• The paper is two-fold.
  • Describe the progression from reader to contributor/moderate poster/or helper.
  • Serve as a reference for future work on the subject. Special attention is given to the references.
• Social Participation is Categorized into 4 Types (Roughly)
  • Reading
  • Contributing – in a small way. Fix spellings. Tag photos. Agree w/ a post via a comment.
  • Collaborating
  • Leading
• The authors discuss various cute ways to entice users to use a site such as interface design, etc.
• Of interest is that the authors claim that users will read peer-posted material if it has perceived value to them. The challenge not touched on, however, is seeding that initial body of data with good data. Perhaps you create a resource and phase-in social networking as the community grows and starts to generate its own content?
• The authors also note that sites can also overwhelm a new participant if they do not give that new user enough tools to prune the data they are dealing with down to a managable level. An example from me is that forums have topics that hide the larger collection of discussions. Old discussions are hidden on a subsequent page. These two techniques limit information overload.
• Be sure to give new users small, low commitment, but valid feed back options. Eg, star a post as helpful. The Facebook poke is another good example of this.
• Gaining confidence is important. Feed back such as family members commenting or friends of immediate family commenting help to build a sense of safety.
• Mutual understanding, shared beliefs and assumptions are important.
• Collaboration distinguished from contributing by there being a feedback loop, or a negotiation of content. A simple correction is given as an example of contributing but not collaborating because the change did not require interacting with anyone.
• The concept of social capitalis mentioned as a reason for people to invest (pun slightly intended) in a social site. Capital is seen as content provided from a person’s expertise or talents.
  • The idea of gaining reputation as a motivator for continuing research/contribution is mentioned a few paragraphs later.
• Perceived similarities between participants fosters trust and then interaction.
• The notion of giving back or a some level of altruism apparently does impact participation in online communities.
• Status vs. Reputation
  • Status is absolute rank.
  • Reputation is a commonly held belief about an individual (which we assume here is a positive thing.
  • Status can be highlighted in the online world through restricted access or special flags next to a contributor’s name.
  • A personal question: How do these relate to the notion of influence?
• Two behaviors on Facebook are highlighted.
  • Connecting with Old Friends
  • Connecting with Important Friends
• A Leader is one who promotes participation.
• A Leader is also described as having influence over the community norms. This includes possible user blocking or dispute resolution.
• Leaders are distinguished from administrators or those assisting with only board policing.
• Word remains to develop metrics for progress along the stages the authors set forth.
• The authors focus on feature analysis of sites that promote their progression.
• An interesting dynamic touched on is when the site is disrupted (they use a login being changed or added as an example) what does the community re-organization look like? Shake the tree and see what falls. There is risk in destroying the community here so only necessary changes should be monitored. On a personal note, I’m on a forum that has changed technology 3 times and is still active and healthy. The disruption there did, interestingly (and anecdotally) have little effect on use.

It’s difficult to criticize a work that set out to serve as a starting point to further refine the field, but I will say that linear progressions like Reader-to-Leader never seem to last. They are easy to see, and are valid for a host of problems that might be studied, but communities are complex. People do not engage simply with communities, but with topics, thus their type of participation, engagement, and the role they are taking in the Reader-To-Leader framework, I maintain, may shift drastically given the context of that participant’s real life (what goes on beyond the keyboard) as well as the complex dynamics in the online community.

Still, this is a nice Framework that absolutely brings value to the conversation. It will be interesting as more sociologists start engaging in social networking dynamics to see if the old models they have are valid in cyberspace.

I would have also really enjoyed some examination of new members to a community as it relates to the hype cycle. Maybe some day. If you know of such a study, there are comments below!

