Data Mining: Text Mining, Visualization and Social Media

National Geographic's Egyptian Twitter Graphic

I love getting the physical edition of the National Geographic. I spotted this graphic in a recent copy - it shows some Twitter activity in Egypt (can you spot when the government turned of the intertubes?).

The National Geographic's Twitter account promoted it as an 'awesome' visualization. This made me think a little about what it means to be an awesome visualization - I'm not sure I know.

A Visual Guide to the International Conference on Weblogs and Social Media (ICWSM)

I’m continually impressed by the breadth of content presented at ICWSM. This conference is truly achieving a unique position for social media and social network researchers: bringing together social science and computer science. I thought it would be interesting to capture the conference visually, so below is a selection of the visual representations of data and results from some of the papers from the conference. Each is linked to the online paper page, so click through to learn more!

 

Search and Social Media 2010

Tomorrow I will be attending Search and Social Media (at WSDM) - and am very much looking forward to it. While thinking about the workshop I realised that the title can lead to endless debates around the definition of 'search' and 'social media', so I think it makes a lot of sense to break things down by aspects of the social media space. In particular: search and real time content, search and community, search and influence, authority and popularity, search and geolocated content, search and social networks, etc.

When breaking things down in this manner, we can also reflect on the notion of 'search'. There are many interactions with social media that currently don't follow the traditional search dialogue. TopicFire and Techmeme, for example, are more focused on real time analytics than on search as a primary mode of interaction. Similarly, Bing, Google, Yelp and many others provide a 'what's nearby' interaction which uses context to parameterize content in the spatial dimension, much as TopicFire and Techmeme use time (what's nearby versus what's rightnow).

Zooming in to Twitter

David Lojudice Sobrinho, who blogs here and who pointed me to another magazine cover (on Veja in Brazil), directed me to @mlemos’ Twitter visualizations. I took the liberty of uploading one of them to the new SeaDragon site which hosts (large) images and provides a zoomable image experience.

Observant readers will notice the similarity between my Twitter image and @mlemos’!

The image is published under the creative commons share alike license.

Surcher

Briefly, Surcher is a ‘dashboard to right now’. Largely a user experience over a number of trending/buzz APIs provided by Google, Yahoo!, Microsoft (bing), Twitter, Technorati and CNN, it ranks words and phrases and lets you drill down to learn more about those topics and why they are either ‘hot’ or ‘catching fire’.

Microsoft Brand Independence

How are the Microsoft product brands picked up in the blogosphere? The graph below shows the general use of various brands in conjunction with the company name.

What we see here is a clear ordering between Windows (operating system), Office (productivity), Xbox and Bing (gaming and search) then Zune (media device).

However, if we look at the following chart, which shows Xbox, Bing and Zune without Microsoft being required in the blog post, we can see that Xbox leaves Bing behind, suggesting that it has a clearer brand independence.

Brand independence in the search space will be a key metric for tracking the success of the recent re-launch of Microsoft (my employer's) search product.

Bing does have some amount of brand independence. The graph below shows how the Bing brand is discussed without Microsoft and also how search in general is discussed with Microsoft. The Bing brand has almost the same weight independent of Microsoft as the category of search in general - i.e. the blue and green lines level off around the same place. In addition there is a constant level of discussion about Microsoft and search independent of Bing (the red line) which seems unaffected by the launch of the new product.

 

I'm assuming that some formal metric of brand (recognition) independence exists - I've not yet found a definition.

Social Business Design

Kate Niederhoffer, and her colleagues have posted about her groups move into a space they term Social Business Design. In a sense, what they describe is a reaction to the siloing of thinking around social media brought on by the term ‘media’ itself. This is, perhaps, an extension of my dismissal of the term ‘consumer generated media’ due to its origination in the world of marketing and advertising. Kate and friends are making the point that the social part of the future of online ecologies is not just about ‘media’ per se, but about the entire end-to-end, front-to-back dissemination, assimilation and synthesis of information and its integration with business processes and models.

While this is an interesting direction, it reminds me of terms like ‘democratization’. As anyone in a large organization can attest, capitalism doesn’t scale through democracy. Good companies succeed via Philosopher Kings. The idea that social effects will permeate a large organization without accommodating hierarchy is a little far fetched.

Niederhoffer et al’s thesis centres on four core notions. As Kate puts it:

• Ecosystem - a community of connections
• Hivemind - the socially calibrated mindset of individuals
• Dynamic Signal - the constant multi-faceted means of collaboration
• Metafilter- a method of finding signals in vast amounts of noise

The later two are clearly about information flow. Hivemind (which I’ve always taken to mean the aggregate psychological process of a group, not that of an individual moderated by social context) is also about information diffusion and the assimilation of social cues in moderating that information and republishing.

Perhaps the key to understanding what this topic is about is to understand the scale at which is intended to be implemented. I’m excited to follow where their thinking leads and what impact it will have on reshaping existing businesses and forming new ones.

Raveable: hotel appraisal

Raveable is a new review aggregation site which specializes in hotels. It claims to provide information for 55 thousand hotels in the US culled from more than 35 million reviews. The site mines these reviews to automate the summarization of opinions from guests for the hotels.

The data

Firstly, where does the data come from? Paging through a number of hotel sheets, it initially appeared to me as if most of the data comes from TripAdvisor. The site states that the data comes from a number of sources, including TripAdvisor, Expedia, Travelocity, etc. as well as individual blogs. Browsing the site brought more of these sources into view – in fact, the experience suggests that some hotels get more data from one source than the other. It would be interesting to know the demographics of those sources and to consider a more representative summary taking the demographics into account. Has Raveable formed relationships with those sources?

