  Flood of information is produced in a daily basis through the global Internet
usage arising from the on-line interactive communications among users. While
this situation contributes significantly to the quality of human life,
unfortunately it involves enormous dangers, since on-line texts with high
toxicity can cause personal attacks, on-line harassment and bullying behaviors.
This has triggered both industrial and research community in the last few years
while there are several tries to identify an efficient model for on-line toxic
comment prediction. However, these steps are still in their infancy and new
approaches and frameworks are required. On parallel, the data explosion that
appears constantly, makes the construction of new machine learning
computational tools for managing this information, an imperative need.
Thankfully advances in hardware, cloud computing and big data management allow
the development of Deep Learning approaches appearing very promising
performance so far. For text classification in particular the use of
Convolutional Neural Networks (CNN) have recently been proposed approaching
text analytics in a modern manner emphasizing in the structure of words in a
document. In this work, we employ this approach to discover toxic comments in a
large pool of documents provided by a current Kaggle's competition regarding
Wikipedia's talk page edits. To justify this decision we choose to compare CNNs
against the traditional bag-of-words approach for text analysis combined with a
selection of algorithms proven to be very effective in text classification. The
reported results provide enough evidence that CNN enhance toxic comment
classification reinforcing research interest towards this direction.
