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Finding Informative Comments for Video Viewing

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Abstract

Of all the information-sharing methods on the Web, video is a factor with increasing importance and will continue to influence the future Web environment. Various services such as YouTube, Vimeo, and Liveleak are information-sharing platforms that support uploading UGC (user-generated content) to the Web. Users tend to seek related information while or after watching an informative video when they are using these Web services. In this situation, the best way of satisfying information needs of this kind is to find and read the comments on Web services. However, existing services only support sorting by recentness (newest one) or rating (high LIKES score). Consequently, the search for related information is limited unless the users read all the comments. Therefore, we suggest a novel method to find informative comments by considering original content and its relevance. We developed a set of methods composed of measuring informativeness priority, which we define as the level of information provided by online users, classifying the intention of the information posted online, and clustering to eliminate duplicate themes. The first method of measuring informativeness priority calculates the extent to which the comments cover all the topics in the original contents. After the informativeness priority calculation, the second method classifies the intention of information posted in comments. Then, the next method picks the most informative comments by applying clustering methods to eliminate duplicate themes using rules. Experiments based on 20 sampled videos with 1000 comments and analysis of 1861 TED talk videos and 380,619 comments show that the suggested methods can find more informative comments compared to existing methods such as sorting by high LIKES score.

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Notes

  1. View, organize, or delete comments—YouTube Help.

    Available: https://support.google.com/youtube/answer/6000976?hl=en (Date last accessed on 4 Oct 2019).

  2. Weka 3: Data Mining Software in Java, Expectation-Maximization API. Available: http://weka.sourceforge.net/doc.dev/weka/clusterers/EM.html (Date last accessed on 4 Oct 2019).

  3. Language Detection Library for Java. Available: https://code.google.com/archive/p/language-detection/ (Date last accessed on 4 Oct 2019).

  4. Software—The Stanford Natural Language Processing Group. Available: http://nlp.stanford.edu/software/index.shtml (Date last accessed on 4 Oct 2019).

  5. Emoticon Analysis. Available: http://www.datagenetics.com/blog/october52012/index.html (Date last accessed on 4 Oct 2019).

  6. N-grams: based on 520 million word COCA corpus. Available: http://www.ngrams.info/ (Data last accessed on 4 Oct 2019).

  7. Weka 3: Data Mining Software in Java. Available: http://www.cs.waikato.ac.nz/ml/weka (Date last accessed on 4 Oct 2019).

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  1. Knowledge Service Engineering, KAIST, 291 Daehak-ro, Daejeon, South Korea

    Seungwoo Choi

  2. Department of Computer Science, University of South Alabama, 150 Jaguar Drive, Mobile, AL, USA

    Aviv Segev

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  1. Seungwoo Choi
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  2. Aviv Segev
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Correspondence to Aviv Segev.

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Choi, S., Segev, A. Finding Informative Comments for Video Viewing. SN COMPUT. SCI. 1, 47 (2020). https://doi.org/10.1007/s42979-019-0048-2

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