- Mapping Twitter Topic Networks (Pew Internet) — Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversation. (via Washington Post)
- yasp — a fully functional web-based assembler development environment, including a real assembler, emulator and debugger. The assembler dialect is a custom which is held very simple so as to keep the learning curve as shallow as possible.
- The 12-Factor App — twelve habits of highly successful web developers, essentially.
- Fast Approximation of Betweenness Centrality through Sampling (PDF) — Betweenness centrality is a fundamental measure in social network analysis, expressing the importance or influence of individual vertices in a network in terms of the fraction of shortest paths that pass through them. Exact computation in large networks is prohibitively expensive and fast approximation algorithms are required in these cases. We present two efficient randomized algorithms for betweenness estimation.
ENTRIES TAGGED "Twitter"
One speaker at Fluent 2013 whose talk was particularly well received was Todd Kloots of Twitter who spoke about HTML5′s pushState API and demonstrated how it was used in Twitter’s Web-based interface.
Some key parts of Todd’s talk include:
- The opportunity Twitter saw in pushState [at 01:45]
- What you had to do with dynamic URLs before pushState [at 02:46]
- A summary of the pushState API [at 06:10]
- Gotchas and browser support [at 07:58]
- How pushState sped up navigation on Twitter.com without re-architecting [at 12:15]
- What Twitter had to do server-side to make progressive enhancement work [at 19:11]
- Final thoughts [at 31:37]
- Q&A [at 32:15]
iOS Pentesting, Twitter's Infrastructure, JS Data Sync, and Chromium as C Runtime
- idb (Github) — a tool to simplify some common tasks for iOS pentesting and research: screenshots, logs, plists/databases/caches, app binary decryption/download, etc. (via ShmooCon)
- Twitter Infrastructure — an interview with Raffi Krikorian, VP of Platform Engineering. Details on SOA, deployment schedule, rollouts, and culture. (via Nelson Minar)
- Chromium is the New C Runtime — using Chrome’s open source core as the standard stack of networking, crash report, testing, logging, strings, encryption, concurrency, etc. libraries for C programming.
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.