75,056 research outputs found
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
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
POISED: Spotting Twitter Spam Off the Beaten Paths
Cybercriminals have found in online social networks a propitious medium to
spread spam and malicious content. Existing techniques for detecting spam
include predicting the trustworthiness of accounts and analyzing the content of
these messages. However, advanced attackers can still successfully evade these
defenses.
Online social networks bring people who have personal connections or share
common interests to form communities. In this paper, we first show that users
within a networked community share some topics of interest. Moreover, content
shared on these social network tend to propagate according to the interests of
people. Dissemination paths may emerge where some communities post similar
messages, based on the interests of those communities. Spam and other malicious
content, on the other hand, follow different spreading patterns.
In this paper, we follow this insight and present POISED, a system that
leverages the differences in propagation between benign and malicious messages
on social networks to identify spam and other unwanted content. We test our
system on a dataset of 1.3M tweets collected from 64K users, and we show that
our approach is effective in detecting malicious messages, reaching 91%
precision and 93% recall. We also show that POISED's detection is more
comprehensive than previous systems, by comparing it to three state-of-the-art
spam detection systems that have been proposed by the research community in the
past. POISED significantly outperforms each of these systems. Moreover, through
simulations, we show how POISED is effective in the early detection of spam
messages and how it is resilient against two well-known adversarial machine
learning attacks
- …