile. In my case this points to all of my Social Site presences such as LinkedIn, Twitter, Facebook, YouTube. You begin to see the potential, the more I am connected within the ‘graph of others’ the more likely that my recommendations and interests show up in the Social Search results of others (establishment of motive and opportunity). Manipulation of this centrality might therefore yield increased influence or (heaven forbid) opportunity to drive monetisation through questionable affiliate schemes. This presents problems; new motivations to drive hyper-connectivity (now inter-personal), a need to filter the Social Graph and the Social Search results and clear them of the behaviours associated with such manipulation.

The good news is that Google is astute and experienced in recognising, accounting for and penalising algorithmic gaming. But all is not simple. There are some very pro-social characters heavily involved in Social Media that have 100,000+ followers on Twitter. Many also follow the same number. Does this denote egalitarianism and utility, or something less admirable? It certainly does not provide transparency (in the link alone) to the utility and nature of that relationship to all 100,000 connections. We need to look therefore at relationship reinforcement in the Social Graph. If two people are tagged in a photograph they are “close by definition”, multiple conversations, multiple connections across disparate social sites also reinforce connections. But this still lacks sophistication as it negates (or dilutes) the role of the influencer. Such relationships might be more characteristically ‘one-way’, but none the less I might be more interested in the Social Search result of an influencer rather than a weak connection. It is also difficult to ascertain current “levels of manipulation” and how people within those networks should be accounted for (or discounted). Twitter seems endemically littered with ‘friend collectors’, fuelled by an insatiable (and mistaken) hunger for collection of worthless and highly contrived influence. So this presents the dilemma of how to filter the signal from the noise. This epitomises what I believe to be a principle challenge of Social Search.

What is noise, what is the signal and how is this algorithmically quantified across a vast array of differing Social Graphs, how do we qualify and ‘level’ the meaning and importance of relationships?

Beyond some of the basic reinforcement checks I described earlier, I suggest semantic analysis, sentiment analysis, measuring utility in relationships and contributions are of primary appeal for research and development. Personal control is also important. It could be argued that this is intrinsic in controlling personal Social Graphs at their source, but this involves restraint and is very difficult to visualise (it is also not a great deal of fun and constrains the potential of proactive networking). Control over the search results and how these feed through the Social Graph to the results of others (i.e. privacy control) also needs to be thought through. This could lead to the creation of additional Inference Channels, which we may prefer remained ‘hidden’.

I encourage you to engage in the Social Search experiment, at the same time ruminating on your perception of social participation on the Web. The motivations of others naturally need to be questioned, but drive your networking on the basis of pro-social activity. Share, contribute, grow, but be cognisant of the Social Graph, its emerging centrality to Social Search and a need to preserve its integrity. Equally, follow with interest further debate concerning algorithmic tuning to ensure social results are not “manipulated” by hyper-connectors. Ponder also Google’s strategy in Social Networking.

Create profiles, connect with others, collaborate and share: Google Profiles, GMail, Google Reader, Google Groups, Google Side Wiki, Google Talk, YouTube, Picasa, Google Wave, Social Search and so forth. There is no direct landing page or dedicated Portal (with the exception of Orkut), but ‘all of the above’ sounds a lot like a decentralised, feature rich Social Networking platform! Could it be argued that Google’s lack of explicit “overarching site” is leading to more natural social interaction and a purer emergent Social Graph from those actions?

I might typically end with a rhetorical “will Google be the ‘glue that binds’?”, but certainly it already is. It is the ultimate in “decentralised” Social Networking, Social Search being a tantalising addition if utility and purity can be appropriately delivered. Page Rank established a mechanism for quantifying importance and authority of sites. Will “Page Rank for People” emerge as a ’solution’ to Social Search manipulation?