# Tales of Two Cities: Using Social Media to Understand Idiosyncratic
Lifestyles in Distinctive Metropolitan Areas

Tianran Hu, Eric Bigelow, Jiebo Luo, and Henry Kautz Authors are with Computer
Science Department, University of Rochester E-mail: {thu, jluo,
<EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Lifestyles are a valuable model for understanding individuals’ physical and
mental lives, comparing social groups, and making recommendations for
improving people’s lives. In this paper, we examine and compare lifestyle
behaviors of people living in cities of different sizes, utilizing freely
available social media data as a large-scale, low-cost alternative to
traditional survey methods. We use the Greater New York City area as a
representative for large cities, and the Greater Rochester area as a
representative for smaller cities in the United States. We employed matrix
factor analysis as an unsupervised method to extract salient mobility and
work-rest patterns for a large population of users within each metropolitan
area. We discovered interesting human behavior patterns at both a larger scale
and a finer granularity than is present in previous literature, some of which
allow us to quantitatively compare the behaviors of individuals of living in
big cities to those living in small cities. We believe that our social media-
based approach to lifestyle analysis represents a powerful tool for social
computing in the big data age.

###### Index Terms:

Lifestyles, Urban Computing.

##  1 Introduction

We take lifestyle to be the way in which a person or group lives including the
interests, opinions, behaviors, and behavioral orientations. Understanding
lifestyle is key to gaining insight of the physical and mental aspects of
individuals, social groups and cultures. Health, for example, is highly
related to one’s lifestyle [1, 2]. Cultural boundaries can be discovered from
people’s ways of living such as pace of life, eating and drinking habits and
so on [3, 4]. Researchers have also discovered correlations between health and
individuals’ daily movements as estimated from cellphone GPS tags on social
media [5].

In this work, we study the differences of lifestyles in cities of different
sizes. A popular stereotype is that life in big cities is fast-paced, high-
pressure, and consistently exciting, while life in small cities is calmer and
less various due to a lower population density and more limited selection of
recreational venues.

We select the Greater New York City area (NYC) as being representative, for
our purposes, of big cities in the US. For smaller cities, we select the Great
Rochester area (ROC) as representative for two main reasons: First, the size
of Rochester (0.2 million) is close to the median size (0.16 million) of
cities in the US, approximately 40 times smaller than NYC. Second, these two
areas are located close to each other (both in the north-eastern US).
Geographic closeness generally leads to similarity of climate and culture,
which helps eliminate confounding factors that may lead to differences in
lifestyle behaviors unrelated to city size.

In contrast to traditional research investigating lifestyle patterns, where
data collection methods include questionnaires and telephone interviewing [6,
7, 8], we leverage data from social media to make inferences about people’s
lifestyles. The wide adoption of social media brings researchers a new
opportunity of studying natural, unconstrained human behavior at very large
scales. Foursquare is one of the most popular Location Based Social Networks
(LBSNs), holding 5 billion check-in records for 55 million users
worldwide111https://foursquare.com/about. This offers us a rich data source
for conducting mobility, behavior and lifestyle studies.

We consider temporal and spatial lifestyle in this work. The temporal
dimension of a person’s lifestyle is assumed to correlate with his/her work-
rest ratio in daily actives. In the primary literature on circadian topology
(CT), people are classified into one of three categories: morning-types,
evening-types, and neither-types [9]. In the CT literature, individuals are
modeled by just one of these types. In our present work, work-rest behavioral
patterns are instead considered to be a weighted combination of all three
temporal lifestyles: “Night Owl”, “Early Bird” and “Intermediate”

To avoid assigning a person to a lifestyle in an arbitrary or qualitative
fashion, we employ non-negative matrix factorization (NMF) to discover three
latent patterns of temporal activity. The extracted patterns offer precise
definitions of activity levels associated with specific lifestyles and align
with our assumptions about human work-rest habits. A spatial dimension is used
to describe lifestyles according to locational behavior. For example, one
primitive lifestyle pattern is defined by frequent visits to POIs (points of
interest) such as bars and music venues, while another is defined by visits to
parks, art galleries and museums. We then apply a clustering method to group
these primitive latent patterns into more complex lifestyles that are
representative of a group of individuals (e.g. students or stay-at-home
parents). We significant variance between the distribution of lifestyles in
NYC and ROC.

