ENTRIES TAGGED "Twitter"

Investigating the Twitter Interest Graph

Why Is Twitter All the Rage?

I’m presenting a short webcast entitled Why Twitter Is All the Rage: A Data Miner’s Perspective that is loosely adapted from material that appears early in Mining the Social Web (2nd Ed). I wanted to share out the content that inspired the topic. The remainder of this post is a slightly abridged reproduction of a section that appears early in Chapter 1. If you enjoy it, you can download all of Chapter 1 as a free PDF to learn more about mining Twitter data.
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Writing Paranoid Code

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.

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Four short links: 3 October 2013

Four short links: 3 October 2013

USB in Cars, Capture Presentations, Amazon Redshift, and Polytweeting

  1. Hyundia Replacing Cigarette Lighters with USB Ports (Quartz) — sign of the times. (via Julie Starr)
  2. Freeseerfree, open source, cross-platform application that captures or streams your desktop—designed for capturing presentations. Would you like freedom with your screencast?
  3. Amazon Redshift: What You Need to Know — good write-up of experience using Amazon’s column database.
  4. GroupTweetAllow any number of contributors to Tweet from a group account safely and securely. (via Jenny Magiera)
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Computing Twitter Influence, Part 1: Arriving at a Base Metric

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.
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Pattern-detection and Twitter’s Streaming API

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.

streaming api vs firehose

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Four short links: 31 May 2013

Four short links: 31 May 2013

  1. Modeling Users’ Activity on Twitter Networks: Validation of Dunbar’s Number (PLoSone) — In this paper we analyze a dataset of Twitter conversations collected across six months involving 1.7 million individuals and test the theoretical cognitive limit on the number of stable social relationships known as Dunbar’s number. We find that the data are in agreement with Dunbar’s result; users can entertain a maximum of 100–200 stable relationships. Thus, the ‘economy of attention’ is limited in the online world by cognitive and biological constraints as predicted by Dunbar’s theory. We propose a simple model for users’ behavior that includes finite priority queuing and time resources that reproduces the observed social behavior.
  2. Mary Meeker’s Internet Trends (Slideshare) — check out slide 24, ~2x month-on-month growth for MyFitnessPal’s number of API calls, which Meeker users as a proxy for “fitness data on mobile + wearable devices”.
  3. What I Learned as an Oompa Loompa (Elaine Wherry) — working in a chocolate factory, learning the differences and overlaps between a web startup and an more traditional physical goods business. It’s so much easier to build a sustainable organization around a simple revenue model. There are no tensions between ad partners, distribution sites, engineering, and sales teams. There are fewer points of failure. Instead, everyone is aligned towards a simple goal: make something people want.
  4. Augmented Reality Futures (Quartz) — wrap-up of tech in the works and coming. Instruction is the bit that interests me, scaffolding our lives: While it isn’t on the market yet, Inglobe Technologies just previewed an augmented reality app that tracks and virtually labels the components of a car engine in real time. That would make popping the hood of your car on the side of the road much less scary. The app claims to simplify tasks like checking oil and topping up coolant fluid, even for novice mechanics.
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These are the top 20 investors to follow on Twitter? Really?

Finding the right people to follow for investment advice has very little to do with the extent of their social media following.

Business Insider really jumped the shark with their recent post entitled These Are The Top 20 Tech Investors You Should Follow On Twitter. It was clearly linkbait for social media rather than real advice for those looking for investment wisdom.  Ashton Kutcher (@aplusk) as the top investor to follow on Twitter?  Really?  When the greatest investor of all time, Warren Buffett (@WarrenBuffett), is also on Twitter?  Sure, Warren is new to Twitter and has only posted one link (to a fascinating article about why women are key to America’s prosperity), but when millions of investors hang on his every word, you’d think he’d get a mention. Ashton is great, but is he a better investor to pay attention to just because he has more “social media pull”?

This kind of story illustrates the vapidity of so much social media reporting.  What does someone’s social media following have to do with whether or not they are worth following for investment advice?

I’d prefer to follow investors who are good investors and who share their investment strategy!  That’s why I’d probably put Fred Wilson (@FredWilson) of Union Square Ventures (who was at an inexplicable number 19 on the Business Insider List) and his partners Brad Burnham (@BradUSV) and Alfred Wenger (@AlbertWenger) at the top.  Not only are they among the most successful tech investors active today (Twitter, Tumblr, Zynga, Foursquare, Etsy, Kickstarter, to name only a few of their investments), but they clearly explain their rationale for investing, their criteria, and their interests. Read more…

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Four short links: 28 May 2013

Four short links: 28 May 2013

Geeky Primer, Visible CSS, Remote Working, and Raspberry Pi Sentiment Server

  1. My Little Geek — children’s primer with a geeky bent. A is for Android, B is for Binary, C is for Caffeine …. They have a Kickstarter for two sequels: numbers and shapes.
  2. Visible CSS RulesEnter a url to see how the css rules interact with that page.
  3. How to Work Remotely — none of this is rocket science, it’s all true and things we had to learn the hard way.
  4. Raspberry Pi Twitter Sentiment Server — step-by-step guide, and github repo for the lazy. (via Jason Bell)
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Four short links: 25 April 2013

Four short links: 25 April 2013

iOS Package Manager, Designed Satire, API Fragility, and Retweeting WWI

  1. Alcatraz — package manager for iOS. (via Hacker News)
  2. Scarfolk Council — clever satire, the concept being a UK town stuck in 1979. Tupperware urns, “put old people down at birth”. The 1979 look is gorgeous. (via BoingBoing)
  3. Stop Designing Fragile Web APIsIt is possible to design your API in a manner that reduces its fragility and increases its resilience to change. The key is to design your API around its intent. In the SOA world, this is also referred to as business-orientation.
  4. @life100yearsago (Twitter) — account that tweets out fragments of New Zealand journals and newspapers and similar historic documents, as part of celebrating the surprising and the commonplace during WWI. My favourite so far: “Wizard” stones aeroplane. (via NDF)
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