Skyrim- it’s fun because it’s shaggy

I know, I know. I’ve been completely remiss in updating this blog. It’s not that I haven’t had content to add, but that I’ve been too busy to take the time to write it. I hope to catch up on that after the Christmas break, once the burden and expectations of coursework have abated, and my only priorities will be thesis research and whatever projects tickle my fancy (believe me, there are more than a few just waiting in the wings).

In addition to being super-busy with thesis prep and coursework, most of my at-home free time has been dominated by video games. For the directed study I’d planned this term, I played through both Dragon Ages; then some content analysis coding related to Mass Effect came up in my research methods course, and I just couldn’t help myself. And more recently, Skyrim, the most recent installment in the Elder Scrolls franchise was released, and I find myself well and truly addicted.

There are several things about Skyrim that set it apart from other games, but the most important quality is the high level of freedom given the gamer to explore the gameworld. Not all the rules are obvious; it often seems more like a simulation game than an RPG. And in a world as big as Skyrim, this can lead to unexpected results. The AV Club’s review of the game puts it best in the following excerpt:

The soul of Skyrim isn’t in these meticulous improvements, but in its shaggier side. Not every aspect of this world lines up perfectly. You might be anointed by an ancient priesthood as the greatest warrior in all the land, only to walk 10 yards down the road and get slaughtered by a stray bear. Incongruities like this arise all the time—characters behave weirdly, and quests veer off-script. It isn’t just about bugs, although there are some of those. These eccentricities are the result of an extremely detailed organic world acting out in unexpected ways.

Skyrim lets these rough edges show, because the element of chaos lets players feel like the game is happening to them, and they are alive in it—not just cogs in a pre-fab Game Experience. That’s what sets Skyrim apart from some of its contemporaries. Where many games with lavish production values seek to direct players’ imaginations, Skyrim seeks to ignite them.

New term, new posts

So it’s that time of year again. The start of the new term means new courses, new projects, and new posts on the blog.

A description of what to expect:

After wrapping up my study of social media use at the reference desk at Grant MacEwan University, I’ll be conducting a similar study with librarians at the University of Alberta. This project represents roughly a third of the work I’ll be completing over the next few months, as well as a significant chunk of the research I intend to use for my thesis. This study, ostensibly, is framed within an LIS course entitled “Advanced Research Methods”, where (mainly) thesis students in the program form a support group to get through the early phases of their thesis research. I’m actually pretty excited about this project, particularly since I’m going into it with findings from my summer study. I expect to post one or two updates over the course of the term, at the least.

I’ll also be taking a course on Reference Services. Not sure if that’ll actually make it on the blog in any form, but it’s worth mentioning insofar as it’s something I’ll be preoccupied with.

The course I’m most anticipating, and that will definitely be featured in most of this Fall’s blog posts, is a directed reading called “Video Game Criticism”. For this, in addition to a ton of self-assigned readings, I’ll be playing Dragon Age 2 and subsequently writing a critical analysis of the game. The basis for this course– unlike most video game courses, which tend to be focused on design and production– is summarized by Ian Bogost in his introduction to Unit Operations:

…similar principles underlie both contemporary literary analysis and computation. I will use this commonality to analyze a field of discursive production that has yet to bind an authoratative place in either world– videogames. […]A practical marriage of literary theory and computation would not only give each field proper respect and attention from its counterpart, but also create a useful framework for the interrogation of cultural artifacts that straddle these fields.

In other words, I’m interested in developing a model or framework for studying video games that is analogous to how we perform literary criticism. As both an English student and a video game enthusiast (not to mention a digital humanist), the most urgent question is why I haven’t thought of doing a directed reading like this before.

Chief component of the directed reading– like last Winter’s directed study in social media and Knowledge Management– is to maintain journal entries (read: blog posts) about my progress in and thoughts of the game, and my synthesis of related readings about game design and theory.

In addition to the more formal journal entries (or “response papers”), I would like to start using this as a personal blog once more. I plan on at least making the attempt; in the past I’ve never been able to consistently keep that sort of thing up.

On a final note, a former professor of mine emailed me a link to this blog post about defining the digital humanities. Imagine my surprise in discovering that my own definition of DH, supplied for 2011’s Day of Digital Humanities, was prominently cited. As a mere graduate student, I feel sheepish about “eschewing disciplinary rigor”, adroitly or not (who am I to fight convention, after all?), but proud all the same that I apparently managed to “capture the spirit” of the DH community.

