Using Sentiment Information for Preemptive Detection of Toxic Comments in Online Conversations
Table IV: Number of positive and negative predictive features per message before the reported message.
['Mess', '10', '9', '8', '7', '6', '5', '4', '3', '2', '1']
Next, we considered the question of which messages in the conversation contain the most predictive features. To this end, we considered the 26 (10%) most predictive positive and negative features, and grouped them per message. However, while health predictors are distributed evenly in the conversation, toxicity predictors are concentrated in the final three messages. This indicates that a healthy conversation is an ongoing process, but a few bad messages can very quickly turn the tides of the conversation and lead to toxic messages being posted. This also indicates a limit to preemptive moderation: long-term predictions are not valid, and one must focus on clues in the latest messages. To confirm this, we ran the experiment again using only 3 messages before the reported message instead of 10. The results are almost identical to before: the logistic regression classification has an accuracy of 57.7% with sentiment and 51.7% without, while the RNN has an accuracy of 61.8% with sentiment and 60.9% without. It seems clear, then, that the previous seven messages did not contribute significantly to the prediction accuracy.