With all of the recent political news stories I thought it would be useful to filter out the wheat from the chaff, and what better way to do that than by introducing new tags to refine the semantics of the news? Surely these are as important as H1, Strong and EM tags for news stories.
I propose that all news stories use the following tags:
- Factual
- Conceptual
- Expert Prediction
- Expert Speculation
- Implicit Prediction
- ImplicitAnalysis
For example, the following paragraph
The unemployment rate hit 9.3 last month as factories laid off 40,000 workers. This news is expected to hurt incumbents in the mid term elections. Economist Rollo Tomasi of the University of North Dakota predicted that unemployment would stabilize at 9.4% before falling to 7.2 next year. He also said that weak seasonal demand is to blame for the slump.
There is only one bit of real news (defined as an event) in the above paragraph, namely that factories laid off 40,000 workers last month. Here is how the story would actually look if it were properly marked up with the new tags
<conceptual>The unemployment rate hit 9.3 last month</conceptual> <factual>as factories laid off 40,000 workers</factual>. <ImplicitPrediction><ImplicitAnalysis>This news is expected to hurt incumbents in the mid term elections.</ImplicitAnalysis></ImplicitPrediction> <ExpertPrediction>Economist Rollo Tomasi of the University of North Dakota predicted that unemployment would stabilize at 9.4% before falling to 7.2 next year.</ExpertPrediction> <ExpertSpeculation>He also said that weak seasonal demand is to blame for the slump.</ExpertSpeculation>
With these tags we could filter out all of the non-news (predictions, speculations, analysis) or fine tune the level of detail to our heart’s desire.
What tags have I missed? Thoughts anyone? Please leave feedback in the comments below.
This post originally appeared on the Stronico blog – with the absorption of Stronico into Digital Tool Factory this post has been moved to the Digital Tool Factory blog
