  Having a quality annotated corpus is essential especially for applied
research. Despite the recent focus of Web science community on researching
about cyberbullying, the community dose not still have standard benchmarks. In
this paper, we publish first, a quality annotated corpus and second, an
offensive words lexicon capturing different types type of harassment as (i)
sexual harassment, (ii) racial harassment, (iii) appearance-related harassment,
(iv) intellectual harassment, and (v) political harassment.We crawled data from
Twitter using our offensive lexicon. Then relied on the human judge to annotate
the collected tweets w.r.t. the contextual types because using offensive words
is not sufficient to reliably detect harassment. Our corpus consists of 25,000
annotated tweets in five contextual types. We are pleased to share this novel
annotated corpus and the lexicon with the research community. The instruction
to acquire the corpus has been published on the Git repository.