I must tip my cap to Eric Forcier, whose reply adroitly eschews disciplinary rigor in favor of admirably capturing the spirit of the DH community—especially in painting DH as an ephemeral, seemingly idiosyncratic curiosity that either attracts or repels people, and often changes them fundamentally:

When I first applied to this grad program, my understanding of what DH was all about was crystalline in its purity. Not so today. My idea of DH is that it’s sort of like a highway oil slick on a sunny day. When you look at the slick, depending on the angle, you might get a psychedelic kaleidoscope of reflected colours; if you’re lucky you might spot your reflection in it; then again, all you might see is darkness. And if you feel compelled to step in it, don’t be surprised if you slip. Those stains will not come out. -Eric Forcier, University of Alberta, Canada

I’ll try not to let it go to my head.

Crowdsourced Intelligence and You

This post should have gone up ages ago, as part of a course assignment for HUCO 510.  Sometimes you just get side-tracked.  Anyway, this week something happened that gave me the perfect topic to complete my assignment.  Enjoy.

~~

On May 2, 2011 Osama Bin Ladin, one of the most feared terrorist leaders in the world, was killed.  Nearly a decade after the September 11 attacks on the World Trade Center in New York, attacks orchestrated by Bin Laden, US Navy Seals successfully carried out the assassination.  A nation rejoiced.

And, as that nation rejoiced, within minutes of the news being made public on the Internet and on television, all social media websites were abuzz.  One can imagine the sheer volume of the expressions of support, opposition, incredulity, happiness, sadness, congratulations and disgust that flooded the web.  Or, one can simply search “osama” on the Twitter index.  The President would later televise an address to the nation confirming the death of the man who had been cast in the role of nemesis to an entire people and way of life.

It is during these kinds of world-changing events that the most interesting insights about our society are discovered.  Megan McArdle, editor for The Atlantic, made one such discovery, as she browsed her Twitter feed on the fateful day.  One tweet in particular caught her eye.  Being one of Penn Jillette’s 1.6 million followers, she read the following quote, apparently in response to the death of Bin Laden:

“ I mourn the loss of thousands of precious lives, but I will not rejoice in the death of one, not even an enemy.” – Martin Luther King, Jr

Amid the—no doubt—millions of reactions, some of them shocking, this short sentence at least had the ring of reason.  And it was attributed to perhaps the most famous civil rights activist in North America.  A combination of Jillette’s celebrity as a performer and this level-headed response to the event in contrast to many much less level-headed responses made it viral; within hours of it going up on Twitter, many of Jillette’s followers had retweeted the quote, and it had become a trending topic on the social network, in the midst of the Bin Laden furor.  McArdle, unlike many others, did not retweet the quote, though she did initially feel the urge to pass it on.  She hesitated, however, because it didn’t “sound” like Martin Luther King, Jr.  And for that hesitation, I am sure she was later grateful, when it was soon discovered that the quote was misattributed.

Besides the end to privacy (which I’ve repeatedly discussed on this blog), another quality of modern communication technologies that we must all adapt to is the speed at which information travels.  Networks like Twitter and Facebook increase the rate of transmission exponentially.  The cult of celebrity has also found fertile earth in these virtual spaces.  If I had been the person to publish the quote on Twitter, with my 80 or so followers, rather than Jillette, the quote would not have been so popular, and the backlash would not have been so severe.  The fact that the initial tweet reached 1.6 million people dramatically increased how quickly the quote spread from that point.  So where did Jillette get the quote?

Despite some media outlets implying that he did this deliberately to mess with his followers, it seems clear now that it was accidental.  Jillette copied the quote from a Facebook user’s status update that read:

I mourn the loss of thousands of precious lives, but I will not rejoice in the death of one, not even an enemy. “Returning hate for hate multiplies hate, adding deeper darkness to a night already devoid of stars.  Darkness cannot drive out darkness: only light can do that.  Hate cannot drive out hate: only love can do that.” MLK jr

In viewing this, it is clear that Jessica Dovey, the Facebook user, was adding her own interpretation to an authentic quote by Martin Luther King, Jr.  Jillette tried to copy it to Twitter, but given the 140 character limit for tweets, was forced to edit it down.  Apparently he did not realize the first sentence was not part of the quotation.  Jillette later apologized repeatedly for the tweet, stating that it was a mistake.

“Why all the fuss over this?” one might ask.  It seems that most people are upset not so much by the misattribution as they are at the criticism of the popular reaction and the media circus that has surrounded the assassination.  Dovey and Jillette, and McArdle as well, who went on to write a blog post and editorial in The Atlantic online about her discovery of the misattribution, have faced a great deal of criticism since the quote was first shared.