Additionally, we use third-order tensor decomposition to find composite
patterns across both spatial and temporal dimensions. We extract clearly
identifiable patterns of behavior, for example high school students posting
during school hours, and for college students frequently visiting or living on
campus. This method offers promise as an efficient way of extracting
complicated patterns across multiple high-dimensional spaces.

The main contributions of this work are:

1\. Use of open-source geo-tagged social media data for analyzing lifestyle
patterns as a low-cost, large-scale alternative to traditional survey methods.

2\. Application of matrix factor analysis to extract persistent and salient
human mobility and work-rest patterns over a large population of users.

3\. Application of CP tensor decomposition to discover composite spatial-
temporal lifestyle patterns which are useful for understanding fluctuations in
people’s activity across different time ranges and locations.

4\. Confirming intuitive knowledge and previous research in human activity
patterns with quantitative, unsupervised data analysis.

5\. Shedding light on the differences and similarities between life in big
cities and life in smaller cities, quantitatively confirming many of the
common perceptions about life in big and small cities. For example, life in
big cities is more work-focused, while it is more home-focused in smaller
cities; life in large cities is also more fast-paced and diverse. Furthermore,
we have discovered fine-grained lifestyle descriptions that previous small-
scale survey-based studies have failed to illuminate. For example, we
extracted three types of temporal lifestyles, and report the activity level of
each lifestyle along time quantitatively.

##  2 Related Work

###  2.1 Sociology and Cronobiology

Lifestyle is well studied in sociology. The work of [8] suggests that
lifestyles such as residential location, mode options, destination choices,
and trip timing are constrained by household considerations. Gender difference
in lifestyles also attracts interest of many researchers. Budesa et al. study
the influence of gender on perceived healthy and unhealthy lifestyles, finding
that gender is not an important determinant of individual perceptions about
health [6]. Merritt et al. in [10] suggest that men and women have no
significant difference in motor ability in daily activities. Finally, a study
[11] on university students finds that female students are healthier due to
less alcohol consumption and more healthy habits.

Much work has been done on human work-rest habits in chronobiology and
Circadian Topology (CT). The traditional method of studying how work-rest
patterns relate to aspects of physical and mental well-being has been learned
through the morningness-eveningness questionnaire (MEQ) of [12] and variations
of it [13]. In the work of Horne et al., Morning-type subjects (MTs) are found
to wake at a mean of 7:24am, Neither-type subjects (NTs) at 8:07am, and
Evening-type subjects (ETs) at 9:18am; mean bed times for the three types are
11:26pm, 11:30pm, and 1:05am, respectively. These specific times vary in
different studies, leading to differing assertions about how much of the
population is a member of each CT type [14, 15].

Randler finds a significant positive correlation between “morningness”
tendencies of people and satisfaction in life [7], and Monk [16] find that MT
individuals appear to have more regular lifestyle than ETs. A positive
correlation between eveningness and depression level is reported by Hasler et
al. [17]. A thorough review of contemporary CT literature is available in
[15].

###  2.2 Social Media Analytics

In recent years researchers have successfully utilized social media in
research ventures related to lifestyle analysis. Noulas et al. of [18] use
Foursquare data to discover the behavioral habits of residents in London. The
work presented in this paper is strongly inspired by this research: we
contribute stacked plots similar to those of Noulas et al., representing the
relative visit frequencies of the most frequent POIs, comparing between NYC
(6) and ROC (5) and between weekends and weekdays within these cities. Based
on the contents of tweets, Sadiek et al. build a language model to detect the
health condition of individuals [19]. By relating a user’s health level with
other attributes such as environmental features of places where the user
spends tags as estimated from his tweet geotags, they estimate the influence
of lifestyle on health conditions [5]. Eating and drinking habit is also a key
point to understanding human life. In [20] Abbar et al. find out the names of
food in people’s tweets and use them to estimate the caloric values people
possibly take.

Cranshaw et al. [21] construct a metric called Location Entropy to measure the
diversity of a POI. Sang et al. discuss people’s movement session patterns
[22] based on China’s LBSN data. The eating and drinking habits of different
countries and regions are investigated in [4] based on Foursquare data. They
find that geographic closeness usually leads to closeness in eating and
drinking habits. Wu et al. reported an approach on modeling temporal dynamic
in[23]. Their work showed that besides user-item factors temporal factors are
as important in social media popularity prediction. Other aspects of lifestyle
such as pace of life and power distance, are discussed in [3]. They estimate
each index related to life via tweets collected all over the world. In [24],
Golder et al. found that the negative affect (NA) tweets sent in winter is
higher than those sent in summer. Similar results are reported in [25], in
which the weather influence on human sentiment is studied using tweets. Tensor
decomposition was applied in [26]. In this work, Zheng Yu et. al decomposed
third-order tensors to extract noise-location compound patterns in an urban
area. We employ similar method in our work to find temporal-spatial compound
patterns of lifestyles.

