21 research outputs found
Scaling in Words on Twitter
Scaling properties of language are a useful tool for understanding generative
processes in texts. We investigate the scaling relations in citywise Twitter
corpora coming from the Metropolitan and Micropolitan Statistical Areas of the
United States. We observe a slightly superlinear urban scaling with the city
population for the total volume of the tweets and words created in a city. We
then find that a certain core vocabulary follows the scaling relationship of
that of the bulk text, but most words are sensitive to city size, exhibiting a
super- or a sublinear urban scaling. For both regimes we can offer a plausible
explanation based on the meaning of the words. We also show that the parameters
for Zipf's law and Heaps law differ on Twitter from that of other texts, and
that the exponent of Zipf's law changes with city size
Understanding Inequalities in Ride-Hailing Services Through Simulations
Despite the potential of online sharing economy platforms such as Uber, Lyft,
or Foodora to democratize the labor market, these services are often accused of
fostering unfair working conditions and low wages. These problems have been
recognized by researchers and regulators but the size and complexity of these
socio-technical systems, combined with the lack of transparency about
algorithmic practices, makes it difficult to understand system dynamics and
large-scale behavior. This paper combines approaches from complex systems and
algorithmic fairness to investigate the effect of algorithm design decisions on
wage inequality in ride-hailing markets. We first present a computational model
that includes conditions about locations of drivers and passengers, traffic,
the layout of the city, and the algorithm that matches requests with drivers.
We calibrate the model with parameters derived from empirical data. Our
simulations show that small changes in the system parameters can cause large
deviations in the income distributions of drivers, leading to a highly
unpredictable system which often distributes vastly different incomes to
identically performing drivers. As suggested by recent studies about feedback
loops in algorithmic systems, these initial income differences can result in
enforced and long-term wage gaps.Comment: Code for the simulation can be found at https://github.com/bokae/tax
The anatomy of a population-scale social network
Large-scale human social network structure is typically inferred from digital
trace samples of online social media platforms or mobile communication data.
Instead, here we investigate the social network structure of a complete
population, where people are connected by high-quality links sourced from
administrative registers of family, household, work, school, and next-door
neighbors. We examine this multilayer social opportunity structure through
three common concepts in network analysis: degree, closure, and distance.
Findings present how particular network layers contribute to presumably
universal scale-free and small-world properties of networks. Furthermore, we
suggest a novel measure of excess closure and apply this in a life-course
perspective to show how the social opportunity structure of individuals varies
along age, socio-economic status, and education level. Our work provides new
entry points to understand individual socio-economic failure and success as
well as persistent societal problems of inequality and segregation
Urban hierarchy and spatial diffusion over the innovation life cycle
Successful innovations achieve large geographical coverage by spreading
across settlements and distances. For decades, spatial diffusion has been
argued to take place along the urban hierarchy such that the innovation first
spreads from large to medium cities then later from medium to small cities.
Yet, the role of geographical distance, the other major factor of spatial
diffusion, was difficult to identify in hierarchical diffusion due to missing
data on spreading events. In this paper, we exploit spatial patterns of
individual invitations on a social media platform sent from registered users to
new users over the entire life cycle of the platform. This enables us to
disentangle the role of urban hierarchy and the role of distance by observing
the source and target locations of flows over an unprecedented timescale. We
demonstrate that hierarchical diffusion greatly overlaps with diffusion to
close distances and these factors co-evolve over the life cycle; thus, their
joint analysis is necessary. Then, a regression framework is applied to
estimate the number of invitations sent between pairs of towns by years in the
life cycle with the population sizes of the source and target towns, their
combinations, and the distance between them. We confirm that hierarchical
diffusion prevails initially across large towns only but emerges in the full
spectrum of settlements in the middle of the life cycle when adoption
accelerates. Unlike in previous gravity estimations, we find that after an
intensifying role of distance in the middle of the life cycle a surprisingly
weak distance effect characterizes the last years of diffusion. Our results
stress the dominance of urban hierarchy in spatial diffusion and inform future
predictions of innovation adoption at local scales
Race, Religion and the City: Twitter Word Frequency Patterns Reveal Dominant Demographic Dimensions in the United States
Recently, numerous approaches have emerged in the social sciences to exploit
the opportunities made possible by the vast amounts of data generated by online
social networks (OSNs). Having access to information about users on such a
scale opens up a range of possibilities, all without the limitations associated
