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

Chelaru, Sergiu; Orellana-Rodriguez, Claudia; Altıngövde, İsmail Sengör

doi:10.1007/s11280-013-0258-9

Cited by 28 publications

(27 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, using classification techniques a study by Siersdorfer et al shows that community feedback on already rated comments can help to filter and predict ratings for possibly useful and unrated comments [4]. Using the state-of-the-art in learning to rank approaches, the user interactions or "social features" were shown to be a promising approach for improving the video retrieval performance in the work introduced by [5]. For improving video categorisation, a text-based approach was conducted to assign relevant categories to videos, where the users' comments among all the other features gave significant results for predicting video categorisation [6].…”

Section: Related Workmentioning

confidence: 99%

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

Abolkasim

Lau

Dimitrova

2016

Adaptive and Adaptable Learning

View full text Add to dashboard Cite

ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. Abstract. This paper presents a computational model for measuring diversity in terms of variety, balance and disparity. This model is informed by the Stirling's framework for understanding diversity from social science and underpinned by semantic techniques from computer science. A case study in learning is used to illustrate the application of the model. It is driven by the desire to broaden learners' perspectives in an increasingly diverse and inclusive society. For example, interpreting body language in a job interview may be influenced by the different background of observers. With the explosion of digital objects on social platforms, selecting the appropriate ones for learning can be challenging and time consuming. The case study uses over 2000 annotated comments from 51 YouTube videos on job interviews. Diversity indicators are produced based on the comments for each video, which in turn facilitate the ranking of the videos according to the degree of diversity in the comments for the selected domain.

show abstract

Section: Related Workmentioning

confidence: 99%

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

Abolkasim

Lau

Dimitrova

2016

Adaptive and Adaptable Learning

View full text Add to dashboard Cite

show abstract

“…Some works focus on analyzing different statistical patterns of UGC (user generated content) such as YouTube videos [13], or on how to improve IR effectiveness by exploiting these UGC, particularly users' actions, with their underlying social network [7,11,12,22,23,25].…”

Section: Time-independent Signals Approachesmentioning

confidence: 99%

“…In the last years, some works have concentrated on studying the richness and the possible use of these user-generated characteristics in search. Chelaru et al [11,12] studied the impact of social signals (like, dislike, comment, etc) on the effectiveness of search on YouTube. They showed that, although the basic criteria using the similarity of query with video title and annotations are effective for video search, social criteria are also useful and have improved the ranking of search results for 48% queries.…”

Section: Time-independent Signals Approachesmentioning

confidence: 99%

“…However, unlike previous work [3,7,11,12,19,23,25], that attempt to improve a search on specific social networks (e.g. YouTube, Twitter) by exploiting their own signals, our work specifically focuses on, firstly, exploiting various signals from different sources as document priors to enhance Web IR.…”

Section: Time-aware Social Signals Approachesmentioning

confidence: 99%

“…Most of existing approaches [11,12,22,25] exploit these signals to estimate the document prior by simply considering the quantity (the number) of signals related to a resource, or the polarity for some specific signals. In this paper, we hypothesize that the time (age) of signals (when they occur), signals diversity on a resource, and the resource age may affect on the prior estimation.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fresh and Diverse Social Signals

Badache

2017

Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval

View full text Add to dashboard Cite

show abstract

Evaluating YouTube videos on sialendoscopy as an educational resource for patients

Campbell

Xiao

Mastrolonardo

et al. 2022

Laryngoscope Investig Oto

View full text Add to dashboard Cite

Objectives: Patients are increasingly relying on YouTube for health information. We objectively evaluated the quality and comprehensiveness of sialendoscopy YouTube videos available to patients. We further investigated the relationship between video content and video popularity. Methods:We identified 150 videos using the search term "sialendoscopy." Videos were excluded if they were lectures for medical professionals, operating room (OR) recordings, unrelated, non-English, or non-audio. Video quality and comprehensiveness were evaluated using modified DISCERN criterion (range: 5-25) and novel sialendoscopy criterion (NSC, range: 0-7), respectively. Secondary outcomes included standard video metrics and Video Power Index to measure popularity.Videos were classified binarily by uploader type as from an academic medical center or from other sources.Results: Twenty-two (14.7%) of 150 videos were included for review, with 7 (31.8%) uploaded from academic medical institutions. One hundred-nine (72.7%) videos were excluded as lectures for medical professionals or OR recordings. Overall mean modified DISCERN (13.45 ± 3.42) and NSC (3.05 ± 0.96) scores were low; however, videos uploaded by academic medical institutions were significantly more comprehensive (NSC mean difference = 0.98, 95% CI: 0.16-1.80, p = .02). There were no significant correlations between video popularity and objective measures of quality or comprehensiveness.Conclusions: This study highlights the paucity and low quality of sialendoscopy videos for patients. More popular videos are not higher quality, and most videos are targeted more toward physicians rather than patients. As YouTube becomes increasingly used by patients, there is opportunity for otolaryngologists to produce more informative videos for patients while implementing targeted strategies to increase viewership.

show abstract

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

Cited by 28 publications

References 36 publications

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

A Semantic-Driven Model for Ranking Digital Learning Objects Based on Diversity in the User Comments

Fresh and Diverse Social Signals

Evaluating YouTube videos on sialendoscopy as an educational resource for patients

Contact Info

Product

Resources

About