129 research outputs found
The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City
When providing directions to a place, web and mobile mapping services are all
able to suggest the shortest route. The goal of this work is to automatically
suggest routes that are not only short but also emotionally pleasant. To
quantify the extent to which urban locations are pleasant, we use data from a
crowd-sourcing platform that shows two street scenes in London (out of
hundreds), and a user votes on which one looks more beautiful, quiet, and
happy. We consider votes from more than 3.3K individuals and translate them
into quantitative measures of location perceptions. We arrange those locations
into a graph upon which we learn pleasant routes. Based on a quantitative
validation, we find that, compared to the shortest routes, the recommended ones
add just a few extra walking minutes and are indeed perceived to be more
beautiful, quiet, and happy. To test the generality of our approach, we
consider Flickr metadata of more than 3.7M pictures in London and 1.3M in
Boston, compute proxies for the crowdsourced beauty dimension (the one for
which we have collected the most votes), and evaluate those proxies with 30
participants in London and 54 in Boston. These participants have not only rated
our recommendations but have also carefully motivated their choices, providing
insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201
Reading the Source Code of Social Ties
Though online social network research has exploded during the past years, not
much thought has been given to the exploration of the nature of social links.
Online interactions have been interpreted as indicative of one social process
or another (e.g., status exchange or trust), often with little systematic
justification regarding the relation between observed data and theoretical
concept. Our research aims to breach this gap in computational social science
by proposing an unsupervised, parameter-free method to discover, with high
accuracy, the fundamental domains of interaction occurring in social networks.
By applying this method on two online datasets different by scope and type of
interaction (aNobii and Flickr) we observe the spontaneous emergence of three
domains of interaction representing the exchange of status, knowledge and
social support. By finding significant relations between the domains of
interaction and classic social network analysis issues (e.g., tie strength,
dyadic interaction over time) we show how the network of interactions induced
by the extracted domains can be used as a starting point for more nuanced
analysis of online social data that may one day incorporate the normative
grammar of social interaction. Our methods finds applications in online social
media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web
(WebSci'14
The Emotional and Chromatic Layers of Urban Smells
People are able to detect up to 1 trillion odors. Yet, city planning is
concerned only with a few bad odors, mainly because odors are currently
captured only through complaints made by urban dwellers. To capture both good
and bad odors, we resort to a methodology that has been recently proposed and
relies on tagging information of geo-referenced pictures. In doing so for the
cities of London and Barcelona, this work makes three new contributions. We
study 1) how the urban smellscape changes in time and space; 2) which emotions
people share at places with specific smells; and 3) what is the color of a
smell, if it exists. Without social media data, insights about those three
aspects have been difficult to produce in the past, further delaying the
creation of urban restorative experiences.Comment: 11 pages, 18 figures, final version published in the Proceedings of
the Tenth International Conference on Web and Social Media (ICWSM 2016
Smelly Maps: The Digital Life of Urban Smellscapes
Smell has a huge influence over how we perceive places. Despite its
importance, smell has been crucially overlooked by urban planners and
scientists alike, not least because it is difficult to record and analyze at
scale. One of the authors of this paper has ventured out in the urban world and
conducted smellwalks in a variety of cities: participants were exposed to a
range of different smellscapes and asked to record their experiences. As a
result, smell-related words have been collected and classified, creating the
first dictionary for urban smell. Here we explore the possibility of using
social media data to reliably map the smells of entire cities. To this end, for
both Barcelona and London, we collect geo-referenced picture tags from Flickr
and Instagram, and geo-referenced tweets from Twitter. We match those tags and
tweets with the words in the smell dictionary. We find that smell-related words
are best classified in ten categories. We also find that specific categories
(e.g., industry, transport, cleaning) correlate with governmental air quality
indicators, adding validity to our study.Comment: 11 pages, 7 figures, Proceedings of 9th International AAAI Conference
on Web and Social Media (ICWSM2015
Folks in Folksonomies: Social Link Prediction from Shared Metadata
Web 2.0 applications have attracted a considerable amount of attention
because their open-ended nature allows users to create light-weight semantic
scaffolding to organize and share content. To date, the interplay of the social
and semantic components of social media has been only partially explored. Here
we focus on Flickr and Last.fm, two social media systems in which we can relate
the tagging activity of the users with an explicit representation of their
social network. We show that a substantial level of local lexical and topical
alignment is observable among users who lie close to each other in the social
network. We introduce a null model that preserves user activity while removing
local correlations, allowing us to disentangle the actual local alignment
between users from statistical effects due to the assortative mixing of user
activity and centrality in the social network. This analysis suggests that
users with similar topical interests are more likely to be friends, and
therefore semantic similarity measures among users based solely on their
annotation metadata should be predictive of social links. We test this
hypothesis on the Last.fm data set, confirming that the social network
constructed from semantic similarity captures actual friendship more accurately
than Last.fm's suggestions based on listening patterns.Comment: http://portal.acm.org/citation.cfm?doid=1718487.171852
6 Seconds of Sound and Vision: Creativity in Micro-Videos
The notion of creativity, as opposed to related concepts such as beauty or
interestingness, has not been studied from the perspective of automatic
analysis of multimedia content. Meanwhile, short online videos shared on social
media platforms, or micro-videos, have arisen as a new medium for creative
expression. In this paper we study creative micro-videos in an effort to
understand the features that make a video creative, and to address the problem
of automatic detection of creative content. Defining creative videos as those
that are novel and have aesthetic value, we conduct a crowdsourcing experiment
to create a dataset of over 3,800 micro-videos labelled as creative and
non-creative. We propose a set of computational features that we map to the
components of our definition of creativity, and conduct an analysis to
determine which of these features correlate most with creative video. Finally,
we evaluate a supervised approach to automatically detect creative video, with
promising results, showing that it is necessary to model both aesthetic value
and novelty to achieve optimal classification accuracy.Comment: 8 pages, 1 figures, conference IEEE CVPR 201
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