with often slow and expensive paper-based polls. A question that remains to be
satisfactorily addressed, however, is how demography is represented in the OSN
content? Here, we study language use in the US using a corpus of text compiled
from over half a billion geo-tagged messages from the online microblogging
platform Twitter. Our intention is to reveal the most important spatial
patterns in language use in an unsupervised manner and relate them to
demographics. Our approach is based on Latent Semantic Analysis (LSA) augmented
with the Robust Principal Component Analysis (RPCA) methodology. We find
spatially correlated patterns that can be interpreted based on the words
associated with them. The main language features can be related to slang use,
urbanization, travel, religion and ethnicity, the patterns of which are shown
to correlate plausibly with traditional census data. Our findings thus validate
the concept of demography being represented in OSN language use and show that
the traits observed are inherently present in the word frequencies without any
previous assumptions about the dataset. Thus, they could form the basis of
further research focusing on the evaluation of demographic data estimation from
other big data sources, or on the dynamical processes that result in the
patterns found here
Real-time estimation of the effective reproduction number of COVID-19 from behavioral data
Near-real time estimations of the effective reproduction number are among the
most important tools to track the progression of a pandemic and to inform
policy makers and the general public. However, these estimations rely on
reported case numbers, commonly recorded with significant biases. The epidemic
outcome is strongly influenced by the dynamics of social contacts, which are
neglected in conventional surveillance systems as their real-time observation
is challenging. Here, we propose a concept using online and offline behavioral
data, recording age-stratified contact matrices at a daily rate. Modeling the
epidemic using the reconstructed matrices we dynamically estimate the effective
reproduction number during the two first waves of the COVID-19 pandemic in
Hungary. Our results demonstrate how behavioral data can be used to build
alternative monitoring systems complementing the established public health
surveillance. They can identify and provide better signals during periods when
official estimates appear unreliable due to observational biases
The role of geography in the complex diffusion of innovations
The urban-rural divide is increasing in modern societies calling for
geographical extensions of social influence modelling. Improved understanding
of innovation diffusion across locations and through social connections can
provide us with new insights into the spread of information, technological
progress and economic development. In this work, we analyze the spatial
adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and
uncover empirical features about the spatial adoption in social networks.
During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million
friendship ties of 3 million users. We find that the number of adopters as a
function of town population follows a scaling law that reveals a strongly
concentrated early adoption in large towns and a less concentrated late
adoption. We also discover a strengthening distance decay of spread over the
life-cycle indicating high fraction of distant diffusion in early stages but
the dominance of local diffusion in late stages. The spreading process is
modelled within the Bass diffusion framework that enables us to compare the
differential equation version with an agent-based version of the model run on
the empirical network. Although both models can capture the macro trend of
adoption, they have limited capacity to describe the observed trends of urban
scaling and distance decay. We find, however that incorporating adoption
thresholds, defined by the fraction of social connections that adopt a
technology before the individual adopts, improves the network model fit to the
urban scaling of early adopters. Controlling for the threshold distribution
enables us to eliminate the bias induced by local network structure on
predicting local adoption peaks. Finally, we show that geographical features
such as distance from the innovation origin and town size influence prediction
of adoption peak at local scales.Comment: 21 pages, 11 figures, 4 table
Urban hierarchy and spatial diffusion over the innovation life cycle
Successful innovations achieve large geographical coverage
by spreading across settlements and distances. For decades,
spatial diffusion has been argued to take place along the
urban hierarchy. Yet, the role of geographical distance was
difficult to identify in hierarchical diffusion due to missing data on spreading events. In this paper, we exploit spatial patterns of individual invitations sent from registered users to new users over the entire life cycle of a social media platform. We demonstrate that hierarchical diffusion overlaps with diffusion to close distances and these factors co-evolve over the life cycle. Therefore, we disentangle them in a regression framework that estimates the yearly number of invitations sent between pairs of towns. We confirm that hierarchical diffusion prevails initially across large towns only but emerges in the full spectrum of settlements in the middle of the life cycle when adoption accelerates. Unlike in previous gravity estimations, we find that after an intensifying role of distance in the middle of the life cycle a surprisingly weak distance effect characterizes the last years of diffusion. Our results stress the dominance of urban hierarchy in spatial diffusion and inform future predictions
of innovation adoption at local scales