We live in a world of memes, in a place where information—regardless of its accuracy or authenticity—is shared at an exponential rate, and where fiction can be accepted as fact based on who says it and how many believe it.  The only thing surprising about this particular incident is that the mistake was discovered and the truth of it spread online as fast as the initial tweet did.  If it had taken a day or two longer for someone like McArdle, with a platform to spread the information, to discover the mistake, would anyone have noticed?  Probably not.  It is not like people haven’t been misquoted or misattributed in the past.  What’s noteworthy is the speed at which this particular misquote proliferated.

I find this interesting because, as I have stated, it gives evidence of how communication has changed in our society.  Many of us rely on sources like Twitter to engage with current events.  It serves us well to be reminded that, in spite of the many benefits of crowdsourced intelligence, the onus for fact-checking is on the reader.

Update

I know it seems like I haven’t posted since February, but I’ve actually got a backlog of entries that I just haven’t had a chance to put up yet.  I’ll be getting this up today (all related to LIS 599: KM) and back-dating them.

Also expect in the next week or so a blog post for HUCO 510: Theory of Humanities Computing.  Haven’t quite decided what to write about yet, but I would like to somehow incorporate this article about Bruce Sterling’s library getting archived, and his comments on digital preservation.

Also: How could I forget to mention my Day of DH blog?  That went up on March 18, and was actually completed on March 25.

Assessing Social Media – Methods

I have written about various social media and web technologies as they relate to knowledge management (KM), and as they are discussed in the literature.  But I haven’t really touched on how the literature approaches measuring the application and success of such technologies in an organizational context.  Prusak notes that one of the priorities of KM is to identify the unit of analysis and how to measure it (2001, 1004).  In this review paper I will examine some of the readings that have applied this question to social media. For the sake of consistency, the readings I have chosen deal with the assessment of blogs for the management of organizational knowledge, but all of the methods discussed could be generalized to other emerging social technologies.

Grudin indicates that the reason most attempts at developing systems to preserve and retrieve knowledge in the past have failed, is that digital systems required information to be represented explicitly when most knowledge is tacit: “Tacit knowledge is often transmitted through a combination of demonstration, illustration, annotation, and discussion.” (2006, 1) But the situation, as Grudin explains, has changed—“old assumptions do not hold…new opportunities are emerging.” (ibid.) Virtual memory is no longer sold at a premium, allowing the informal and interactive activities used to spread tacit knowledge to be captured and preserved; emerging trends such as blogs, wikis, the ever-increasing efficiency of search engines, and of course social networks such as Twitter and Facebook that have come to dominate the Internet landscape open up a multitude of ways in which tacit knowledge can be digitized.

In his analysis of blogs, Grudin identifies five categories (2006, 5):

diary-like blogs, or personal blogs, developing the skill of engaging readers through personal revelation;

A-list blogs by journalists and high-profile individuals, as a source of information on events products and trends;

Watchlists, which track references across a wide selection of sources, reveal how a particular product, organization, name, brand, topic, etc is being discussed;

Externally visible employee blogs provide a human face for an organization or product, which offsets the potential legal and PR risks for a corporation.

Project blogs are internal blogs that focus on work and serve as a convenient means of collecting, organizing and retrieving documents and communication.

Lee, et al. make a similar move in categorizing the types of public blogs used by Fortune 500 companies (2006, 319):

Employee blogs (maintained by rank-and-file employees, varies in content and format)

Group blogs (operated by a group of rank-and-file employees, focuses on a specific topic)

Executive blogs (feature the writings of high-ranking executives)

Promotional blogs (promoting products and events)

Newsletter-type blogs (covering company news)

Grudin does not conduct any formal assessment of blogs, except to provide examples of project blogs, and to assign technical and behavioral characteristics to that particular sub-type that allowed them to be successful, based on his personal experience (2006, 5-7). Lee, et al.’s approach to assessing blogs involves content analysis of 50 corporate blogs launched by the 2005 Fortune 500 companies (2006, 322-23). In addition to the categories above, Lee, et al. also identified five distinct blogging strategies based on their findings, which broadly fall under two approaches (321):

Bottom-up, in which all company members are permitted to blog, and each blog serves a distinct purpose (not necessarily assigned by a higher authority)[1];

Top-down, in which only select individuals or groups are permitted to blog, and the blogs serve an assigned purpose that rarely deviates between blogs.

As the names suggest, a greater control of information is exercised in the top-down approach, while employee bloggers in companies adopting the bottom-up approach are provided greater autonomy.

