Zhenyu Liao
ABSTRACT
Time-Sync Comment (TSC) is a type of crowdsourced user review embedded in online video websites, which provides better real-time user interaction than traditional user comment type. Various TSC-related problems and approaches have been studied to improve user experience by taking advantage of special characteristics of TSCs such as strong time reliance. However, there are three major drawbacks to these TSC researches. First, they did not explicitly show advantage of TSC features over the traditional features in terms of users' experience. Second, the experiments were conducted on some inconsistent TSC datasets crawled from different source, which makes the effectiveness of their methods less convincing. Third, the methods were manually evaluated by a limited number of so-called "experts" in these experiments, so it is hard for other researchers to obtain the data labels and reproduce the results. In order to overcome these drawbacks, this paper aims to explore the usefulness of TSC data for for the improvement of user experience online by exploiting the TSC pattern inside a new dataset. Specifically, we present a larger-scale TSC dataset with four-level structures and rich self-labeled attributes and formally define a group of TSC-related research problems based on this dataset. The problems are solved by adapted state-of-the-art methods and evaluated through crowdsourced labels in the dataset. The result can be regarded as a baseline for further research.
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Index Terms
- TSCSet: A Crowdsourced Time-Sync Comment Dataset for Exploration of User Experience Improvement
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