Taming The Social Media Firehose, Part III – Tumblr

In part I, I discussed high-level attributes of the social media firehose. In Part II , I examined a single event by looking at activities from four firehoses for the earthquake in Mexico earlier this year. In Part III, I wrap up this series with some guidelines for using unique rich content from social media firehoses that may be less familiar. To keep it real, I used examples from the Tumblr firehose.

Since the Twitter APIs and firehoses have been available for years, you may be very familiar with many analysis strategies you can apply to the Twitter data and metadata.  I illustrated a couple of very simple ideas in the last post. With Twitter data and metadata, the opportunities to understand tweets in the context of time, timezone, geolocation, language, social graph, etc. are as big as your imagination.

Due to the popularity of blogging for both personal and corporate communication, many of you will also understand some of the opportunities of the WordPress firehose.  With the addition of firehoses of comments, you have the capabilities of connecting threads of conversation to realize another possible analysis strategy. “Likes” and Disqus “votes” provide additional hints about user reaction and engagement–yet another way to filter and understand posts and comments.

Why go to the effort and expense of adding a new firehose?
There are three benefits from investing your efforts in learning to integrate these differences. Users of social networks choose to participate in Twitter, Tumblr or other social networks based on their affinities and preferences. Integrating additional active social media sources gives:

  1. Richer audience demographics
  2. More diverse perspective and preference
  3. Broader topic coverage.

Here’s an example.

Tumblr

The newest firehose from Gnip became available earlier in 2012. Tumblr’s exciting because the unique, rich content from Tumblr provides a complementary perspective and a distinct form of conversation. Tumblr is important because of the unique audience and modes of interaction common within this audience and platform.

With a firehose of over 50 million new posts a day from web users, Tumblr is a source with strong social sharing features and an active network of users where discussions can reach a large audience quickly.  Some Tumblr posts have been reblogged more than a million times and stories regularly travel to thousands of readers in a couple of days.

Before jumping into consuming the Tumblr firehose in the next section, it may help to understand some of what makes it different and valuable. These questions provide a useful framework when approaching any unfamiliar stream of social data.

What is unique about the Tumblr firehose?

1. Demographics. The user community on Tumblr skews young, over-indexing strongly in the 18-24 demographic of trend setters and cool hunters.

2. Communication and Activity Style. As you are thinking about filtering and mining the Tumblr firehose, realize conversations on Tumblr are often quite different from what you’ll find on other social platforms. As you start to interpret the data from Tumblr it’s important to note that Tumblr has an inside language. For example, many sites contain f**kyeah___ in their name and URL. When you start to hone in on your topic, you will need to understand the inside language used for both positive and negative responses. Terms you consider negative on one platform may have positive connotations on another. Be sure to review a subset of your data to get a feel for the nuances before drawing larger conclusions.

3. Rich Content. Content is rich in that there many types of media and a wide range of depth. Users will post audio, video, animated gifs, simple photos as well as short and long text posts.

You’ll also see 7 different Post Types on Tumblr. These represent the different types of content that users can post on Tumblr. They break out as follows:

Table of Post Types on Tumblr

Table 1 – Tumblr post type breakdown.

To answer the questions, we often rely on filters based on text since these are the simplest filters to think about and create.  The textual data and metadata available in the Tumblr firehose include titles, tags and image captions in addition to the text of the body of the post. Including all of this content allows us to filter approximately 20% of the Tumblr firehose based on text. Additional strategies include looking at reblog and “like” activity, as well as reblog and “like” relationships between users.  More sophisticated strategies such as applying character or object recognition to images open up the tens of millions of activities daily for mining and exploration.

4. Rich Topics. In addition to diverse content forms, Tumblr has attracted many active conversations on a wide variety of topics. This content is often very complementary to other social media platforms due to differences in audience, tone, volume or perspective. With more than 20 billion total posts to date, there is content for about almost  anything you can imagine.  Some examples include:

  • Brands. Any brand you can think of is being discussed right now on Tumblr. Big brands with an official presence on Tumblr include Coca-Cola, Nike, IBM, Target, Urban Outfitters, Puma, Huggies, Lufthansa, Mac Cosmetics and many more. NPR and the President of the United States have their own presences on Tumblr.
  • Fashion and Cosmetics. Because of the visual nature of the medium and cool-hunting audience it attracts, there is a large volume of content related to cosmetics and fashion.
  • Music and Movies. With Spotify music plugins and easy upload and sharing of visual content, pop culture plays a big role in the interests and attention of many of the active users on Tumblr. Information, analysis and fan content is rich, creative and travels through the community rapidly.