Huh, et al. developed a unique approach in their study of BlogCentral, IBM’s internal blogging system (2007).  The study combined interviews with individual bloggers about their blogging practices and content analysis of their blogs.  Based on this data, they were able to measure two characteristics of blogs: the content (personal stories/questions provoking discussion/sharing information or expertise) and the intended audience (no specific audience/specific audience/broad audience).  These findings revealed four key observations:

– Blogs provide a medium for employees to collaborate and give feedback;

– Blogs are a place to share expertise and acquire tacit knowledge;

– Blogs are used to share personal stories and opinions that may increase the chances of social interaction and collaboration;

– Blogs are used to share aggregated information from external sources by writers who are experts in the area.

Rodriguez examines the use of WordPress blogs in two academic libraries for internal communication and knowledge management at the reference desk (2010).  Her analysis measures the success of these implementations using diffusion of innovation and organizational lag theories. Rogers’ Innovation Diffusion Theory establishes five attributes of an innovation that influence its acceptance in an organizational environment: Relative advantage, compatibility, complexity, triability, and observability (2010, 109). Meanwhile, organizational lag identifies the discrepancy between the adoption of technical innovation—i.e. the technology itself—and administrative innovation—i.e. the underlying, administrative purpose(s) for implementing the technology, usually representing a change in workflow to increase productivity.  In analyzing the two implementations of the blogging software, Rodriguez discovers that both libraries succeeded in terms of employee adoption of the technical innovation, but failed with the administrative innovation.  This was due specifically to the innovation having poor observability: “the degree to which the results of the innovation are easily recognized by the users and others” (2010, 109, 120). The initiators of the innovation in both cases did not “clearly articulate the broader administrative objectives” and “demonstrate the value of implementing both the tool and the new workflow process.” (2010, 120) If they had done so, Rodriguez suggests, the blogs might have been more successful.

While all of these studies approached blogging in a different way—project blogs, external corporate blogs, internal corporate blogs and internal group blogs—and measured different aspects of the technology—what it is, how it is used, if it is successful—they reveal a number of valuable approaches to studying social media in the KM context. Categorization, content and discourse analysis, interviews, and the application of relevant theoretical models are all compelling methods to assess social media and web technologies.

 


[1] One of the valuable contributions of Lee, et al.’s study is to also identify the essential purposes for which corporate blogs are employed. Some of these include product development, customer service, promotion and thought leadership. The notion of ‘thought leadership’ in particular, as a finding of their content analysis, is worth exploring; ‘thought leadership’ suggest that the ability to communicate innovative ideas is closely tied to natural leadership skills, and that blogs and other social media (by extension) can help express these ideas. Lee, et al.’s findings also suggest that ‘thought leadership’ in blogs will build the brand, or ‘human’ face of the organization, while acting as a control over employee blogs, evidenced by the fact that it is found primarily in blogs that employ a top-down strategy.


Bibliography

Grudin, J. (2006).  Enterprise Knowledge Management and Emerging Technologies. Proceedings of the 39th Hawaii International Conference on System Sciences. 1-10.

Huh, J., Jones, L., Erickson, T., Kellogg, W.A., Bellamy, R., and Thomas, J.C. (2007) BlogCentral: The Role of Internal Blogs at Work.  Proceeding Computer/Human Interaction CHI EA 2007, April 28-May 3. 2447-2452. San Jose, CA.  doi <10.1145/1240866.1241022>

Lee, S., Hwang, T., and Lee, H. (2006). Corporate blogging strategies of the Fortune 500 companies. Management Decision 44(3). 316-334.

Prusak, L. (2001). Where did knowledge management come from? IBM Systems Journal, 40(4), 1002-1007.

Rodriguez, J. (2010). Social Software in Academic Libraries for Internal Communication and Knowledge Management: A Comparison of Two Reference Blog Implementations. Internet Reference Services Quarterly 25(2). 107-124.

Collective Intelligence, Web 2.0, and Understanding Knowledge

One of the key elements of Web 2.0, as established by Tim O’Reilly in his 2005 paper “What is Web 2.0?”, is the notion of ‘collective intelligence’.  The term itself does not suggest any particular type of technology; rather, it evokes an epistemological stance toward the concept of ‘intelligence’— if ‘intelligence’ is the cognitive capacity to think and learn, ‘collective intelligence’ implies the capacity to think and learn together, as a group. ‘Collective intelligence’ is the capacity to think, learn and share knowledge. Web 2.0 is more of a paradigm than simply a new breed of information technologies; it is a shift in how we perceive the ways in which knowledge is shared, by expanding the means of knowledge production to non-specialists.  A prime example of this principle is Wikipedia; once upon a time, encyclopedias (such as Britannica) were produced by a small group of subject specialists, high priests of their respective domains.  Wikipedia’s model transformed this approach, stripping the high priests of their power and opening up the opportunity to produce, edit and debate content to all.  The results are revealing—while occasionally entries on Wikipedia lack the accuracy of a traditional encyclopedia, they almost always reflect the current debates that surround a given topic, revealing the fluid nature of such knowledge.  This is not something one could easily apprehend from a traditional encyclopedia.  Why? Because the knowledge is mediated by a variety of perspectives, rather than one alone. That’s the power of collective intelligence[1].