##  3 Data Set and Preprocessing

The large number of self-reported location records and wide geographic
coverage make Foursquare a valuable data source for analyzing behavioral
tendencies across groups of individuals. However, directly collection of
users’ check-ins is a nontrivial task due to the strict limits on Foursquare
data download rates. As an alternative, many researchers collect Foursquare
data through other social media sources that connect with Foursquare such as
Twitter [18]. If a user links his or her Foursquare account with a Twitter
account, when s/he performs a check-in on Foursquare, a geo-tagged tweet will
be posted automatically. This tweet contains a link to the webpage of the
venue where the user checks in via Foursquare. In the present work, we use
this method to collect users’ check-in data. To avoid tweets from possible
tourists, we filter out users whose tweets appeared exclusively within a
period of less than 7 days.

###  3.1 Lifestyle Study through Social Media

We collected 233,046 Foursquare check-ins from 49,744 POIs from geo-tagged
tweets in NYC, and 99,466 check-ins from 13,483 POIs from ROC. Foursquare also
provides around 600 POI categories, such as Arts & Entertainment, Home, etc. A
venue can be assigned to several categories, where one category can be a
subset of another. For example, Foursquare may assign both American Restaurant
and Restaurant to a single venue.

Due to the sparsity of direct Foursquare check-ins, we chose to extend these
activity records by applying a method used in [19]: for each geo-tagged tweet
located within a small distance (30 meters) of a POI, we count it as a check-
in from this POI. Through this process, we extend the number of check-ins to
1,028,016 for NYC and 971,660 for ROC. In order to study gender effect on
lifestyles, we employ the API of genderize.io to assign gender tags to each
user [20]. Genderize.io gives the probability of an individual being either
male or female given his or her username. We first filter out the users who
send less than 10 tweets during our sampling period, and obtain 12,960 users
in NYC and 10,576 users in ROC. We then feed the handles for these users’
Twitter accounts into the genderize.io API, and filter out gender tags with
low confidence (probability $<0.8$). From this, we aggregate a total of 3,493
male- and 3,508 female-labeled users (see Table I). Other work has predicted
users’ gender using tweet contents [27], profile information, and profile
pictures [28]; however, given the complexity of these methods, and the
reasonable accuracy of our approach, we exclusively used the genderize.io API
to assign gender labels.

| NYC | ROC
---|---|---
Foursquare Check-ins | 233,046 | 49,744
Foursquare venues | 99,466 | 13,483
Extended Check-ins | 1,028,016 | 971,660
Total # of users | 12,960 | 10,576
Male users | 1,690 | 1,491
Female users | 1,803 | 2,017
TABLE I: Number of users in our data set, by city and gender. We only assign
gender labels to users when a high confidence in gender classification is
achieved, so the genders of many users are considered unknown.

Two key points should be verified to ensure the quality of our data set:

Figure 1: Plot of Complementary Cumulative Distribution Functions for the
numbers of check-in amount of POIs. This plot reports the probabilities of a
POI’s check-in volume exceeds a given amount in three data sets.

1) Only 20% to 25% Foursquare accounts are linked with Twitter [29]. Check-ins
collected from tweets form a subset of all the Foursquare records. We need to
ensure that our data set should have a similar distribution to the original
Foursquare data. To validate the applicability of our extension method, we
plot the probability of the amount of visits as a function of the amount of
POIs as Noulas et al. did in [18] in Fig. 1. It shows that the extended data
set not only preserves the long-tailed characteristic, but also shortens the
gap between the original Foursquare data and its subset that is extracted from
tweets.

Figure 2: Percentages of check-ins from top 10 visited POI categories.

2) In order to obtain datasets of comparable size for the two cities, we
collect tweets in NYC for a one-month period, and ROC for a one-year period.
The length of the time period for tweet collection is different in NYC and ROC
data sets. Tweets from NYC were posted during June 2012, while tweets from ROC
were posted from July 2012 to June 2013. In Fig. 2, we plot the percentage of
check-ins from the top 10 most frequent check-in categories. Note that we
eliminate duplicate categories. For example we omit “Restaurant” (ranked 3rd)
since we have “American Restaurant” in the first place. The portions of check-
ins from most categories remain at stable levels throughout the year. This
observation implies that the distribution in one month could approximately
represent the remaining months of a year. One exception is the category of
University, which shows a decrease from May to August. This coincides with the
summer break of universities.

