# Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments

## TL;DR

This paper introduces a novel method combining similarity-based networks and interactive variance attention to effectively detect spoilers in time-sync comments on online videos, improving accuracy over existing methods.

## Contribution

The paper proposes a new framework, SBN-IVA, that leverages semantic similarity and interactive variance attention for improved spoiler detection in TSCs.

## Key findings

- SBN-IVA outperforms state-of-the-art methods by 11.2% in F1-score.
- The model effectively reduces noise impact in spoiler classification.
- Experimental results validate the robustness of the proposed approach.

## Abstract

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.