71,026 research outputs found
Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places
New research cutting across architecture, urban studies, and psychology is
contextualizing the understanding of urban spaces according to the perceptions
of their inhabitants. One fundamental construct that relates place and
experience is ambiance, which is defined as "the mood or feeling associated
with a particular place". We posit that the systematic study of ambiance
dimensions in cities is a new domain for which multimedia research can make
pivotal contributions. We present a study to examine how images collected from
social media can be used for the crowdsourced characterization of indoor
ambiance impressions in popular urban places. We design a crowdsourcing
framework to understand suitability of social images as data source to convey
place ambiance, to examine what type of images are most suitable to describe
ambiance, and to assess how people perceive places socially from the
perspective of ambiance along 13 dimensions. Our study is based on 50,000
Foursquare images collected from 300 popular places across six cities
worldwide. The results show that reliable estimates of ambiance can be obtained
for several of the dimensions. Furthermore, we found that most aggregate
impressions of ambiance are similar across popular places in all studied
cities. We conclude by presenting a multidisciplinary research agenda for
future research in this domain
Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory
Social groups play a crucial role in social media platforms because they form
the basis for user participation and engagement. Groups are created explicitly
by members of the community, but also form organically as members interact. Due
to their importance, they have been studied widely (e.g., community detection,
evolution, activity, etc.). One of the key questions for understanding how such
groups evolve is whether there are different types of groups and how they
differ. In Sociology, theories have been proposed to help explain how such
groups form. In particular, the common identity and common bond theory states
that people join groups based on identity (i.e., interest in the topics
discussed) or bond attachment (i.e., social relationships). The theory has been
applied qualitatively to small groups to classify them as either topical or
social. We use the identity and bond theory to define a set of features to
classify groups into those two categories. Using a dataset from Flickr, we
extract user-defined groups and automatically-detected groups, obtained from a
community detection algorithm. We discuss the process of manual labeling of
groups into social or topical and present results of predicting the group label
based on the defined features. We directly validate the predictions of the
theory showing that the metrics are able to forecast the group type with high
accuracy. In addition, we present a comparison between declared and detected
groups along topicality and sociality dimensions.Comment: 10 pages, 6 figures, 2 table
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A conceptual framework for studying collective reactions to events in location-based social media
Events are a core concept of spatial information, but location-based social media (LBSM) provide information on reactions to events. Individuals have varied degrees of agency in initiating, reacting to or modifying the course of events, and reactions include observations of occurrence, expressions containing sentiment or emotions, or a call to action. Key characteristics of reactions include referent events and information about who reacted, when, where and how. Collective reactions are composed of multiple individual reactions sharing common referents. They can be characterized according to the following dimensions: spatial, temporal, social, thematic and interlinkage. We present a conceptual framework, which allows characterization and comparison of collective reactions. For a thematically well-defined class of event such as storms, we can explore differences and similarities in collective attribution of meaning across space and time. Other events may have very complex spatio-temporal signatures (e.g. political processes such as Brexit or elections), which can be decomposed into series of individual events (e.g. a temporal window around the result of a vote). The purpose of our framework is to explore ways in which collective reactions to events in LBSM can be described and underpin the development of methods for analysing and understanding collective reactions to events
ShenZhen transportation system (SZTS): a novel big data benchmark suite
Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets
Sequential Deliberation for Social Choice
In large scale collective decision making, social choice is a normative study
of how one ought to design a protocol for reaching consensus. However, in
instances where the underlying decision space is too large or complex for
ordinal voting, standard voting methods of social choice may be impractical.
How then can we design a mechanism - preferably decentralized, simple,
scalable, and not requiring any special knowledge of the decision space - to
reach consensus? We propose sequential deliberation as a natural solution to
this problem. In this iterative method, successive pairs of agents bargain over
the decision space using the previous decision as a disagreement alternative.
We describe the general method and analyze the quality of its outcome when the
space of preferences define a median graph. We show that sequential
deliberation finds a 1.208- approximation to the optimal social cost on such
graphs, coming very close to this value with only a small constant number of
agents sampled from the population. We also show lower bounds on simpler
classes of mechanisms to justify our design choices. We further show that
sequential deliberation is ex-post Pareto efficient and has truthful reporting
as an equilibrium of the induced extensive form game. We finally show that for
general metric spaces, the second moment of of the distribution of social cost
of the outcomes produced by sequential deliberation is also bounded
History of art paintings through the lens of entropy and complexity
Art is the ultimate expression of human creativity that is deeply influenced
by the philosophy and culture of the corresponding historical epoch. The
quantitative analysis of art is therefore essential for better understanding
human cultural evolution. Here we present a large-scale quantitative analysis
of almost 140 thousand paintings, spanning nearly a millennium of art history.
Based on the local spatial patterns in the images of these paintings, we
estimate the permutation entropy and the statistical complexity of each
painting. These measures map the degree of visual order of artworks into a
scale of order-disorder and simplicity-complexity that locally reflects
qualitative categories proposed by art historians. The dynamical behavior of
these measures reveals a clear temporal evolution of art, marked by transitions
that agree with the main historical periods of art. Our research shows that
different artistic styles have a distinct average degree of entropy and
complexity, thus allowing a hierarchical organization and clustering of styles
according to these metrics. We have further verified that the identified groups
correspond well with the textual content used to qualitatively describe the
styles, and that the employed complexity-entropy measures can be used for an
effective classification of artworks.Comment: 10 two-column pages, 5 figures; accepted for publication in PNAS
[supplementary information available at
http://www.pnas.org/highwire/filestream/824089/field_highwire_adjunct_files/0/pnas.1800083115.sapp.pdf
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