Team Harry Friberg at SemEval-2019 Task 4: Identifying Hyperpartisan News through Editorially Defined Metatopics

This report describes the starting point for a simple rule based hypothesis testing excercise on identifying hyperpartisan news items carried out by the Harry Friberg team from Gavagai. We used manually crafted \textit{metatopics}, topics which often appear in hyperpartisan texts as rant conduits, together with tonality analysis to identify general characteristics of hyperpartisan news items. While the precision of the resulting effort is less than stellar{---} our contribution ranked 37th of the 42 successfully submitted experiments with overly high recall (95{\%}) and low precision (54{\%}){---}we believe we have a model which allows us to continue exploring the underlying features of what the subgenre of hyperpartisan news items is characterised by.

PDF Abstract
No code implementations yet. Submit your code now


  Add Datasets introduced or used in this paper

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here