In his remarks at the launch of the MIT Center for Collective Intelligence (2006), Thomas Malone defines ‘collective intelligence’ as “groups of individuals doing things collectively that seem intelligent.” As Malone makes clear, this is not a new idea—in the same way that knowledge management (KM) builds on concepts that have existed for decades, even centuries, ‘collective intelligence’ can be considered in a particular way as a new name for old ideas.  What makes it (and what makes KM) ‘new’ again is its potential application through new information technologies (i.e. the Web):

It is now possible to harness the intelligence of huge numbers of people connected in very different ways and on a much larger scale than has ever been possible before. (Malone, 2006).

The question becomes: “How can people and computers be connected so that collectively they act more intelligently than any individual, group or computer has ever done before?” (ibid.) This same question is reflected before Web 2.0, rather prophetically, in Marwick’s consideration of KM technology (2001).  Channeling Nonaka’s model of organizational knowledge creation, Marwick emphasizes the value and importance of tacit knowledge, while identifying the shortcomings of then-current technologies. The great hope for Marwick is ‘groupware’, a broad term that perhaps has less currency today referring to portals, intranets and collaborative software packages to facilitate group communication and project work.  In 2001, Marwick refers to such tools as ‘applications’ or ‘products’, standalone packages that organizations purchase and own; it is significant that the Web 2.0 paradigm eliminates the accuracy of such phrasing to describe collective intelligence—or social media—tools.  Rather, the web itself has become the ‘product’, the platform, and the tools are services.  This distinction is essential: the difference between a handful of software packages for computer-supported cooperative work and a universally accessible platform for social media is that one better reflects the interconnected nature of activities involved in the knowledge creation process. While Nonaka’s model of knowledge creation is split into four categories (socialization, internalization, externalization, and combination) that describe the type of transfer of knowledge that occurs between individual and group, tacit knowledge and explicit knowledge, it is unified conceptually as a spiral that circles through these categories in an eternal series of overlapping cycles.  Pre-Web 2.0, this poses a problem for KM, because it means that a variety of technologies—many of which will not communicate well, or at all, with each other—needs to be employed at each stage. There is no continuity, no sense of connection between one tool and the next, when the process of knowledge creation is by its very nature continuous and interconnected. Web 2.0 gives us the paradigm with which to understand that continuity. It also gives us the potential for collective intelligence that Malone is so excited about.

Collective intelligence in the Web 2.0 context is by no means flawless.  In fact, this approach to understanding knowledge has led to a whole new set of problems.  While we might be less concerned today than Marwick in 2001 about the sharing of tacit knowledge through technologies, thanks to an ever-expanding assortment of social networks available on the web which situate individuals, communities and organizations in relation to one and other, the explosion of information in such an unimaginably vast array poses increasingly difficult challenges.  In 2006, Grudin writes that there was some concern at the time when photos tagged as ‘london’ in Flickr jumped from 70,000 to 200,000 over three months.  Would this be a “tragedy of the commons”, a tool that shows such promise, combining folksonomic tagging with user-generated photographic collections, grown out-of-control? But then Flickr introduced clusters, subsets and pools to re-organized tagged content in a more refined way; crisis averted, and new innovation achieved.  While we have come a long way from Marwick’s groupware, we are still struggling to grasp how concepts like ‘collective intelligence’ and ‘Web 2.0’, and their associated technologies, can help KM.  New challenges and innovations are encountered every day.  And as Grudin suggests, “These are still early days.”


[1] That’s not to say that the collective intelligence or crowdsourcing principle that underlies web social media is definitively superior; quite the opposite, Web 2.0 introduces a new host of challenges, such as determining reliability, issues of intellectual property, and organization of information that were not nearly as problematic from a traditional approach to knowledge creation.


Bibliography

Grudin, J. (2006). Enterprise Knowledge Management and Emerging Technologies. Proceedings of the 39th Hawaii International Conference on System Sciences. 1-10.

