  Nowadays, time-sync comment (TSC), a new form of interactive comments, has
become increasingly popular in Chinese video websites. By posting TSCs, people
can easily express their feelings and exchange their opinions with others when
watching online videos. However, some spoilers appear among the TSCs. These
spoilers reveal crucial plots in videos that ruin people's surprise when they
first watch the video. In this paper, we proposed a novel Similarity-Based
Network with Interactive Variance Attention (SBN-IVA) to classify comments as
spoilers or not. In this framework, we firstly extract textual features of TSCs
through the word-level attentive encoder. We design Similarity-Based Network
(SBN) to acquire neighbor and keyframe similarity according to semantic
similarity and timestamps of TSCs. Then, we implement Interactive Variance
Attention (IVA) to eliminate the impact of noise comments. Finally, we obtain
the likelihood of spoiler based on the difference between the neighbor and
keyframe similarity. Experiments show SBN-IVA is on average 11.2\% higher than
the state-of-the-art method on F1-score in baselines.
