"social web" entries
The subtle variables affecting a base metric
This post introduces a series that explores the problem of approximating a Twitter account’s influence. With the ubiquity of social media and its effects on everything from how we shop to how we vote at the polls, it’s critical that we be able to employ reasonably accurate and well-understood measurements for approximating influence from social media signals.
Unlike social networks such as LinkedIn and Facebook in which connections between entities are symmetric and typically correspond to a real world connection, Twitter’s underlying data model is fundamentally predicated upon asymmetric following relationships. Another way of thinking about a following relationship is to consider that it’s little more than a subscription to a feed about some content of interest. In other words, when you follow another Twitter user, you are expressing interest in that other user and are opting-in to whatever content it would like to place in your home timeline. As such, Twitter’s underlying network structure can be interpreted as an interest graph and mined for insights about the relative popularity of one user when compared to another.
Surprising social media stats
I’ve been filtering Twitter’s firehose for tweets about “#Syria” for about the past week in order to accumulate a sizable volume of data about an important current event. As of Friday, I noticed that the tally has surpassed one million tweets, so it seemed to be a good time to apply some techniques from Mining the Social Web and explore the data.
While some of the findings from a preliminary analysis confirm common intuition, others are a bit surprising. The remainder of this post explores the tweets with a cursory analysis addressing the “Who?, What?, Where?, and When?” of what’s in the data.
"Mining the Social Web" author Matthew Russell on the questions and answers social data can handle.
Matthew Russell, author of "Mining the Social Web" and a speaker at the Where 2.0 Conference, discusses the tools and the mindset that can unlock social data's utility.
A new web app applies trend analysis to structured social media.
A new web app put to the test during Australia's recent flooding shows how crowdsourced social intelligence can be integrated into crisis response
The term, “Social Business” has been gaining currency over the past year among influential thinkers such as Stowe Boyd, Peter Kim, Jeff Dachis and Jeremiah Owyang. I am excited to announce that I will be moderating an O’Reilly panel discussion with this group on January 14 to discuss Social Business and how it can impact strategy, design, technology and customer experience. I would love to hear about any questions you would like to see addressed during this upcoming webcast.
As we move from the "web of information" to the "web of people" (aka the Social Web) the output of all of this social participation is massive dossiers on individual behavior (your social network profiles, photos, location, status updates, searches etc.) and social activity. This loss of control over personal information is on a collision course with the law of unintended consequences Amidst this barrage of good news for how much power we wield in the transaction of commerce one has to wonder if we are giving away something quite precious in the bargain.
This is the fifth post for the Twitter Approval Matrix with data that spanned the month of October and different sources such as tweetsentiment.com, scraping archives, and observations. This month I received help from Joe Fernandez the CEO of Klout.com. Joe continues to provide some great 'hard' data that allowed me to better place more items on the grid this month. A quick refresher, the matrix shows four quadrants used to describe trends found on Twitter.
In the circles that I travel the Internet is often breathlessly embraced as the herald of all things good; the bringer of increased choice, personal empowerment, social harmony… and the list goes on. And yet, as with any powerful technology, the truth of its consequences eludes such a singular and happy narrative. More access to information doesn’t bring people together, often it isolates us.
I am releasing my conversation with John Hagel in three segments. In the first segment we discussed the real-time web. Here we discuss the move from the information web to the Social Web. John makes the point that the rise of the Social Web feels “a bit like Back to the Future” for people who have a long history with…
Scott Berkun just posted a great rant titled, Calling Bullshit on Social Media. I suggest everyone read it. Berkun raises good points – and I agree the hype around social media warrants taking a critical look. Despite being in general agreement, there are a few areas I can’t abide, starting with this statement: social media is a stupid term. Is there any anti-social media out there? Of course not. All media, by definition, is social in some way.