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

Abstract

Nowadays, time-sync comment (TSC), a new form of interactive comments, has become increasingly popular on 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.

Reaction or Speculation: Building Computational Support for Users in Catching-Up Series Based on an Emerging Media Consumption Phenomenon

“…Despite the promising applications of leveraging speculation to support catching-up users, our semi-structured interviews identified a potential problem: the risk of encountering spoilers (described in Section 3.3.3). In this respect, many studies have examined spoiler detection and related interaction techniques [3,20,23,46,67]. For example, Guo et al [23] proposed a method to detect spoiling movie reviews using latent Dirichlet allocation, while Golbeck [20] demonstrated how simple keyword-based filtering using the name of actors or sports players could effectively block live-tweets containing spoilers, although the method's precision was poor.…”

“…For example, Guo et al [23] proposed a method to detect spoiling movie reviews using latent Dirichlet allocation, while Golbeck [20] demonstrated how simple keyword-based filtering using the name of actors or sports players could effectively block live-tweets containing spoilers, although the method's precision was poor. Yang et al [67] proposed a spoiler detection method specifically designed for Danmaku comments that relied on a comment's similarity to comments made during the climax period, i.e., peak volume.…”

Sentiment classification of time-sync comments: A semi-supervised hierarchical deep learning method

“…For video platforms, TSC has brought about a new mechanism for video quality assessment, by which a series of operational strategies can be modified. For example, some unruly viewers may spoil the video content through TSCs, so platforms can use automatic detection technologies to block spoilers and penalize offenders (Yang et al, 2019a). Platforms can encourage and award excellent video creators according to video quality, and the awards can be fine-grained (i.e., awards given to specific clips of a video) with the help of approaches such as highlight detection (Liaw & Dai, 2020).…”

“…The figure shows that, compared with the video comment, each TSC contains insufficient semantic information to understand the sentiment. As a comment on real-time video content, the TSC content is highly correlated to the content of the corresponding video clip (Yang et al, 2019a). In this case, TSCs posted in the same period of time (i.e., neighboring TSCs) comment on similar objects, and thus may contain similar sentiment polarities.…”