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New Version of ImpactWatch.com Launches


Yesterday we launched a new version of the marketing site for our monitoring platform, ImpactWatch.  The new site includes new branding and copy, a new IW Twitter feed and a redesigned blog.  I would encourage you to check out the blog if you haven’t already.  JW has a great post up that provides a bit of a behind the scenes look at our redesign process and Hannah has a good take on a recent social media study.  Anyway, give the new site a look and sign up for an ImpactWatch demo if you haven’t checked out the product. 

Using Tweets and ImpactWatch Tools to Predict American Idol

Once again, the Internet is abuzz with predictions and theories about who is going to win American Idol.  In the early days of the competition (back when Kelly Clarkson was still a nobody singing karaoke and we only hypothesized that we hated the British without actually knowing it through Simon Cowell), there was much less web traffic about the show.  This season and the previous one, however, it's all the Internet can talk about.

This leads people in finding numerous ways to predict who of the now-6 remaining contestants will be voted off each week.  After all, this is a show that purports that the American public gets to decide who is going to stay and who is going to go.  Polls, blogs, and fansites may all play an important role in deciding the overall victor, much like a modern day political campaign. 

TV Squad, a popular television site, uses polls from various sources as well as their own intuition to predict the next bootee.  Most of the polls incorrectly predicted Syesha Mercado's demise, while the real loser was Kristy Lee Cook.  Obviously, this is not an accurate way to predict the contestant with the lowest votes.  The polls are simply too specific in the sense that only those Internet snoopers that come across them will actually get a chance to vote in the poll.  This does not represent an accurate view of the American public.

DialIdol.com has found a more inventive way to predict the successful contestants.  Their software measures the busy signal of each phone line to determine who is getting the most votes.  They started the program during the previous season, but achieved only moderate success in the predictions.  The company also sells software to enable one person to vote many times for a contestant.  Many sites have reported that the software is now known by the American Idol producers and rarely works anymore.

Tivo also found a creative way to measure the votes.  The company claims that they can predict who is going to be voted off by which minutes of the recorded programs are re-watched.  The theory is that Idol favorites will have their performances re-watched by their adoring public, while soon-to-be eliminees will have fewer views.  Unfortunately, the system seems to not be altogether accurate, since Tivo has incorrectly predicted Mercado two weeks in a row.

Another social media company, BuzzLogic, uses their "influencer blog" ratings to follow the entire competition via their blog.  I was impressed by the fledgling company's efforts at first glance, but upon closer inspection realized that few, if any, of their predictions have been true.  In addition, BuzzLogic gives very little explanation when they are incorrect.  This does, however, bolster my recent opinion that Katie Paine's connection between online activity and offline activity is flawed.  Many ‘influential' bloggers may be writing about certain candidates for American Idol, but that does not necessarily mean that they are voting for them, or voting at all.

I decided to tackle the task of predicting American Idol, ImpactWatch style.  Instead of using news articles, I used Tweet Scan to analyze 90 tweets per remaining contestant, using two separate searches for each.  I searched for each contestant's full name as well as their first name and the phrase "American Idol".  I read and ranked each tweet post as positive, negative, or neutral.

Castro Tweet Example

There are two reasons why I believe this method to be more valid than the other ways that were described above.  First, tweets represent impulses and first impressions, which I assume mirrors the mindset of actual voters.  Secondly, this is the only method that ascribes a positive or negative take on the information.  Polls just rank the favorite, while the Tivo system lacks any real information about why certain parts of the show are re-watched.  BuzzLogic's system has merit, but suffers from the need of personal input by its bloggers to explain anomalies in the amounts of influencer blogs.

Using my ImpactWatch inspired protocol, I found that David Cook and Jason Castro have the highest amount of positive tweets.  Sure enough, after doing some extended research, I found that the two received much praise for their performances last week.  All three females had an identical number of negative tweets (45), but Mercado has the lowest amount of positive tweets at a scant 30.  This is preliminary, but on Wednesday morning, I will post an updated tweet analysis (since Tuesday is when the contestants will perform their new songs).  Voters will most likely be tweeting away while they are waiting to vote.  Let's see if I can accurately predict which Idol will fall.

My current results are summarized below, using a graph created using ImpactWatch.