Now, you’re probably wondering why I italicized the little quote about highly regarded sources, and if you are the foreshadowing type, you may already be able to guess where I’m going with this. First, let me say that most of the effective algorithms for extracting entities, and almost all of NLP, rely on fairly well formatted, “predictable” text. By “predictable” I mean that it follows formal grammar rules, etc. So, in selecting highly regarded sources (say, CNN?) you are constraining your statistical frame to sites that you can processes. There isn’t anything terribly wrong with doing what you’re good at, but I would like to argue that, at least at Networked Insights, we fight to keep away from this restriction. In part, formal writing carries a bias. It carries a message motivated by some latent motivations. Keeping a high reputation, for instance. It is an immensely more difficult task to harvest information from the drips-and-drabs of informal writing such as is found in twitter and forums (or even blogs).

It’s because of this that, while the thought of Evri is exciting, I don’t think it will tell you anything that you didn’t already know. Now, my goal isn’t to pick on Evri, but I think it is fascinating to realize that the reason that analyzing more formal and easily analyzed text on the web is a bit of a losing battle is because the formalism comes in part from the author knowing that we are watching. Fox or CNN write with a specific audience in mind, and that audience is the same audience that TV seeks to entertain, and on, and on, and on. What is so powerful about the social web is that it’s text produced with only an audience of two or three people being expected.

I say this is powerful in two ways. First, biases equal out by the sheer volume and diversity of publishers. If someone is trying to catch the attention of a particular audience and tailors their text to fit, then that intentional word-smithing is likely reduced by countless other authors with similar overall ideas to express, but different spins to put on the text. Second, since most of this text is being generated quickly, and admittedly not always that well thought out, the raw feelings of people tend to leak into the text. It’s the living room conversations had without thinking. It’s the reflexive “boo” at the stadium. This rawness, if you will, if far more valuable because it’s never what you were expecting.

There is power in having concrete numbers, even if they do not mean much in isolation, for at least the sake of comparison. One of the best examples of a measure without absolute meaning is baseball’s OPS. A player’s OPS is the sum of their On-base percentage (which is a pretty self-explanatory number) with the Slugging Percentage. The Slugging Percentage (SLG) is the sum of the total bases reached by a runner on a single at bat divided by the total number of at-bats that the batter has. The concept that SLG captures is the hitter’s overall effectiveness and power ^[1].

If you close your eyes and get near to that crevasse where conscientiousness and lucid dreaming meet, you may start to understand what on earth SLG or OBP meant in the minds of their creators. They are, as numbers without a context, meaningless. That said, if you give them either the context of Other Players or the context of Chance We’ll Win This Game their value is unmatched in today’s baseball. An OPS near 1 is fabulous! What does 1 mean? Well… uh… it means we win a lot, and that’s good enough (unless you are my Mets who have great stats yet constantly defy the odds). Sigh, I digress…

Software, like baseball players, is magnificent, often times mysterious, and occasionally questionably worth the sum you paid for it. And to evaluate it, like  baseball players (who’s behavior also often seems peculiar and irrational), you must boil your application down to some simple numbers you can digest.

For instance, if you were to compute a health metric to digest your system’s state, what might you use? Some insights gained from the recent software sprint I was involved in point to more than your typical CPU usage, Memory usage, and network usage. While these numbers are valuable, they do not give you a good idea as to how your system is behaving compared to how it should be behaving. That is to say, if your application uses 90% of the CPU most of the time, then that is normal and acceptable. It is not until you establish the mode water mark that you can start reasoning about what high and low look like.

To begin our application’s instrumentation we first built a restful webpage to pump our statistics out via XML. We also wired up simple serialization of all the available system JMX server named object values. This gives us, essentially for free, the max and current values for memory, garbage cleanup statistics, thread statistics, and a host of other diagnostic bits of information.

In retrospect, we should have wired our application metrics into JMX as well, and perhaps that will be a future task, but for now they reside in series of iBatis calls to the database storing simple double-precision values, their weighted moving averages, their min, max, and current values as well as some time stamping information about how long the interval is between calls to that statistic and how often the statistic has been updated.

