- Hyundia Replacing Cigarette Lighters with USB Ports (Quartz) — sign of the times. (via Julie Starr)
- Freeseer — free, open source, cross-platform application that captures or streams your desktop—designed for capturing presentations. Would you like freedom with your screencast?
- Amazon Redshift: What You Need to Know — good write-up of experience using Amazon’s column database.
- GroupTweet — Allow any number of contributors to Tweet from a group account safely and securely. (via Jenny Magiera)
Twitter isn't quite beyond jumping the shark, but it has taken a big step backward.
While I’ve been skeptical of Twitter’s direction ever since they decided they no longer cared about the developer ecosystem they created, I have to admit that I was impressed by the speed at which they rolled back an unfortunate change to their “blocking” feature. Yesterday afternoon, Twitter announced that when you block a user, that user would not be unsubscribed to your tweets. And sometime last night, they reversed that change.
I admit, I was surprised by the immediate outraged response to the change, which was immediately visible on my Twitter feed. I don’t block many people on Twitter — mostly spammers, and I don’t think spammers are interested in reading my tweets, anyway. So, my first reaction was that it wasn’t a big deal. But as I read the comments, I realized that it was a big deal: people complaining of online harassment, trolls driving away their followers, and more.
So yes, this was a big deal. And I’m very glad that Twitter has set things right. In the past years, Twitter has seemed to me to be jumping the shark in small steps, rather than a single big leap. If you think about it, this is how it always happens. You don’t suddenly wake up and find you’ve become the evil empire; it’s a death of a thousand cuts. Read more…
Finding ways to make media interact with the physical world
Reporters, editors and designers are looking for new ways to interact with readers and with the physical world–drawing data in through sensors and expressing it through new immersive formats.
In this episode of the Radar podcast, recorded at News Foo Camp in Phoenix on November 10, Jenn and I talk with three people who are working on new modes of interaction:
- Mark Trammell, of Sonos, previously of Obama HQ and Twitter
- Rebekah Monson, of the University of Miami
- Robert Hernandez, of the University of Southern California’s Annenberg School
Along the way:
- SensorSub, a project Rebekah is working on that uses data-gathering submarines to measure water quality
- Robert’s students are working on augmented-reality projects at the Los Angeles Central Library–building story-time experiences for children and interpreting the library’s famous murals
- How a lengthier sign-up process brought more people onto Twitter
- Mark’s efforts to understand user interaction on physical hardware
- Wise Kaplan and Cranky Kaplan, the fictional Twitter alter-egos of former New York Observer editor Peter Kaplan, who passed away shortly after we recorded this episode. In the same vein: Mayor Emanuel, Twitter satire from Dan Sinker that was subsequently anthologized.
- Snow Fall
For more on the intersection of software and the physical world, be sure to check out Solid, O’Reilly’s new conference program about the collision of real and virtual.
Twitter could be so much better than an advertising company
We can now gather from Twitter’s IPO that it’s fundamentally postured as an advertising company, but its real value isn’t in advertising. Twitter’s most fundamental value rests squarely within data analytics. However, just because Twitter could make a lot of money in advertising doesn’t mean that advertising is where it should concentrate the majority of your efforts or where its most fundamental value proposition lies.
More specifically, Twitter’s most fundamental value is in the overall collective intelligence of its user base when interpreted as an interest graph. Think of an interest graph as a mapping of people to their interests. In other words, if you follow an account on Twitter, what you’re really saying is that you’re interested in that account. Even though there’s lots to be gleaned in all of the little 140 character quips associated with a particular account, there’s a good bit you can tell about a person by solely examining the accounts that the person follows.
Computing Twitter Influence, Part 2
In the previous post of this series, we aspired to compute the influence of a Twitter account and explored some relevant variables to arriving at a base metric. This post continues the conversation by presenting some sample code for making “reliable” requests to Twitter’s API to facilitate the data collection process.
Given a Twitter screen name, it’s (theoretically) quite simple to get all of the account profiles that follow the screen name. Perhaps the most economical route is to use the GET /followers/ids API to request all of the follower IDs in batches of 5,000 per response, followed by the GET /users/lookup API to retrieve full account profiles for up to Y of those IDs in batches of 100 per response. Thus, if an account has X followers, you’d need to anticipate making ceiling(X/5000) API calls to GET /followers/ids and ceiling(X/100) API calls toGET /users/lookup. Although most Twitter accounts may not have enough followers that the total number of requests to each API resource presents rate-limiting problems, you can rest assured that the most popular accounts will trigger rate-limiting enforcements that manifest as an HTTP error in RESTful APIs.
USB in Cars, Capture Presentations, Amazon Redshift, and Polytweeting
In some key use cases a random sample of tweets can capture important patterns and trends
Researchers and companies who need social media data frequently turn to Twitter’s API to access a random sample of tweets. Those who can afford to pay (or have been granted access) use the more comprehensive feed (the firehose) available through a group of certified data resellers. Does the random sample of tweets allow you to capture important patterns and trends? I recently came across two papers that shed light on this question.
Systematic comparison of the Streaming API and the Firehose
A recent paper from ASU and CMU compared data from the streaming API and the firehose, and found mixed results. Let me highlight two cases addressed in the paper: identifying popular hashtags and influential users.
Of interest to many users is the list of top hashtags. Can one identify the “top n” hastags using data made available throughthe streaming API? The graph below is a comparison of the streaming API to the firehose: n (as in “top n” hashtags) vs. correlation (Kendall’s Tau). The researchers found that the streaming API provides a good list of hashtags when n is large, but is misleading for small n.