CC BY 4.0 UnportedHakimov, SherzodEwerth, RalphMehta, ParthMandl, ThomasMajumder, PrasenjitMitra, Mandar2022-09-012022-09-012021https://oa.tib.eu/renate/handle/123456789/10135http://dx.doi.org/10.34657/9173The detection of offensive, hateful and profane language has become a critical challenge since many users in social networks are exposed to cyberbullying activities on a daily basis. In this paper, we present an analysis of combining different textual features for the detection of hateful or offensive posts on Twitter. We provide a detailed experimental evaluation to understand the impact of each building block in a neural network architecture. The proposed architecture is evaluated on the English Subtask 1A: Identifying Hate, offensive and profane content from the post datasets of HASOC-2021 dataset under the team name TIB-VA. We compared different variants of the contextual word embeddings combined with the character level embeddings and the encoding of collected hate terms.enghttps://creativecommons.org/licenses/by/4.0/004hate speech detectionoffensive language detectionabusive language detectionsocial media miningBookPartKonferenzschrift