Malone, T. W. (2006, October 13). What is collective intelligence and what will we do about it? MIT Center For Collective Intelligence. Retrieved from http://cci.mit.edu/about/MaloneLaunchRemarks.html

Marwick, A. D. (2001). Knowledge management technology. IBM Systems Journal, 40(4). 814-830.

O’Reilly, T. (2005, November 30). What is Web 2.0? Design Patterns and Business Models for the Next Generation of Software. O’Reilly Media. Retrieved from http://oreilly.com/web2/archive/what-is-web-20.html

Twitter and the KM Context

[W]e came across the word “twitter,” and it was just perfect. The definition was “a short burst of inconsequential information,” and “chirps from birds.” And that’s exactly what the product was.
– Jack Dorsey (Sarno, 2009)

Twitter, the popular microblogging tool from which users post updates in 140 character increments, recently celebrated its five-year anniversary. In the world of fly-by-night Web 2.0 applications, that makes it a well-established and time-tested social technology. What has contributed to Twitter’s success? Why is it such a popular tool?

As its co-founder, Jack Dorsey, suggests in the quotation above, Twitter is a place where users can publish short bursts of information and share it with a larger community. It is “the best way to discover what’s new in your world”, reads the website’s about page (http://twitter.com/about). Still, users unfamiliar with the platform or dubious about this claim might wonder precisely how this tool can be productive. After all, Dorsey’s endorsement is not exactly inspiring: what good is information if it is inconsequential? What makes Twitter such a powerful tool, from both a knowledge management or business perspective and the broader context of information-sharing is how it operates in real-time. It allows members of communities of practice to track relevant news and share important events as they happen. This crowdsourcing approach to information means that users who follow other users publishing information relevant to their community of practice can keep their finger on the pulse—an extremely valuable commodity in a world that is increasingly knowledge-centric. Similarly, these users can participate in a live, public conversation within a global network of peers, encouraging an ongoing exchange of knowledge. More importantly, the simple premise of “following” (or, in other words, subscribing to user feeds) allows complete personalization, while creating links between users that shape community networks organically, rhizomatically.

Another advantage of Twitter is that it is highly scalable. Twitter has an API (Application Programming Interface) that allows customized software to be built around the basic platform. In this way, users can log in to their account using third-party software like TweetDeck, which allows them to organize and view tweets in a variety of ways. In addition, this characteristic also allows the development of widgets to publish tweets on websites and blogs. Viewed as much as a disadvantage as an advantage, the 140-character limit on updates forces users to state a single idea clearly and concisely. This limitation was originally due to the average character limit for text messages from cell phones, which had been considered by the founders as the principal technology for using the service. Soon after the service went public, however, most smart phone models no longer had that limitation on text messages. By then users had discovered that the character limit was the ideal length for short status updates; the limitation distinguishes Twitter from other casual blogging services such as Tumblr, which, no doubt, helped promote the service as a brand. While sometimes inconvenient for users with longer, more elaborate messages, the difference makes Twitter unique as a social media tool.

A definite disadvantage of this technology, as with many social media technologies, is the public nature of updates and the murky notion of intellectual property. Twitter is perhaps more volatile in this sense than other, similar technologies like blogs or wikis, which require more thoughtful consideration before publishing. The brief nature of tweets make it easy for users to submit whatever they happen to be thinking or seeing, regardless of legal considerations such as intellectual property or copyrights, and updates are published immediately without the opportunity to review or delete before they go live. This can be problematic for users, particularly high-profile users; one dramatic example, though certainly not the only one, would be a tweet that resulted in the termination of one CNN correspondent. In 2010, Octavia Nasr was fired for publishing an update expressing regret over the death of the Grand Ayatollah Mohammad Hussein Fadlallah, a leader of Hezbollah. Twitter poses a problem for e-Discovery that courts around the world have not yet come to terms with.

To provide a nuts-and-bolts explanation of how Twitter works and to help understand its practicality, it is useful to consider the following scenario: You are interested in motorcycles, and want current information about promotions, events, and people in your area related to that interest. You create an account on Twitter.com, and search the website for likeminded users. Scanning through user profiles, you decide to follow users representing several motorcycle dealers in your city, a couple motorcycle riding clubs, a national motorcycle news magazine, and a number of individuals who identify themselves as “motorcycle enthusiasts”. You begin receiving these users’ updates (or tweets), and begin to learn about the local motorcycle community. After a few days of reading tweets, you learn that there is going to be a bike show and that several of the users will be attending. You are unable to attend the bike show yourself, but you get to experience it through the tweets of your fellow users, who describe the event and post pictures of different models on display. You are able to engage some of these users, asking them questions about the event as it is taking place. You also discover that there is a hashtag that Twitter users are using to identify tweets about the event, and by searching all tweets that include that hashtag you discover several more users to follow. In this way information is exchanged, and you develop relationships with other members of the community that you might otherwise not have had. Now consider this same scenario in a different context: you have recently opened a motorcycle shop. Using the tool in the same way, Twitter becomes a valuable social tool for promoting yourself or your company, in addition to acquiring and sharing useful information.

