In order to provide directions for future work, we analyze the errors made by the classifier trained on the extended features on the four prediction tasks.

Errors on Intention (I) prediction: The lack of background is a major problem when identifying trolling comments. Non-cursing aggressions and insults This is a challenging problem, since the majority of abusive and insulting comments rely on profanity and swearing.  Another source of error is the presence of controversial topic words such as “black”,“feminism”, “killing”, “racism”, “brown”, etc. that are commonly used by trolls. Errors on Disclosure (D) prediction: A major source of error that affects disclosure is the shallow meaning representation obtained from the BOW model even when augmented with the distributional features given by the glove vectors. Errors on Interpretation (R) prediction: it is a common practice from many users to directly ask the suspected troll if he/she is trolling or not.  Errors on Response Strategy (B) prediction: In some cases there is a blurry line between “Frustrate” and “Neutralize”.  Another challenging problem is the distinction between the classes “Troll” and “Engage”.  
 Question: What is an example of a difficult-to-classify case?
The lack of background Non-cursing aggressions and insults the presence of controversial topic words   shallow meaning representation directly ask the suspected troll if he/she is trolling or not a blurry line between “Frustrate” and “Neutralize” distinction between the classes “Troll” and “Engage”