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

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.

[1]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[2]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[3]  Jennifer Golbeck.  The twitter mute button: a web filtering challenge , 2012, CHI.

[4]  Qiang Yang,et al.  Crowdsourced time-sync video tagging using temporal and personalized topic modeling , 2014, KDD.

[5]  Richard Socher,et al.  Ask Me Anything: Dynamic Memory Networks for Natural Language Processing , 2015, ICML.

[6]  Weijia Jia,et al.  Herding Effect Based Attention for Personalized Time-Sync Video Recommendation , 2019, 2019 IEEE International Conference on Multimedia and Expo (ICME).

[7]  Jing Wang,et al.  Event Detection on Online Videos Using Crowdsourced Time-Sync Comment , 2016, 2016 7th International Conference on Cloud Computing and Big Data (CCBD).

[8]  Shogo Nishida,et al.  Context-Based Plot Detection from Online Review Comments for Preventing Spoilers , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[9]  Naren Ramakrishnan,et al.  Finding the Storyteller: Automatic Spoiler Tagging using Linguistic Cues , 2010, COLING.

[10]  Yoshinori Hijikata,et al.  A Basic Study on Spoiler Detection from Review Comments Using Story Documents , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[11]  Yongfeng Zhang,et al.  Personalized Key Frame Recommendation , 2017, SIGIR.

[12]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[13]  Weijia Jia,et al.  Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network , 2019, IJCAI.

[14]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[15]  Satoshi Nakamura,et al.  Temporal filtering system to reduce the risk of spoiling a user's enjoyment , 2007, IUI '07.

[16]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[17]  Qing Ping.  Video recommendation using crowdsourced time-sync comments , 2018, RecSys.

[18]  Christopher Joseph Pal,et al.  Describing Videos by Exploiting Temporal Structure , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[19]  Philipp Koehn,et al.  Findings of the 2017 Conference on Machine Translation (WMT17) , 2017, WMT.

[20]  Shogo Nishida,et al.  Sentence-Based Plot Classification for Online Review Comments , 2014, 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[21]  Jaewoo Kang,et al.  A Deep Neural Spoiler Detection Model Using a Genre-Aware Attention Mechanism , 2018, PAKDD.

[22]  Geoffrey E. Hinton,et al.  Grammar as a Foreign Language , 2014, NIPS.

[23]  Yi Zheng,et al.  Reading the Videos: Temporal Labeling for Crowdsourced Time-Sync Videos Based on Semantic Embedding , 2016, AAAI.

[24]  Chenxi Zhang,et al.  TSCSet: A Crowdsourced Time-Sync Comment Dataset for Exploration of User Experience Improvement , 2018, IUI.

[25]  Ashish Vaswani,et al.  Self-Attention with Relative Position Representations , 2018, NAACL.

[26]  Wei Zhao,et al.  Time-Sync Video Tag Extraction Using Semantic Association Graph , 2019, ACM Trans. Knowl. Discov. Data.

[27]  Weijia Jia,et al.  Improving Abstractive Document Summarization with Salient Information Modeling , 2019, ACL.

[28]  Le Wu,et al.  Predicting the Popularity of DanMu-enabled Videos: A Multi-factor View , 2016, DASFAA.

[29]  Chenxi Zhang,et al.  Video Highlight Shot Extraction with Time-Sync Comment , 2015, HOTPOST@MobiHoc.

[30]  Chao Zhang,et al.  Bridging Video Content and Comments: Synchronized Video Description with Temporal Summarization of Crowdsourced Time-Sync Comments , 2017, AAAI.

[31]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[32]  Jason Weston,et al.  End-To-End Memory Networks , 2015, NIPS.

[33]  Wei Liu,et al.  Fine-grained Video Attractiveness Prediction Using Multimodal Deep Learning on a Large Real-world Dataset , 2018, WWW.

[34]  Weijia Jia,et al.  Crowdsourced time-sync video tagging using semantic association graph , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[35]  Hwanjo Yu,et al.  Don't Be Spoiled by Your Friends: Spoiler Detection in TV Program Tweets , 2013, ICWSM.

[36]  Chaomei Chen,et al.  Video Highlights Detection and Summarization with Lag-Calibration based on Concept-Emotion Mapping of Crowdsourced Time-Sync Comments , 2017, NFiS@EMNLP.

[37]  Christopher Joseph Pal,et al.  Video Description Generation Incorporating Spatio-Temporal Features and a Soft-Attention Mechanism , 2015, ArXiv.