ComMetrics TriageHazard Score

The ComMetrics TriageHazard Score (you are here) is a story-analysis algorithm that assigns a numerical weighting to each blog post  to obtain the score ranging from 0 (worst) through 100 (best).

The ComMetrics TriageHazard is part of what makes up the ComMetrics Footprint (see also ComMetrics Headline Score, ComMetrics Resonance Score, ComMetrics Mnemonic Score, ComMetrics Ripple Score and the ComMetrics InfLink Score).  

1 – screen-and-glean score

Research reports that the time users spend on a Web page follows a Weibull distribution.

Hence, people scan things and decide often within five seconds if they intend to read more or switch over to another page or blog post. Nielsen has proposed that  educated readers may be able to read about 250 words in 1 minute.   If you structure your post well so that people can screen-and-glean a lot of information within about 30 seconds, chances are greater that they will read on than otherwise.

Based on  the literature much might happen within about 5 seconds, 10, 20 and 30.  Of course, if your audience has read about 125 words and finds things still interesting, the chances are they will read on.  Nielsen suggests that in-depth content provides more value in less time for business people than numerous superficial postings.

Using headers, bold text as well as paragraphs helps increase the chances that people continue. Hence, considering that your blog attracts literate users, visitors are able to read about the following number of words:

- Sum A = 1^st 5 seconds on your web site or blog = reading about 20 words,
- Sum B = 10 seconds spent on Web site or blog = reading about 40 words in total,
- Sum C = 20 seconds about 80 words, and
- Sum D = 30 seconds about 125 words.

On the average Web page or blog post, users may read about 20 percent. Therefore, the  first 10 seconds are critical.(see Jakob Nielsen: How little do users read?)

Hence, using bulleted lists and highlighted keywords, chunking the material and using descriptive headings, subheadings as well as hyperlinks helps.

Score:
Sum A – 20 words = [Headline, bold or italic text (each word)/numbers, paragraphs, URLs]
Sum B – 21 – 40 words =   [Headline, bold or italic text (each word)/#, paragraphs, URLs]
Sum B – 41 – 80 words =   [Headline, bold or italic text (each word)/#, paragraphs, URLs]
Sum D – 81 – 125 words = [Headline, bold or italic text (each word)/#, paragraphs, URLs]

PS: A headline counts once, if text is bold and italic it counts once, each paragraph counts and so does each URL.

2 – identifiability score

Research indicates that victim identification tends to generate more aid than any other type of pitch for donations. This might explain why charities employ a poster child, since this helps raise money for a general cause. This builds on the notion that certain stimuli evoke more affect than others.

Of course, showing how a donation can make a difference in a child’s life does help. As does if we show an image with a graphic or we embed a short video in a blog post that illustrates  how a customer’s problem can be solved with one of your products.

- 10 points = 2 to 3  images  (e.g., picture, graphic, or table) or else 2 videos, 1 or 2 images and 1 video
-   7.5 points =  2 images, 1 image and 1 video clip
-   5.0 points = 1 image (e.g., picture, graphic, or table) and/or 1 video clip
-     0 points = other

Selectively use images to reinforce your message, as images often take less time to understand than words.

My experience has been that if there are too many videos and pictures, something gets lost in the story. One reason being that the online reader (i.e. she is scanning the text on a mobile screen and/or laptop) is getting somewhat overwhelmed with too many images and/or videos.  See for instance, ComMetrics weekly review – social media going’s on.

This post has 4 short videos embedded and 2 images.  Based on this and other examples we decided to choose as top score the 2 images and 1 video or just 2 videos. To illustrate this press release has one graphic as an image: Sicherheit und Bedienungsfreundlichkeit sind Top-Kriterien für soziale Netzwerke.  It would probably benefit a great deal and improve the quality of the story if it also had a 1:45 min video embedded with this press release.

How can we score your story

So how will we score a title like this one: Sicherheit und Bedienungsfreundlichkeit sind Top-Kriterien für soziale Netzwerke”

Sum[(A x .30) +  ( B x .275) + (C x .225)  (D x .20)]/4 =
1 + 1.375 + 0 + 0 = 2.375 

In the above story, there is also one graphic, which results in 5 points

We weigh the glean-and-screen score with .60 and the identifiability score with .40

Addordingl, the above results in: [(2.375  x .60) + (5.0 x .4) ] = 3.425

You can download the text analysis here

Another example is the beginning of this blog post which ends up to score as follows:

Sum[(A x .30) +  ( B x .275) + (C x .225)  (D x .20)]/4 =
           (6 x .30) + (9 x .275) + (9 x .225) (13 x .20) =

 The above results in:  2      +   2.475   + 2.025 + 2.6 =  9.1

Since there is no graphic in the above story, it gets a 0 for the Identifiability Score.

You can download the text analysis here

Just to repeat, the numbers we get above are then added with the following weights: Sum[(screen-and-glean score x .60) +  ( identifiability score  x .40)] = ComMetrics TriageHazard  Score which we can then use to rank your blog compared to others.

The z-scores for the above indicators are added up to get an overall z-score.  This information is calculated into an overall score. Click here to find out how we process the raw data.

The actual ComMetrics Mnemonic  Score number is used in the ComMetrics algorithm to help determine the ComMetrics Footprint of the blog, website or other social media effort being benchmarked.

At this point, the overall scores are compared and rescaled using 100 as the top score.

Literature

Chao Liu, Ryen W. White, and Susan Dumais. (2010). Understanding web browsing behaviors through Weibull analysis of dwell time. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval (SIGIR ’10), pp. 379-386. New York: ACM. DOI: 10.1145/1835449.1835513. Retrieved August 23, 2011, from http://doi.acm.org/10.1145/1835449.1835513

Small, Deborah, A., Loewenstein, George, Slovic, Paul (2007). Sympathy and callousness: The impact of deliberative thought on donations to identifiable and statistical victims. Organizational Behavior and Human Decision Processes, 102, pp. 143-153. Retrieved December 23, 2011, from http://www.juststoryit.com/articles?mode=PostView&bmi=778807

Weinreich, Harald, Obendorf, hartmut, Herder, Elco, and Mayer, Matthias. (February, 2008). Not quite the average: An empirical study of web use. ACM Transactions on the Web (TWEB), 2(1). Doi: 10.1145/1326561.1326566. Retrieved December 23, 2011, from http://dl.acm.org/citation.cfm?doid=1835449.1835513

This also benefitted from: Karen Dietz’s guest post that addresses the conceptual challenges of story telling: Terms of the art: What every discipline needs.