How useful is social feedback for learning to rank YouTube videos?

Orellana-Rodriguez, Claudia
Altıngövde, İsmail Sengör
A vast amount of social feedback expressed via ratings (i.e., likes and dislikes) and comments is available for the multimedia content shared through Web 2.0 platforms. However, the potential of such social features associated with shared content still remains unexplored in the context of information retrieval. In this paper, we first study the social features that are associated with the top-ranked videos retrieved from the YouTube video sharing site for the real user queries. Our analysis considers both raw and derived social features. Next, we investigate the effectiveness of each such feature for video retrieval and the correlation between the features. Finally, we investigate the impact of the social features on the video retrieval effectiveness using state-of-the-art learning to rank approaches. In order to identify the most effective features, we adopt a new feature selection strategy based on the Maximal Marginal Relevance (MMR) method, as well as utilizing an existing strategy. In our experiments, we treat popular and rare queries separately and annotate 4,969 and 4,949 query-video pairs from each query type, respectively. Our findings reveal that incorporating social features is a promising approach for improving the retrieval performance for both types of queries.


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An analysis of the social video sharing platform YouTube and the news aggregator Yahoo! News reveals the presence of vast amounts of community feedback through comments for published videos and news stories, as well as through metaratings for these comments. This article presents an in-depth study of commenting and comment rating behavior on a sample of more than 10 million user comments on YouTube and Yahoo! News. In this study, comment ratings are considered first-class citizens. Their dependencies with t...
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The web provides a suitable media for users to share opinions on various topics, including consumer products, events or news. In most of such content, authors express different opinions on different features (i.e., aspects) of the topic. It is a common practice to express a positive opinion on one aspect and a negative opinion on another aspect within the same post. Conventional sentiment analysis methods do not capture such details, rather an overall sentiment score is generated. In aspect based sentiment ...
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Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences "better." Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users' interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of in...
Can social features help learning to rank YouTube videos?
Chelaru, Sergiu Viorel; Orellana-Rodriguez, Claudia; Altıngövde, İsmail Sengör (2012-11-26)
We investigate the impact of social features (such as likes, dislikes, comments, etc.) on the effectiveness of video retrieval in YouTube video sharing system using state-of-the-art learning to rank approaches and a greedy feature selection algorithm. Our experiments based on a dataset of 3,500 annotated query-video pairs reveal that social features are promising to improve the retrieval performance.
Analyzing Implicit Aspects and Aspect Dependent Sentiment Polarity for Aspect-based Sentiment Analysis on Informal Turkish Texts
Kama, Batuhan; ÖZTÜRK, MURAT; Karagöz, Pınar; Toroslu, İsmail Hakkı; Kalender, Murat (2017-11-09)
The web provides a suitable media for users to post comments on different topics. In most of such content, authors express different opinions on different features or aspects of the topic. In aspect based sentiment analysis, it is analyzed as to for which aspect which opinion is expressed. Once aspects are available, the next important step is to match aspects with correct sentiments. In this work, we investigate enhancements for two cases in matching step: extracting implicit aspects, and sentiment words w...
Citation Formats
S. CHELARU, C. Orellana-Rodriguez, and İ. S. Altıngövde, “How useful is social feedback for learning to rank YouTube videos?,” WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS, pp. 997–1025, 2014, Accessed: 00, 2020. [Online]. Available: