Number of items: 2.
and Kossinets, Gueorgi
and Kleinberg, Jon
and Lee, Lillian How Opinions are Received by Online Communities: A Case Study on Amazon.com Helpfulness Votes.
There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like “26 of 32 people found the following review helpful.” Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, “What did Y think of X?”, we are asking, “What did Z think of Y’s opinion of X?” Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We ﬁnd that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review “plagiarism” to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our ﬁndings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications – Data Mining General Terms: Measurement, Theory Keywords: Review helpfulness, review utility, social inﬂuence, online communities, sentiment analysis, opinion mining, plagiarism.
and Backstrom, Lars
and Huttenlocher, Daniel
and Kleinberg, Jon Mapping the World's Photos.
We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to deﬁne a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classiﬁcation methods for predicting such locations from visual, textual and temporal features of the photos. We ﬁnd that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.
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