5. Reblogs and Likes. Tumblr is all about engagement! The primary user activities for interactions are Reblogs and Likes. Some entries are reblogged thousands of time in a day or two. When a user reblogs a post, it places the other user’s post into your blog with any changes they make. There is a list of all of the notes (likes, reblogs) associated with a post appended to that post wherever it shows up on Tumblr. Each post activity record in the firehose can contain reblog info. It will have a count, a link to the blog this entry was a reblog of and a link to the root entry. To build the blog note list that a user would see at the bottom of a liked or reblogged entry, you have to trace each entry in the stream (i.e. keep a history or know what you want to watch) or scrape the notes section of a page.

Filtering and Mining The Tumblr Firehose

Volume. There are a number of metrics we can use to talk about the volume of the Tumblr firehose. The three gating resources that we run up against most often are related to the network (bandwidth and latency) and storage (e.g. disk space). Tumblr activities are delivered compressed, so for estimating, the bandwidth and disk space requirements can be based on the same numbers. The Tumblr firehose averages about 900 MB/hour compressed volume during peak hours, falling to a minimum of 300 MB/hour during slower periods of the day.

To store the firehose on disk, plan on ~16 GB/day based on current volumes. Planning for bandwidth, you want headroom of 2-5 x average peak hourly bandwidth (4 to 10 Mbps) depending on your tolerance for disconnects during peak events.

The other consideration is end-to-end network latency as discussed in Consuming the Firehose, Part II.  Very simplistically, latency can limit the throughput of your network (regardless of bandwidth) by using up too much time negotiating connections and acknowledging packets. (For a detailed calculation, see, for example, The TCP Window, Latency, and the Bandwidth Delay Product.)  The theoretical limit for 20 Mbps throughput is 50-70 ms (depends on TCP window size), but practically you will want to reliably observe less than this (< 50 ms) to realize reliable network performance.

Metadata. A firehose is a time-ordered, near real-time stream of user activities. While this structure is clearly powerful for identifying emerging trends around brands or news stories, the time-ordered stream is not the optimal structure for looking at other things like the structure social networks to discover, e.g., influencers. Fortunately, the Tumblr firehose activities contain a lot of helpful metadata about place, time, and social network to get answers to these questions.
Each activity has a post objectType as discussed above as well as links to resources referred to in the post such as image files, video files and audio files. Each activity has a source link that takes you back to the original post on Tumblr. If the post is a re-blog, it will also have records like the JSON example below, describing the number of reblogs, the root blog and blog this post reblogged.

"tumblrRebloggedFrom" :
    {
         "author" :
         {
               "displayName" : "A Glimpse",
               "link" : "http://onlybutaglimpse.tumblr.com/"
         },
         "link" : "http://onlybutaglimpse.tumblr.com/post/24141204872"
    },
"tumblrRebloggedRoot" :
    {
         "author" :
         {
                "displayName" : "Armed With A Mind",
                "link" : "http://lizard-skin.tumblr.com/"
         },
         "link" : "http://lizard-skin.tumblr.com/post/16004808098/the-nautilus-car-from-the-league-of-extraordinary"
    },

To assemble the entire reblog chain, you must connect the reblog activities within the firehose using this metadata.

Additional engagement metadata is available in the form of likes (hearts in the Tumblr interface) in a separate Tumblr engagement firehose.

Tumblr Likes Metadata

Non-Text Based Filters. Not all non-text post types have enough textual context (captions, title and tags) to identify a topic or analyze sentiment through simple text filtering. You will want to develop strategies for dealing with some ambiguity around the meaning of posts with very little text content. This ambiguity can be reduced unless you have audio or image analysis capabilities (e.g. OCR or audio transcription). Approximately 20% of all posts can be filtered effectively with text-based filtering of text, URL text, tags and captions–about 15M activities per day).

Memes. Another consideration related to the Tumblr language is that official brand sites as well as many bloggers tend to promote a style or overall image more than providing a catalog of particular products. As a result, e.g., you will match the brand name with a lot of cool stuff, but may see specific product names and descriptions much less frequently. There are many memes within Tumblr that will lead you to influencers and sentiment, but looking at “catalog” terms won’t be the most effective path.

I hope I have uncovered some of the mysteries of successfully consuming social media firehoses.  I have only suggested a handful of questions one might try to answer with the social media data. The community of professionals providing text analysis, image analysis, machine learning for prediction, classification and recommendation, and many other wonders is continuing to invent and refine ways to model and predict real-world behavior based on billions of social media interactions.  The start of this process is always a great question.  Best of luck (and the benefits of all of Gnip’s experience and technology) to you as you jump into consuming the social media firehose.

Full Series:

Taming The Social Media Firehose, Part I – High-level attributes of a firehose

Taming The Social Media Firehose, Part II – Looking at a single event through four firehoses

Taming The Social Media Firehose, Part III – Tumblr