##  4 Lifestyle difference at city level

Figure 3: Box plot of visiting frequency of Bar, Church, Drugstore, Gas
Station, Grocery, Park, Restaurant and Supermarket, aggregated over both
cities. Figure 4: Comparison between visiting frequencies of 9 POI categories
in big cities and small cities. The yellow boxes are the frequencies for NYC
and the green ones are for ROC.

###  4.1 Visiting Frequency of POIs

The visiting frequency of for a location is defined as the number of visits
(check-ins) divided by the number of unique visitors. In other words, visiting
frequency is the average visits per visitor to a location. This metric
measures the degree of relevance of a POI to people’s daily life. The higher
visiting frequency is, the more relevant the POI is to a person’s lifestyle.
“Home”, for example, as one of the most important locations to individuals’
lives, has a very high visiting frequency. Most of the check-ins at home are
performed by family members or friends, so the visiting frequency of home is
very high. On the contrary, a public location such as bar, usually has a lower
visiting frequency.

Regarding visiting frequency, we have two interesting observations:

  * •

Each POI category has a specific range of visiting frequency, which is clearly
indicative of differing functionality between different POIs in people’s daily
life.

  * •

Some categories show different ranges of visiting frequency in cities with
different sizes. This help us to examine the different lifestyles at the city
level.

####  4.1.1 Visiting frequency range of POI categories

We plot the visiting frequency of several popular POI categories as a box plot
in Figure 3. This plot shows that categories that are highly related to daily
life are visited repeatedly, e.g. Church, Grocery, Drugstore and Supermarket
all have a high visiting frequency. The median visiting frequencies of all
categories is approximately 4, though some show very high visiting
frequencies. For example, the visiting frequencies for some churches reaches
12, indicating a high prevalence of Church in some people’s lives. As we
expected, the highest visiting frequencies appear in Home (with a median of
approximately 20 for both cities) and Office (with a median of approximately
10 for both cities), as shown in Figure 4 for details. These two are the most
frequently visited locations for most people. The visiting frequencies of Bar
and Restaurant are much lower with a median around 2.

####  4.1.2 Difference in visiting frequencies between NYC and ROC

In this section, we compare the visiting frequencies of categories in big
cities and small cities (see Figure 4). It is interesting that the visiting
frequencies for some categories in small cities are larger than those in big
cities, such as Restaurant, Bar, Supermarket and Drugstore. This may imply a
higher regularity of life in smaller cities – in other words, people in
smaller cities are more localized, with stricter routines. In big cities,
people have more options to go when eating (Restaurant), having fun (Bar) and
purchasing daily necessities (Supermarket and Drugstore). Therefore, these
places are generally visited less frequently in larger cities. While for other
categories such as Home, Office and Church, the visiting frequencies of two
types of cities are roughly the same. This makes obvious sense because the
lifestyles of working, returning home and religion should be similar under the
same cultural atmosphere.

###  4.2 Basic mobility patterns in big cities and small cities

It is interesting to study the fluctuation of residents’ activity over time in
terms of occurrence at POIs. We plot the 10 most popular POI categories on
weekdays and weekends separately for ROC and NYC. On weekends, the mobility
patterns of the two cities are similar (Figure 5(b) and Figure 6(b)). The
total check-in amount climbs rapidly to a high level around 10am and 12pm in
ROC and NYC, respectively. The activity levels remain constant until 9pm, when
a peak of check-ins appears in both cities, indicating a sudden increase of
mobility in weekends night. After the peak, the activity level in ROC moves
down quickly, while it remains at a high level through 2am in NYC. The 3 most
frequently visited POI categories on weekends for both cities are Bar,
American Restaurant, Home. Obvious divergence is present between the weekday
mobility patterns of the two cities (Figure 5(a) and Figure 6(a)). In big
cities, there are three peaks during a day appearing around 8am, 1pm and 9pm.
Similar pattern also appear in London according to [18], indicating roughly
the same mobility pattern between London and NYC. Among the three peaks, the
highest one is at night, which implies that night is the most active period in
large cities. In contrast, there is only one peak during a day at 10am in
smaller cities and the check-in amount drops significantly after that. It
reveals that during weekday nights, people in small cities are not as active
as those in large cities. During the nights in weekdays, people in small
cities prefer visiting Home, American Restaurant and Cafeteria, while in large
cities, Bar is much more popular during night, indicating that people in large
cities are more prone to indulge in copious recreation during the weekends.

