Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments
This addresses the issue of spoilers ruining viewer surprise in online videos, though it is an incremental improvement specific to Chinese video platforms.
The paper tackles the problem of detecting spoilers in time-sync comments on Chinese video websites by proposing a novel Similarity-Based Network with Interactive Variance Attention (SBN-IVA) method, which achieves an average 11.2% higher F1-score than state-of-the-art baselines.
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.