The UI

In general, I found the UI clean and pleasantly put together. One of the most important aspects of presenting data for a choice like hotel booking is to get some understanding of the ranking of the candidates and the coverage of the system (how many of the competing hotels are in the system). The UI goes some of the way here by providing an overall ranking:

However, it would be nice to get a bit of additional geographic context that shows other hotels near by. In addition, with the added layer of appraisal (summaries of reviews) one could get a picture of the general quality of the hotels in the neighbourhood. Am I looking at a good hotel in a forest of good hotels? A good hotel in a sparse neighbourhood?

The Interesting Part

Of course, the interesting part about this whole thing is the automated summarization and extraction of opinions written in natural language. With 636 reviews available, on average, per hotel, there should be good opportunity to deal with accuracy issues by doing something sensible with summarization and presentation (note that I’ve not yet seen a hotel with > 600 reviews so I’m not sure if all the reviews are assigned to hotels in the system).

My expectations for the extraction of opinions was that the positive stuff would generally be pretty accurate, but negative comments – always harder than positive to recognize – would show some amount of error. Some anecdotal examples (pulled without too much hunting):

• Our room was on the 3rd floor we were offered a room at the back or the front, we chose the front whats the point of being in new york with out the noise. [Casablanca Hotel]
• The rooms are not large but are more than adequate. [Casablanca Hotel]
• You can see the old bank vaults in the basement and the registration area on the first floor is where the tellers used to be. [Drury Plaza Hotel]
• Housekeeping was called and eliminated the two problems. [Camelback Inn]

These false positives are interesting in terms of what they tell us about the algorithms that compute the sentiment. There is some background value associated with certain terms (noise in the first example triggered a negative tag; large in the second and problem in the third). The trouble comes with the valence associated with the terms by the surrounding context.

I don’t feel that these problems are really a weakness in the system. There are always going to be errors. I believe there are two ways to combat this problem in general. The most obvious is figure out a way to introduce another level of summarization in the UX. There is high level summary for each major category. One could try something simple, like a tag cloud, that summarizes within each category in a way that would allow the user to get a finer grained, but still aggregate summary of the content.

Twendz: Twitter and Sentiment

Waggener Edstrom (who do plenty of work for Microsoft) has released a Twitter topic/sentiment tool called Twendz (other coverage here and here). The site shows both a tag cloud of related terms and attempts to surface positive, negative and neutral posts on the topic. Thus far, I’m not too impressed with the sentiment analysis on a per tweet level. Here are some postitive false positives tweets about Jim Cramer:

• The most important video you'll watch this year: Jon Stewart ends Jim Cramer's career Video
• oh wow, John Stewart probably ended Jim Cramer's career last night, it was amazing.
• Please take 21 minutes and 12 seconds to watch this video. http://is.gd/nahG Jim Cramer and John Stewart discuss financial markets.

Here’s some of the negative false positives for Jim Cramer:

• Jim Cramer is not at fault! He'll agree to be blamed, but YOU PEOPLE are the ones investing! Be accountable! (still love J. Stewart, though)

Here’s some negative false positives or watchmen:

• Fair point But that is intensely faithful to the original graphic novel. It's dark, dark socio-political commentary. #watchmen
• back to designer mode and crazy to see Watchmen!
• Saw #Watchmen. Must watch for anyone interested in politics/visual effects. The complex philosophical plot kept me at the edge of my seat.

It is interesting to see this in the context of recent posting about the twittersphere making things easier for sentiment mining. While the system should be doing the right thing with valence shifters (negation, etc.) these examples also demonstrate the problem of topic association. In addition, the examples with Watchmen demonstrate the problems between the nature of the plot and the evaluation of the film. And, of course, like nearly every tool in this space, by providing a direct link to the raw data, the errors get surfaced and the user begins to question the value of the results.

Summary: I’m really excited to see that WE is building tools in this space. However, it looks like they are starting at the shallow end when it comes to sentiment mining.

Is Authority Migrating?

There’s an interesting discussion going on between Brian Solis at TechCrunch and Dorrion Carroll at Technorati. Brian’s thesis is that the number of inlinks to blogs from other blogs is dropping because microblogs and other attention sources are proliferating. Rather than link to something from your blog, you link to it from Twitter, for example.

However, a disruptive trend is already at play. While blogs are increasing in quantity, their authority–as currently measured by Technorati–is collectively losing influence. For instance, just last November, Technorati counted 32,493 links towards gadget blog Engadget’s “authority.” Today, it counts half that amount (16,326). Even TechCrunch’s link authority as measured by Technorati is down by several thousand links, yet its relative position in the overall ranking (No. 3) hasn’t moved.

Dorrion responds by pointing out that it is the quality of Technorati’s analysis that has improved: by removing a lot of spam from their index, their numbers are slimmer and better. Technorati, and now Google, indexed the whole blog page in many cases, not just the post. Consequently, link counting was confused with links intended to be consumed in a different manner (e.g. blogrolling). This too, Dorrion suggests, has changed recently at Technorati.

While Brian may have got it wrong in terms of Technorati’s data, it should be clear that any type of social measurement needs to consider as many sources as possible. In addition, when analytics are being sold, they need to be sold with a deep understanding of the meaning of the different types of data sources and how to interpret them. Is a link from a tweet worth the same as from a blog?