(a) Rochester weekdays

(b) Rochester weekends

Figure 5: Stacked plot of the 10 most popular categories over weekdays and
weekends in Rochester. Categories are listed in the order of increasing
probability from the top down. The width of each fault indicates the
percentage of a POI category for a given time of day.

(a) New York City weekdays

(b) New York City weekends

Figure 6: Stacked plot of the 10 most popular categories over weekdays and
weekends in New York City. Categories are listed in the order of increasing
probability from the top down. The width of each fault indicates the
percentage of a POI category for a given time of day.

##  5 Mining Lifestyles with Matrix and Tensor Decomposition

###  5.1 Matrix Decomposition

The activities of a user, $a$, can be described with an $M$ dimensional
vector. For temporal patterns we set $M$ to 24 and values in the vector are
the activities of the user performed in each hour, i.e. the number of check-
ins. When we examine spatial patterns latent in individuals’ actives, $M$ is
set to be equal to the number of POI categories, and each element indicates
the amount of check-ins the person performed in a single POI category. We
refer to vector $a$ as an “activity vector” of a user.

We assume that a person’s activities are determined by the lifestyle(s) that
person lives. Formally,

$a=w\times L$

where L is a $k$ by $M$ matrix, recording $k$ latent lifestyles, and $w$ is a
coefficient vectors of $k$ dimensions, indicating the user’s preference to
each lifestyle.

To uncover and compare lifestyles that are commonly followed in different
cities, first we assemble the activity vectors of residents into a single
matrix for each city. We define

$A_{roc}=(a_{1},a_{2},...,a_{N_{roc}})^{T}$

where $a_{i}$ indicates the activity vector of a resident, and $N_{roc}$ is
the number of samples we collected from the Great Rochester area. Similarly,

$A_{nyc}=(a_{1},a_{2},...,a_{N_{nyc}})^{T}$

where $N_{nyc}$ is the number of samples we collected from the Greater New
York area. Second, we concatenate $A_{roc}$ and $A_{nyc}$ to obtain a complete
matrix

$A=(A_{roc},A_{nyc})^{T}$

where $A$ is a $(N_{roc}+N_{nyc})$ by $M$ matrix.

Third, we decompose $A$ into two matrix $W$ and $L$. $W$ is a $N$ by $k$
coefficient matrix, while $L$ is the lifestyle matrix we explained above.
Since non-negative matrix factorization (NMF) usually leads to interpretable
results [2], we applied it to complete the decomposition. Formally, we solve
the following optimization problem:

$\underset{W,L}{min}\frac{1}{2}\|A-WL\|^{2}_{F}\quad s.t.\quad L\geq 0,W\geq
0$

where $A\in\mathbb{R}^{(N_{roc}+N_{nyc})\times M}$,
$W\in\mathbb{R}^{(N_{roc}+N_{nyc})\times k}$, $L\in\mathbb{R}^{k\times M}$.
$\|X\|_{F}=(\sum_{i,j}|X_{ij}|^{2})^{-\frac{1}{2}}$ is the Frobenius norm,
$L\geq 0$ (or $W\geq 0$) requires that all components of $L$ (or $W$) should
be nonnegative. $L$ uncovers the lifestyles that people follow, while $W$
provides information about individuals’ preferences across these lifestyles.

Figure 7: Active time ranges of night owls, early birds and intermediates over
weekdays.

After decomposition, we split $W$ into smaller matrices, each of which records
a sample of lifestyles for various groups of people. On a city level, $W$ is
split into two smaller matrices, $W=(W_{roc},W_{nyc})^{T}$, where
$W_{roc}\in\mathbb{R}^{N_{roc}\times k}$ and
$W_{nyc}\in\mathbb{R}^{N_{nyc}\times k}$. At a finer granularity, $W$ consists
of four even smaller matrices: $W=(W_{roc\ male},W_{roc\ female},W_{nyc\
male},W_{nyc\ female})^{T}$. For a particular group of people, i.e. a
component matrix, the degree of preference for a lifestyle is defined as the
average of the coefficients of people in the group for the lifestyle. For
example, the preference to $i_{th}$ lifestyle of residents of New York City is
calculated by averaging the $i_{th}$ column of matrix $W_{nyc}$.

