2,382 research outputs found
Ranking News-Quality Multimedia
News editors need to find the photos that best illustrate a news piece and
fulfill news-media quality standards, while being pressed to also find the most
recent photos of live events. Recently, it became common to use social-media
content in the context of news media for its unique value in terms of immediacy
and quality. Consequently, the amount of images to be considered and filtered
through is now too much to be handled by a person. To aid the news editor in
this process, we propose a framework designed to deliver high-quality,
news-press type photos to the user. The framework, composed of two parts, is
based on a ranking algorithm tuned to rank professional media highly and a
visual SPAM detection module designed to filter-out low-quality media. The core
ranking algorithm is leveraged by aesthetic, social and deep-learning semantic
features. Evaluation showed that the proposed framework is effective at finding
high-quality photos (true-positive rate) achieving a retrieval MAP of 64.5% and
a classification precision of 70%.Comment: To appear in ICMR'1
Exploring Image Virality in Google Plus
Reactions to posts in an online social network show different dynamics
depending on several textual features of the corresponding content. Do similar
dynamics exist when images are posted? Exploiting a novel dataset of posts,
gathered from the most popular Google+ users, we try to give an answer to such
a question. We describe several virality phenomena that emerge when taking into
account visual characteristics of images (such as orientation, mean saturation,
etc.). We also provide hypotheses and potential explanations for the dynamics
behind them, and include cases for which common-sense expectations do not hold
true in our experiments.Comment: 8 pages, 8 figures. IEEE/ASE SocialCom 201
Online Popularity and Topical Interests through the Lens of Instagram
Online socio-technical systems can be studied as proxy of the real world to
investigate human behavior and social interactions at scale. Here we focus on
Instagram, a media-sharing online platform whose popularity has been rising up
to gathering hundred millions users. Instagram exhibits a mixture of features
including social structure, social tagging and media sharing. The network of
social interactions among users models various dynamics including
follower/followee relations and users' communication by means of
posts/comments. Users can upload and tag media such as photos and pictures, and
they can "like" and comment each piece of information on the platform. In this
work we investigate three major aspects on our Instagram dataset: (i) the
structural characteristics of its network of heterogeneous interactions, to
unveil the emergence of self organization and topically-induced community
structure; (ii) the dynamics of content production and consumption, to
understand how global trends and popular users emerge; (iii) the behavior of
users labeling media with tags, to determine how they devote their attention
and to explore the variety of their topical interests. Our analysis provides
clues to understand human behavior dynamics on socio-technical systems,
specifically users and content popularity, the mechanisms of users'
interactions in online environments and how collective trends emerge from
individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201
Animating and sustaining niche social networks
Within the communicative space online Social Network Sites (SNS) afford, Niche Social Networks Sites (NSNS) have emerged around particular geographic, demographic or topic-based communities to provide what broader SNS do not: specified and targeted content for an engaged and interested community. Drawing on a research project developed at the Queensland University of Technology in conjunction with the Australian Smart Services Cooperative Research Centre that produced an NSNS based around Adventure Travel, this paper outlines the main drivers for community creation and sustainability within NSNS. The paper asks what factors motivate users to join and stay with these sites and what, if any, common patterns can be noted in their formation. It also outlines the main barriers to online participation and content creation in NSNS, and the similarities and differences in SNS and NSNS business models. Having built a community of 100 registered members, the staywild.com.au project was a living laboratory, enabling us to document the steps taken in producing a NSNS and cultivating and retaining active contributors. The paper incorporates observational analysis of user-generated content (UGC) and user profile submissions, statistical analysis of site usage, and findings from a survey of our membership pool in noting areas of success and of failure. In drawing on our project in this way we provide a template for future iterations of NSNS initiation and development across various other social settings: not only niche communities, but also the media and advertising with which they engage and interact. Positioned within the context of online user participation and UGC research, our paper concludes with a discussion of the ways in which the tools afforded by NSNS extend earlier understandings of online ‘communities of interest’. It also outlines the relevance of our research to larger questions about the diversity of the social media ecology
An Image Is Worth More than a Thousand Favorites: Surfacing the Hidden Beauty of Flickr Pictures
The dynamics of attention in social media tend to obey power laws. Attention
concentrates on a relatively small number of popular items and neglecting the
vast majority of content produced by the crowd. Although popularity can be an
indication of the perceived value of an item within its community, previous
research has hinted to the fact that popularity is distinct from intrinsic
quality. As a result, content with low visibility but high quality lurks in the
tail of the popularity distribution. This phenomenon can be particularly
evident in the case of photo-sharing communities, where valuable photographers
who are not highly engaged in online social interactions contribute with
high-quality pictures that remain unseen. We propose to use a computer vision
method to surface beautiful pictures from the immense pool of
near-zero-popularity items, and we test it on a large dataset of
creative-commons photos on Flickr. By gathering a large crowdsourced ground
truth of aesthetics scores for Flickr images, we show that our method retrieves
photos whose median perceived beauty score is equal to the most popular ones,
and whose average is lower by only 1.5%.Comment: ICWSM 201
Can Computers Create Art?
This essay discusses whether computers, using Artificial Intelligence (AI),
could create art. First, the history of technologies that automated aspects of
art is surveyed, including photography and animation. In each case, there were
initial fears and denial of the technology, followed by a blossoming of new
creative and professional opportunities for artists. The current hype and
reality of Artificial Intelligence (AI) tools for art making is then discussed,
together with predictions about how AI tools will be used. It is then
speculated about whether it could ever happen that AI systems could be credited
with authorship of artwork. It is theorized that art is something created by
social agents, and so computers cannot be credited with authorship of art in
our current understanding. A few ways that this could change are also
hypothesized.Comment: to appear in Arts, special issue on Machine as Artist (21st Century
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
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