The growth in social Web platforms in the past years has brought an increase in displays of online hate speech. This subject is considered as a critical matter in the Web community, since it can be related to potentially dangerous actions that affect individuals and groups in the physical world. The automatic detection of this type of expressions has been the center of several investigations over the past few years. However, most research on this subject has been done for the English language and on rather limited datasets. In addition, although some works approach the problem from a multilingual perspective, analyzing different language separately, across-lingual perspective of this problem has not been used so far. The main research proposal of this thesis is to characterize hate speech and other forms of online harassment from different perspectives and use this characterizations to create novel models for online hate speech detection across different languages and domains.