  The pervasiveness of offensive language on the social network has caused
adverse effects on society, such as abusive behavior online. It is urgent to
detect offensive language and curb its spread. Existing research shows that
methods with community structure features effectively improve the performance
of offensive language detection. However, the existing models deal with
community structure independently, which seriously affects the effectiveness of
detection models. In this paper, we propose an end-to-end method based on
community structure and text features for offensive language detection
(CT-OLD). Specifically, the community structure features are directly captured
by the graph attention network layer, and the text embeddings are taken from
the last hidden layer of BERT. Attention mechanisms and position encoding are
used to fuse these features. Meanwhile, we add user opinion to the community
structure for representing user features. The user opinion is represented by
user historical behavior information, which outperforms that represented by
text information. Besides the above point, the distribution of users and tweets
is unbalanced in the popular datasets, which limits the generalization ability
of the model. To address this issue, we construct and release a dataset with
reasonable user distribution. Our method outperforms baselines with the F1
score of 89.94%. The results show that the end-to-end model effectively learns
the potential information of community structure and text, and user historical
behavior information is more suitable for user opinion representation.
