A Basic Study on Spoiler Detection from Review Comments Using Story Documents

Maeda, Kyosuke; Hijikata, Yasuto; Nakamura, Satoshi

doi:10.1109/wi.2016.0098

Cited by 4 publications

(1 citation statement)

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“…The recent studies of spoiler detection mainly focus on two domains, keyword matching, and machine learning methods. Keyword matching methods filter out spoilers based on predefined keywords, such as actor and character names [8], the name of the sports team or sporting event [22], or words in the latter half of the document [20]. However, the keyword matching methods require human-fixed input and are not widely used in various application scenarios.…”

Section: Related Workmentioning

confidence: 99%

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

Yang

Jia²,

Gao

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

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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.

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Section: Related Workmentioning

confidence: 99%