Of particular interest is that the weighted moving average consists of only two data points: The current average and the new value. Also stored with the statistic is the weight given to the new value (the value of which should be >= 0 and <= 1). The larger this weight is, the faster the average will move toward the new value. For daily job runs this value should be high. For frequent calls to the database, this value should be low. This causes the average to move quickly for the infrequent calls and slowly for the very frequent calls.

One added bonus, errors and exceptions, regardless of language, should be treated as data to be handled and preferably captured and stored. The statistics engine does provide an exception handling routine to store an exception, though this system needs refinement as we learn more about how we will be using this information in the future.

The goal of all this a dashboard with fields not unlike the following:

• exception count for the last day
• current memory divided by peak memory
• current threads divided by peak threads
• batch job timeout count + batch job completion count = total job count
• user calls per minute

These are all instrumentable values and, at a glance, can tell you some profound things about the general activity of your system. It will tell you something about throughput and something about how close to your hardware limitations you are.

What is even more profound is that once the application holds this information, the application can “learn” and take action when the situation changes. It can throttle user queries to let the database catch up. It can send warning emails. It can stop running jobs. It can signal other clients of the DB or other resources to “back off use” for 10 minutes. These are all very simple reactions to resource strains, but impossible to do intelligently without some very basic measurements.

People come to the internet with an established view on a particular topic. We do not care precisely what the topic is, but suffice it to say, that it is one that the Internet user has some knowledge about, perhaps would like to know more about, and has some preconceived notions about. When they come to the Internet with their question, they punch it into a search engine and get back a few hundred results.

They read the headlines and queue in on the ones that match their preconceptions. They may click on a few that are not in agreement with what they believe is true, but they feel compelled to check them out also. The vast array of disagreeing sites are as a hydra, too numerous, and in this case too varied, for our Internet-searcher to make much sense out of. In the end, they are most likely not persuaded by any of them (mostly because they are all lumped into one category of “opposing views” and the single category of “my view” has some definite, clear, coherent representation).

The result? People find what they already know on the Internet. This is how forums become so mono-thought-esque and this is why people shout-down or ignore opposing views that appear in their specialized forums. This is, of course, a generalization, but a prevalent pattern.
I point this out because it is a re-presentation of a fundamental pair of challenges that the Internet faced when it was just the World Wide Web. Social Media offers some ways to alleviate the problem, but not many.

1. How do you find a trusted voice for your point of view? That is, how can you be sure that you have a valid point of view and have found on the Internet someone who represents it with reason and balanced judgment?
2. Second, very similar to the first, and indeed necessary to satisfy the first point, how can you find balanced, well-reasoned opposition claims to the view point you are searching for? There are simply too many views that do not agree to single out the few that are perhaps actually challenging to your perspective, when considered.

To put it another way, to solve #1 you need #2. To solve #2, currently, you need to read through a lot of information and most people simply do not have time for that. As a result, they tend to form their opinion off-line and validate it online.

The result is online communities that are essentially echo chambers in which opposition perspectives have a very tough time gaining any sort of traction. Is this a bad thing? Well… not really, so long as you don’t think that your online community is some sort of truth-broker.

Social media offers some methods by which you can more easily find valid voices of agreement and opposition by creating continuity of relationships. If you can find one person with well-reasoned arguments for or against your position, chances are that they only dialog with like-quality people. If they disagree online, they most likely will do so at the same level at which they argue for their position. Thus, if you try to find valid opposition, find those who disagree with your initial social network contact on the subject matter.

Spidering along the social web, you should be able to find a much smaller set of points of view with higher quality arguments. The problem them becomes finding only that entry person, something you could perhaps accomplish in the “real” world and take online.

One last thought: Malcom Gladwell rightly points out in Tipping Point that there are different people who serve different roles in societies. He breaks them up into three groups one of which are Mavens and the other of which are Connectors. The Mavens are the subject matter experts whose opinions you want. The Connectors are social butterflies looking to bring together people. If you can find not only a subject matter expert who argues at the level you are looking at, but if you can identify who among his connections is a Connector-type of person, you will have a fabulous initial sub-network of people to read for opinions.