American Idol Tweets Bar Graph

More on Comcast and Tweets

[This post is cross-posted at the ImpactWatch Blog

To follow up on a recent post concerning Comcast’s effort to answer consumer complaints via Twitter, I used Tweet Scan to search specifically for Comcast posts and research exactly with what we are dealing. A basic one-word search found well over 1000 tweets about Comcast within just the last couple of hours, so I narrowed my focus down to the most recent 300. I read each of them, and categorized them in three different ways.

The first specification was whether the tweet was positive, negative, or neutral, overall. The results are as follows: 26 of the tweets were positive, 86 were neutral, and a majority of 188 were negative. It is a pretty negative environment for Comcast on Twitter right now.


The second category dealt with what category of complaint or praise under which the tweet fell. There were four distinctions: Not Working, Slow, Prices, and Company. “Not Working” and “Slow” deal with complaints about the Internet and cable service. “Prices” concern any complaints or praise about cost or billing issues. “Company” refers to any mention of the company that does not fall into one of those categories, or short tweets with little information (i.e. “grrr…Comcast”). 178 were about the company itself, 66 were problems with the Internet or cable completely not working, 33 were about slowdown, and 22 were about pricing concerns. It is interesting that on Twitter there is a lot of general venting about Comcast (bad for the brand), and less specific complaints.


The final category is whether or not the tweet contains cursing of any sort. From a quick skim of the 300 tweets, it seems like this is a good indicator of the level of frustration by the writer of the tweet. 35 contained curse words, and 265 did not.


Found below are some examples of Comcast-related tweets, as well as a document containing all the graphs above. This post is similar to the kind of analysis we perform through out service ImpactWatch. Interesting to note is that several of the tweets among the 300 were by the same user, who claims to be a representative from Comcast. Also, many of the tweets contained links to articles referencing the recent customer service use of Twitter by Comcast. Unfortunately, the representative could only handle one consumer problem at a time, so the use of tweets was just as effective as phone consumer services. The links below represent the tweet-by-tweet written data, some examples of Comcast-related tweets, and analytics.

All Data Collected

Example Tweets

Graphs Made in IW

Comcast and Twitter

Over the weekend, two of the users I follow on Twitter, David All and Techcrunch (Michael Arrington), had separate problems with Comcast and vented about them via their Twitter accounts. Comcast apparently monitors Twitter and proactively reached out to both of them.

Here is the relevant tweet from Techrunch:


And here is the tweet from David:


An article in the Consumerist confirms that other users have received responses after complaining via Twitter. In a follow up article about his problems, Michael Arrington offers advice to folks with a Comcast service problem: “Skip the hold time on their customer service line and go on the attack at Twitter instead. You may find your problem fixed in a hurry.”

Three thoughts on this:

(1) I think it is great that Comcast is listening to people on Twitter and reacting proactively to fix problems. Based on a quick search, there appear to be plenty of problems to that need addressing. More companies should monitor and participate in Twitter in a meaningful way (we are working on doing Twitter tracking through our ImpactWatch service).

(2) As a consumer, I’m bothered by the precedent of the squeaky wheels on Twitter getting preferential treatment over people who go through normal channels.

(3) Not speaking specifically about Comcast, I think the focus some companies place on social media is more about PR/crisis management than a true commitment to customer service and dialogue. Performing triage on complaints that come in through Twitter may keep the customer revolt at bay for a short time, but when that levee eventually breaks, it isn’t going to be pretty.

Reuters Wants to Play Tag

ReadWriteWeb's Alex Iskold blogged last week about how Reuters is trying to help organize all on the Internet — including its own content — by launching its API — Open Calais.

As complex as programming and APIs can get, the idea behind Open Calais is rather simple.  Reuters simply wants to find a better way to easily identify important bits of information in an article or on a web page.  For instance, a news article has the who, where, when, where, why, and how in it; thus, finding a way to pull out the people, organizations, location, etc. in an article would help file the article in vast sea of information.

Hopefully, this will make it a lot easier for people to find articles about events that occurred at a specific time, involved an individual or organization, took place in a certain city, was caused by a specific issue, etc.  Needless to say, anyone who deals with information — which should include virtually all of us — will benefit from more enhanced technology to sort through the large amounts of information that we literally have at our finger tips through the Internet.

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