Knowledge management (KM) resides in an interesting interdisciplinary space, somewhere between sociology, philosophy and economics. In his 1962 article, “The Economic Implications of Learning by Doing”, Nobel-prize winning economist Kenneth Arrow clearly states the necessity for organizational practices that manage the learning process; the economics of KM are concerned with breaking down and quantifying this process. In The Tacit Dimension (1966), Michael Polanyi describes the concept of “tacit knowing”; knowledge that deals with the implicit nature of human experience, skill and action is considered tacit, while knowledge codified and transmittable through language is explicit. Polanyi’s epistemological model serves as the fundamental principle of KM, distinguishing knowledge from the concepts of information and data. The sociological underpinnings of KM provide us with the a sound basis for understanding “knowledge” as a concept and its notably various manifestations, while also giving us a framework for making sense of how knowledge circulates within communities and through individuals. The seminal work of Emile Durkheim lends KM a primary concern with the “social facts”—the observable behaviours at the root of human interaction. Rather than relying on theory, KM is preoccupied with studying how people actually share, learn, and use knowledge. KM arose from these disciplinary cornerstones in the early 1990s, when an increased emphasis on the creation, dissemination and utilization of organizational knowledge in professional and scholarly literature identified a growing need for a systematic approach to managing information and expertise in firms. Laurence Prusak identifies three social and economic trends that make KM essential in any organization today: globalization, ubiquitous computing and “the knowledge-centric view of the firm” (1002). Prusak’s description of globalization in particular emphasizes the necessity to stay current; information technology has resulted in a “speeding up” of all elements of global trade, as well as an increase in the “reach” of organizations. Twitter is a technology that can facilitate this necessity.

There are any number of examples that demonstrate how Twitter fulfills the requirements of KM that I have described. In terms of leveraging group and individual interactions based on “social facts”, we can consider the role Twitter has played in the recent revolution in Egypt. Protesters on the ground in Cairo were publishing updates about the conflicts they faced, giving the crisis international exposure it might otherwise not have had. Following the government’s failed attempt to block Twitter—evidence in itself as to the effectiveness of Twitter for spreading a message—there was overwhelming support from around the world for the protestors against President Mubarak’s regime. This global support, along with the grassroots reporting of Egyptian demonstrators, certainly contributed to Mubarak’s ultimate resignation from office. This example shows how the knowledge of individuals in a particular context spread to other communities, and how this in turn inspired a global movement—based on the ever-expanding network of interactions through this particular social tool. The “social fact” inherent in Twitter is how human interaction manifests around these short bursts of highly contextual information, and how communities take shape by engaging in the same and other closely related contextual spaces.

An example of how Twitter facilitates the transfer of tacit knowledge might be the way events are recorded and experienced through it. Take, for instance, the recent SXSW Conference and Festival in Austin, TX, a yearly event that is recognized worldwide as a showcase of music, films and emerging technologies; a Twitter search for “#SXSW” reveals a host of users recording their experience through a variety of media—text describing talks, shows and screenings combined with links to photos, videos, and websites that together form an image of the event. These individuals’ experiences might not otherwise be expressible without a tool like Twitter that facilitates the blending of online multimedia. Moreover, the combined force of a community of users sharing these experiences at the same time can provide a comprehensive panorama of what they are hearing, seeing, and learning. In this way, Twitter allows tacit knowledge to be codified for mass consumption.

