91 research outputs found
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
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
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
Link creation and profile alignment in the aNobii social network
The present work investigates the structural and dynamical properties of
aNobii\footnote{http://www.anobii.com/}, a social bookmarking system designed
for readers and book lovers. Users of aNobii provide information about their
library, reading interests and geographical location, and they can establish
typed social links to other users. Here, we perform an in-depth analysis of the
system's social network and its interplay with users' profiles. We describe the
relation of geographic and interest-based factors to social linking.
Furthermore, we perform a longitudinal analysis to investigate the interplay of
profile similarity and link creation in the social network, with a focus on
triangle closure. We report a reciprocal causal connection: profile similarity
of users drives the subsequent closure in the social network and, reciprocally,
closure in the social network induces subsequent profile alignment. Access to
the dynamics of the social network also allows us to measure quantitative
indicators of preferential linking.Comment: http://www.iisocialcom.org/conference/socialcom2010
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
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
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