In the following sections, we report the temporal and spatial lifestyles found
from people’s activities, and compare the preferences to lifestyles in the two
cities.

###  5.2 Third-Order Tensor Decomposition

User activities may be analyzed across multiple dimensions simultaneously
using higher-order tensors. Tensors are a natural way to aggregate data across
multiple factors. Vectors and matrices are special cases of tensors, where
each vector $v$ of dimensionality $D$, it is true that $v\in\mathbb{R}^{D}$,
and for each matrix $M$ of dimensionality $D_{1}$ by $D_{2}$,
$M\in\mathbb{R}^{D_{1}\times D_{2}}$. Tensors generalize this to data
structures of arbitrary order, where vectors are of order 1, matrices of order
2. For tensor $T$ of order $d$, $T$ may be concisely described as:
$T\in\mathbb{R}^{D_{1}\times D_{2}\times\ldots\times D_{d}}$. To learn
temporal-spatial patterns of human activities, for example, we can aggregate
the data into a third-order tensor. In such a tensor, a person’s activities is
recorded as a matrix, of which dimensions are POI categories and hours of a
day. Decomposition on the tensor produces multidimensional knowledge on
lifestyles. In Fig. 8, we illustrate the tensor decompositio processes.

The most commonly used technique for tensor decomposition is CANDECOMP/PARAFAC
(CP) decomposition [30]. This algorithm decomposes a tensor of order $d$ into
$d$ separate matrices, each of dimensionality $k\times D_{j}$, where $k$ is
the number of components selected a priori and $D_{j}$ is the dimension for
the tensor’s $j^{th}$ order.

Figure 8: Visualization of tensor cube $T$, decomposed into component matrices
$W$, $L_{M}$, and $L_{P}$.

The formal optimization problem for this decomposition is:

$\underset{W,L_{M},L_{P}}{min}\|T-W(L_{M}\odot L_{P})^{\top}\|$

In this equation, $\odot$ represents the Khatri-Rao product. The Khatri-Rao
product may be considered as a column-wise Kronecker product $\otimes$ between
two matrices with equal numbers of columns $A=[a_{1},a_{2},a_{3}]$ and
$B=[b_{1},b_{2},b_{3}]$, where $A\odot B=[a_{1}\otimes b_{1},a_{2}\otimes
b_{2},a_{3}\otimes b_{3}]$. While $A$ and $B$ both have 3 columns here, this
can be generalized for any number of columns. Assuming that
$A\in\mathbb{R}^{M\times K}$ and $D\in\mathbb{R}^{N\times K}$, the Khatri-Rao
product matrix will be of dimensionality $MN\times K$.

To solve the optimization problem for CP decomposition, we use the alternating
least-squares (ALS) algorithm, originally proposed by [31, 32]. The specific
implementation of CP-ALS used is provided in the scikit-tensor toolkit.
222https://github.com/mnick/scikit-tensor At a high level, ALS incrementally
uses $W$ and $L_{M}$ to estimate $L_{P}$, then $L_{M}$ and $L_{P}$ to estimate
$W$, and so on, improving estimations of one matrix in each iteration.

As ALS monotonically decreases error rate for the optimization, the algorithm
is subject to getting trapped in local minima. Thus ALS is not guaranteed to
find an optimal solution, and results may depend heavily on initialization. In
our experience, it was found that using both singular vector and random
initializations converged to similar decompositions with similar error rates
when using a termination condition of $10^{-5}$ error improvement between
iterations.

Similar to our matrix decomposition methodology, we assume that individuals’
check-in activity may be decomposed into a weighted combination of lifestyle
factors stored in a matrix:

$t=w\ (L_{M}\odot L_{P})^{\top}$

where $L_{M}\in\mathbb{R}^{k\times M}$, and $L_{P}\in\mathbb{R}^{k\times P}$,
each recording $k$ latent lifestyles, where again $w\in\mathbb{R}^{k}$ is a
coefficient vector for a single user. $L_{M}$ reveals the first dimensional
characterizations of each lifestyle component, and $L_{P}$ the second
dimensional characteristics. As with our matrix decomposition framework, we
consider weight matrix $W$ as a concatenation of four smaller matrices
according to city and gender. However, in the work presented here, we found no
significant differences in mean component weights across these demographics.

