Analyzing and Mining Comments and Comment Ratings on the Social Web

Date

2014-06-01

Author

SİERSDORFER, Stefan
CHELARU, Sergiu
Pedro, Jose San
Altıngövde, İsmail Sengör
NEJDL, Wolfgang

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

197
views

0
downloads

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 textual content, thread structure of comments, and associated content (e.g., videos and their metadata) are analyzed to obtain a comprehensive understanding of the community commenting behavior. Furthermore, this article explores the applicability of machine learning and data mining to detect acceptance of comments by the community, comments likely to trigger discussions, controversial and polarizing content, and users exhibiting offensive commenting behavior. Results from this study have potential application in guiding the design of community-oriented online discussion platforms.

Subject Keywords

Computer Networks and Communications

URI

https://hdl.handle.net/11511/42360

Journal

ACM TRANSACTIONS ON THE WEB

DOI

https://doi.org/10.1145/2628441

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

How useful is social feedback for learning to rank YouTube videos? CHELARU, Sergiu; Orellana-Rodriguez, Claudia; Altıngövde, İsmail Sengör (Springer Science and Business Media LLC, 2014-09-01) 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 r...
Analyzing, Detecting, and Exploiting Sentiment in Web Queries Chelaru, Sergiu; Altıngövde, İsmail Sengör; Siersdorfer, Stefan; Nejdl, Wolfgang (Association for Computing Machinery (ACM), 2013-12-01) The Web contains an increasing amount of biased and opinionated documents on politics, products, and polarizing events. In this article, we present an indepth analysis of Web search queries for controversial topics, focusing on query sentiment. To this end, we conduct extensive user assessments and discriminative term analyses, as well as a sentiment analysis using the SentiWordNet thesaurus, a lexical resource containing sentiment annotations. Furthermore, in order to detect the sentiment expressed in quer...
A time-evolution model for the privacy degree of information disseminated in online social networks Othmane, Lotfi Ben; Weffers, Harold; Angın, Pelin; Bhargava, Bharat (Inderscience Publishers, 2013-01-01) People tend to share private information with their friends on online social networks (OSNs). The common position is that the shared information eventually reaches all users of the network since OSNs exhibit the small-world property. However, dissemination of private information in an OSN exhibits a set of factors that need to be accounted for in order to create more realistic models of the evolution of the privacy degree of information disseminated in an OSN. Among these factors are relationship strength b...
Second Chance: A Hybrid Approach for Dynamic Result Caching and Prefetching in Search Engines Ozcan, Rifat; Altıngövde, İsmail Sengör; Barla Cambazoglu, B.; ULUSOY, ÖZGÜR (Association for Computing Machinery (ACM), 2013-12-01) Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some data that is used to construct the final search result page returned to the user. An alternative strategy is to store in the cache only the result document IDs, which take much less space, allowing results of more queries to be cached. These two strategies lead to an interesting trade-off between the hit rate and the average query response latency. In this work...
Scanpath Trend Analysis on Web Pages: Clustering Eye Tracking Scanpaths Eraslan, Sukru; Yesilada, Yeliz; Harper, Simon (Association for Computing Machinery (ACM), 2016-12-01) Eye tracking studies have widely been used in improving the design and usability of web pages and in the research of understanding how users navigate them. However, there is limited research in clustering users' eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist, which means that the resulting path is so short that it is not useful. Moreover, there is little work on correlating users' scanpaths with visual elements of...

Citation Formats

S. SİERSDORFER, S. CHELARU, J. S. Pedro, İ. S. Altıngövde, and W. NEJDL, “Analyzing and Mining Comments and Comment Ratings on the Social Web,” ACM TRANSACTIONS ON THE WEB, pp. 0–0, 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42360.