54,388 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
Finding Street Gang Members on Twitter
Most street gang members use Twitter to intimidate others, to present
outrageous images and statements to the world, and to share recent illegal
activities. Their tweets may thus be useful to law enforcement agencies to
discover clues about recent crimes or to anticipate ones that may occur.
Finding these posts, however, requires a method to discover gang member Twitter
profiles. This is a challenging task since gang members represent a very small
population of the 320 million Twitter users. This paper studies the problem of
automatically finding gang members on Twitter. It outlines a process to curate
one of the largest sets of verifiable gang member profiles that have ever been
studied. A review of these profiles establishes differences in the language,
images, YouTube links, and emojis gang members use compared to the rest of the
Twitter population. Features from this review are used to train a series of
supervised classifiers. Our classifier achieves a promising F1 score with a low
false positive rate.Comment: 8 pages, 9 figures, 2 tables, Published as a full paper at 2016
IEEE/ACM International Conference on Advances in Social Networks Analysis and
Mining (ASONAM 2016
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
User preference profiling is an important task in modern online social
networks (OSN). With the proliferation of image-centric social platforms, such
as Pinterest, visual contents have become one of the most informative data
streams for understanding user preferences. Traditional approaches usually
treat visual content analysis as a general classification problem where one or
more labels are assigned to each image. Although such an approach simplifies
the process of image analysis, it misses the rich context and visual cues that
play an important role in people's perception of images. In this paper, we
explore the possibilities of learning a user's latent visual preferences
directly from image contents. We propose a distance metric learning method
based on Deep Convolutional Neural Networks (CNN) to directly extract
similarity information from visual contents and use the derived distance metric
to mine individual users' fine-grained visual preferences. Through our
preliminary experiments using data from 5,790 Pinterest users, we show that
even for the images within the same category, each user possesses distinct and
individually-identifiable visual preferences that are consistent over their
lifetime. Our results underscore the untapped potential of finer-grained visual
preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop
User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience
A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further
step towards an iterative design that considers the user interaction a central point. The paper discusses
how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the
developments that followed that first experience: our findings seem still valid despite the passed time
Mining for Social Serendipity
A common social problem at an event in which people do not personally know all of the other participants is the natural tendency for cliques to form and for discussions to mainly happen between people who already know each other. This limits the possibility for people to make interesting new acquaintances and acts as a retarding force in the creation of new links in the social web. Encouraging users to socialize with people they don't know by revealing to them hidden surprising links could help to improve the diversity of interactions at an event. The goal of this paper is to propose a method for detecting "surprising" relationships between people attending an event. By "surprising" relationship we mean those relationships that are not known a priori, and that imply shared information not directly related with the local context of the event (location, interests, contacts) at which the meeting takes place. To demonstrate and test our concept we used the Flickr community. We focused on a community of users associated with a social event (a computer science conference) and represented in Flickr by means of a photo pool devoted to the event. We use Flickr metadata (tags) to mine for user similarity not related to the context of the event, as represented in the corresponding Flickr group. For example, we look for two group members who have been in the same highly specific place (identified by means of geo-tagged photos), but are not friends of each other and share no other common interests or, social neighborhood
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