Full tensors $T_{i}=\\{t_{1},t_{2},\ldots,t_{N}\\}$ that we consider
concatenate lifestyle matrices $t$ across all users. In this work, we present
an analysis of two third-order tensors $T_{1}$ and $T_{2}$, such that
$T_{i}\in\mathbb{R}^{N\times M\times P}$. $N$ indexes check-in counts by user
id and $P$ indexes by category. Only the 100 categories with the highest
number of check-ins are used, so $P=100$ for both $T_{1}$ and $T_{2}$. $T_{1}$
indexes by times of day as well, so $M=24$. $T_{2}$ indexes instead by days of
the week, so $M=7$.

The first tensor, $T_{1}\in\mathbb{R}^{N\times 24\times 100}$, will allow us
to examine joint spatial-temporal lifestyles, indicative of user’s locational
behavior at various times of the day. Trivially, we might find components of
user check-in at bars and pubs, with greater weight assigned to night hours
than to the morning or afternoon. The second tensor,
$T_{2}\in\mathbb{R}^{N\times 7\times 100}$, will be conducive to locational
lifestyles with distinct trends across the work week, through the weekend. For
example, we might see lifestyles of individuals visiting restaurants and
entertainment venues later in the week, with less weight assigned to Monday,
Tuesday, and Wednesday.

##  6 Temporal Aspects of Lifestyle

Figure 9: Active time ranges of night owls, early birds and intermediates over
weekends.

Analogous to ETs, MTs, and NTs in the circadian topology literature, we
classify people’s work and rest habits into three categories: night owls,
people who tend to stay up until late at night; early birds, people who
usually get up early in the morning and go to bed early in the evening; and
intermediates, people who have schedules between night owls and early birds
[9].

Interestingly but not surprisingly, our approach provides support for these
three common temporal lifestyles. Moreover, we are able to provide a precise
description of activity level along time of day for each lifestyle. We study
weekday and weekend separately to gain a better understanding of people’s
lives.

Let $A_{weekday}$ be a $(N_{roc}+N_{nyc})$ by $M$ matrix, where $M$ equals to
24. A component $a_{ij}$ in the matrix denotes the $i_{th}$ user’s total
number of check-ins during $j_{th}$ hour of weekdays. Similarly,
$A_{weekend}$, also a $(N_{roc}+N_{nyc})$ by $M$ matrix, records the
activities of users on weekends. We set $k$ as 3 to align with the number of
predefined categories, and then employ matrix decomposition on $A_{weekday}$
and $A_{weekend}$, respectively. The results are $L_{weekday}$ and
$W_{weekday}$ for $A_{weekday}$; $L_{weekend}$ and $W_{weekend}$ for
$A_{weekend}$. We first plot the result matrices $L_{weekday}$ and
$L_{weekend}$ in Fig. 7 and Fig. 9.

Figure 10: Average weight on night owls, early birds and intermediates of male
and female residents of a small and big city over weekends.

Early birds: In weekdays, early birds’ days start around 7 am, and they are
most active around noon. After that, their activities decrease, and then
vanish gradually in the night around 10 pm. The distribution of activity over
time in weekend is similar for early birds, except the increase and decrease
of activities in weekend is faster, which leads to a sharper peak around 12
pm.

Night Owls: For night owls, we observe two active periods in a day. Their
activities start from 10 am for both weekdays and weekends. We observe the
first small peak appears at 2 pm, but is not comparable to their active level
during night. After the inactive daytime, the activities of night owl rocket
from 10 pm and achieve maximum at late night (1 am in weekdays and 2 am
weekends). Their activities vanish in the early morning (6 am).

Intermediates: People who are neither early birds nor night owls usually start
their day in the late morning (10 am). On weekdays, the active level increases
gradually in the afternoon and reaches the peak around 10 pm, and rapidly
decreases to zero around 2 am. On weekends, they are more active during
afternoon than on weekdays. Instead of a gradual increase, the active level
grows faster after being activated (11 am) and remains high till the peak of
night (11 pm).

The results extracted through data agree with the time ranges defined by
traditional studies. We compare our results with those from previous human
Circadian Rhythm [33].

In table II we list the time ranges of wake up time, sleep time and most
active time of the corresponding types (morning-type, evening-type, neither-
type) in the morningness-eveningness questionnaire (MEQ) [12], to compare with
these time ranges we list the percentage of activity during the same time
range of each lifestyle decomposed from our data using NMF. We define these
three time ranges as: from the early morning (5 am), the time range of the
first $\sim 15\%$ of activities is defined as “get up”, the next $\sim 70\%$
is defined as “most activity” and the final $\sim 15\%$ is defined as “go to
bed”. These percentages are not exact, and a small amount of activity is
present between the “go to bed” and “wake up” time ranges. For most time
ranges, our results generally agree with those from previous work, e.g. “get
up” time range, and “go to bed” time range for the Early Bird and Intermediate
lifestyle.