Measuring the impact of Twitter and how knowledge circulates through the network is not a simple task. Perhaps the most effective way to do so that we have today is the application of web and text analytics to social media. There are several companies that have recently achieved success in this area, based on textual data (e.g. lexical analysis, natural language processing, etc), user data (e.g. demographics, geographic data), and traffic data (e.g. clickstream, page views, number of followers/subscribers, replies and retweets, etc) mined from social media websites. Canadian-based Sysomos has used MAP (Media Analysis Platform) to provide an in-depth analysis of how people, products and brands are effectively marketed through Twitter and other social media tools. One reviewer describes MAP as follows:

MAP can, for example, tell you that the largest number of Twitter users who wrote about the Palm Pre come from California and Great Britain, as well as who the most authoritative Twitter users who tend to tweet about the Pre are (MAP assigns a score from 1 to 10 to every Twitter user, based on the number of followers, replies, retweets, etc.). Of course, you can then also compare these results with results from a query for ‘iPhone,’ for example. (Lardinois, 2009)

MAP, in fact, was used for an analysis of users during the crisis in Egypt. Some of the visualizations of this data are available online[1] . A recent study comparing social media monitoring software identified five key categories that need to be considered to appropriately measure the effectiveness of a social media tool (FreshMinds Research, 2010):

  1. Coverage – Types of media available based on geographic coverage.
  2. Sentiment analysis – The attitude of the speaker/writer with respect to the topic, based on tone.
  3. Location of conversations
  4. Volume of conversations
  5. Data-latency – The speed at which conversations are collected by a tool, based on the frequency of its web crawlers and the length of time it takes the monitoring tool to process the data.

As the researchers who undertook the study indicate, the possibilities for such data, from both a qualitative and quantitative perspective, are “huge”. Social media monitoring allows us to examine any number of factors in the learning and communicative process as it is manifested through social media technologies, “from category choices to the lifestyles of different segments”, on an individual or at an aggregate level (ibid.). The research group also identifies areas in which social media monitoring needs to improve—particularly within the realm of sentiment analysis. The monitoring tools are not sophisticated enough to provide an accurate measure. While Twitter in itself can be thought of as an organizational practice for knowledge-sharing, the application of monitoring tools can be thought of as Arrow’s organizational practices for managing knowledge. Based on the analysis that such monitoring tools—like Sysomos’ MAP—can provide, organizations and individuals can make more effective use of Twitter.

It is clear that Twitter can be a huge benefit for the effective creation and dissemination of knowledge, if used correctly. Organizations that are prepared to invest the time and energy in a sound social media plan to improve KM would be remiss not to include a presence on Twitter. On the other hand, this technology poses many risks for organizations, particularly in the realm of e-Discovery. The fact that content published to Twitter resides on the website’s servers, and not in the hands of the organization must play an important factor in any organization’s KM assessment. Twitter is perhaps more useful for NFP organizations that have a mandate for advocacy and public promotion (take, for instance, SXSW). It also is useful for individuals with either a professional interest in promotional or informational knowledge-sharing (such as consultants, agents, performers, journalists and salespeople) or as members of an existing community (like our motorcycle enthusiast). The professional and the social are not easily distinguished on Twitter, which can be both a benefit and a curse for users, as we have seen. Finally, while the information shared on Twitter might seem “inconsequential” to some, to others it can be very valuable. It is this value that KM needs to harness, in order to effectively make use of Twitter.


[1] Visualizations for the Twitter data related to the crisis in Egypt can be found at http://mashable.com/2011/02/01/egypt-twitter-infographic/. For a compelling overview of the sort of data Sysomos has analyzed with respect to Twitter, an indispensable resource is their report “Inside Twitter: An in-depth look inside the Twitter World”, 2009: http://www.sysomos.com/docs/Inside-Twitter-BySysomos.pdf

Bibliography

Arrow, K. (1962, June). The Economic Implications of Learning by Doing. Review of Economic Studies 29(3), 153-73.

Durkheim, E. (1982). The Rules of the Sociological Method, Ed. S. Lukes. Trans. W.D. Halls. New York: Free Press.

FreshMinds Research. (2010, May 14). Turning conversations into insights: A comparison of Social Media Monitoring Tools. [A white paper from FreshMinds Research, http://www.freshminds.co.uk.] Retrieved on March 22, 2011 from http://shared.freshminds.co.uk/smm10/whitepaper.pdf

Lardinois, F. (2009, June 4). Pro Tools for Social Media Monitoring and Analysis: Sysomos Launches MAP and Heartbeat. ReadWriteWeb.com. Retrieved on March 22, 2011 from http://www.readwriteweb.com/archives/pro_tools_for_social_media_sysomos_launches_map_and_heatbeat.php

Polanyi, M. (1966). The Tacit Dimension. London: Routledge & Kegan Paul.

Prusak, L. (2001). Where did knowledge management come from? IBM Systems Journal, 40(4), 1002-1007.

Sarno, D. (2009, February 18) Twitter creator Jack Dorsey illuminates the site’s founding document. Part I. Los Angeles Times. Retrieved September 24, 2010 from http://latimesblogs.latimes.com/technology/2009/02/twitter-creator.html