63,381 research outputs found
Connection Discovery using Shared Images by Gaussian Relational Topic Model
Social graphs, representing online friendships among users, are one of the
fundamental types of data for many applications, such as recommendation,
virality prediction and marketing in social media. However, this data may be
unavailable due to the privacy concerns of users, or kept private by social
network operators, which makes such applications difficult. Inferring user
interests and discovering user connections through their shared multimedia
content has attracted more and more attention in recent years. This paper
proposes a Gaussian relational topic model for connection discovery using user
shared images in social media. The proposed model not only models user
interests as latent variables through their shared images, but also considers
the connections between users as a result of their shared images. It explicitly
relates user shared images to user connections in a hierarchical, systematic
and supervisory way and provides an end-to-end solution for the problem. This
paper also derives efficient variational inference and learning algorithms for
the posterior of the latent variables and model parameters. It is demonstrated
through experiments with over 200k images from Flickr that the proposed method
significantly outperforms the methods in previous works.Comment: IEEE International Conference on Big Data 201
Mapping Twitter Topic Networks: From Polarized Crowds to Community Clusters
Conversations on Twitter create networks with identifiable contours as people reply to and mention one another in their tweets. These conversational structures differ, depending on the subject and the people driving the conversation. Six structures are regularly observed: divided, unified, fragmented, clustered, and inward and outward hub and spoke structures. These are created as individuals choose whom to reply to or mention in their Twitter messages and the structures tell a story about the nature of the conversatio
#Halal Culture on Instagram
Halal is a notion that applies to both objects and actions, and means
permissible according to Islamic law. It may be most often associated with food
and the rules of selecting, slaughtering, and cooking animals. In the
globalized world, halal can be found in street corners of New York and beauty
shops of Manila. In this study, we explore the cultural diversity of the
concept, as revealed through social media, and specifically the way it is
expressed by different populations around the world, and how it relates to
their perception of (i) religious and (ii) governmental authority, and (iii)
personal health. Here, we analyze two Instagram datasets, using Halal in Arabic
(325,665 posts) and in English (1,004,445 posts), which provide a global view
of major Muslim populations around the world. We find a great variety in the
use of halal within Arabic, English, and Indonesian-speaking populations, with
animal trade emphasized in first (making up 61% of the language's stream), food
in second (80%), and cosmetics and supplements in third (70%). The
commercialization of the term halal is a powerful signal of its detraction from
its traditional roots. We find a complex social engagement around posts
mentioning religious terms, such that when a food-related post is accompanied
by a religious term, it on average gets more likes in English and Indonesian,
but not in Arabic, indicating a potential shift out of its traditional moral
framing
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
Face Clustering for Connection Discovery from Event Images
Social graphs are very useful for many applications, such as recommendations
and community detections. However, they are only accessible to big social
network operators due to both data availability and privacy concerns. Event
images also capture the interactions among the participants, from which social
connections can be discovered to form a social graph. Unlike online social
graphs, social connections carried by event images can be extracted without
user inputs, and hence many social graph-based applications become possible,
even without access to online social graphs. This paper proposes a system to
discover social connections from event images. By utilizing the social
information from even images, such as co-occurrence, a face clustering method
is proposed and implemented, and connections can be discovered without the
identity of the event participants. By collecting over 40000 faces from over
3000 participants, it is shown that the faces can be well clustered with 80% in
F1 score, and social graphs can be constructed. Utilizing offline event images
may create a long-term impact on social network analytics.Comment: 18 page
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