All the “most active” ranges in our findings are later than the previous
assessments, and the “go to bed” for Night Owl is much later than that of the
evening-type in previous work. Our explanation for these differences is
twofold: Firstly, we believe that as a general trend people’s activities shift
a lot into night in modern times when compared with the year when the seminal
previous work was done (1976) [9]. Secondly, individuals’ behaviors in our
model are modeled by a weighted combination of “lifestyles”, whereas in the
MEQ paradigm, an individual is assigned to a single, discrete “type”. As a
consequence, our “lifestyle” patterns should capture more distinctive work-
rest activities a single individual might follow as a subset of all his or her
behaviors, whereas each MEQ “type” should capture the aggregate work-rest
patterns for all of an individual’s behaviors. For example, an individual in
our model might be a “night owl” on the weekends and an “early bird” on the
weekdays, while in the MEQ model this individual would be considered as either
a morning-type or an evening-type.

| MEQ | Our results
---|---|---
Early Bird |
Get up | 5:00 am - 7:45 am | 6:00 am - 8:00 am
Most active | 5:00 am - 10:00 am | 7:00 am - 2:00 pm
Go to bed | 8:00 pm - 10:15 pm | 8:00 pm - 10:00 am
Inter |
Get up | 7:45 am - 9:45 am | 8:00 am - 10:00 am
Most active | 10:00 am - 5:00 pm | 2:00 pm - 8:00 pm
Go to bed | 10:00 pm - 12:30 am | 10:00 pm - 12:00 am
Night Owl |
Get up | 9:45 am - 12 pm | 10:00 am - 12:00 pm
Most active | 5:00 pm - 5:00 am | 9:00 pm - 1:00 am
Go to bed | 12:00 am - 3:00 am | 3:00 am - 6:00 am
TABLE II: Comparison between our method with traditional methods

$W_{weekday}$ and $W_{weekend}$ indicate the preference of each user to three
lifestyles on weekday and weekend, respectively. For each matrix, we first
split it to 4 smaller matrices according to user’s city (ROC or NYC) and
gender. Secondly, we calculated the average preference of each group to each
lifestyle. We plot the results in Fig. 10 and Fig. 11.

Figure 11: Average weight on night owls, early birds and intermediates of male
and female residents of a small and big city over weekdays.

Generally speaking, the average preference to night owl lifestyle on weekends
is significantly higher than on weekdays. Correspondingly, the average weight
of early bird and the intermediate style is significantly lower on weekends.
This indicates that people in both cities are more willing to stay active late
on weekends, while getting up early on weekdays. People live in big cities
usually have higher preference to the night owl type for both males and
females, while people tend to have higher weights on the early bird type in
small cities. This observation suggests that big cities are more active than
small cities during night. We did not observe significant difference between
two genders on their preference to temporal lifestyles in both two cities.
This agrees with previous work [10], in which the authors verify that daily
activity levels generally are not biased by gender.

###  6.1 Spatial Aspects of Lifestyles

Individuals’ preferences towards specific locations are another important
indicator to their lifestyles. These lifestyles can be described as
combinations of several specific POI categories. For example, we observed the
co-occurrence of Home, Grocery and Gas Station in many people’s visiting
records; we could have the feeling that the people performed this pattern are
“home-originated”, since the places they visited are quite related to daily
life. Another commonly observed combination is Bar, Pub and Music Venue;
people following this pattern clearly tend to have much excitement (alcohol)
in their daily life. These movement patterns are conducive to understanding
individuals’ lifestyle preferences.

Hidden Patterns | 1st category | 2nd category | 3rd category | 4th category | 5th category
---|---|---|---|---|---
1, College | Residence Hall | Co-working Space | Lab | Rec Center | Wine Bar
2, Restaurant | American Restaurant | Grocery Store | Supermarket | Fast Food | Diner
3, Bar & Pub | Bar | Music Venue | nightclub | Lounge | Rock Club
4, Office | Office | Co-working Space | Building | Conf. Room | Bar
5, Home & Grocery | Home (private) | Supermarket | Grocery Store | Drugstore | Church
6, Entertainment | Arts & Entertainment | Baseball Stadium | Bar | Burger Joint | Concert Hall
7, Sports | Gym | Yoga Studio | Athletics & Sports | Spa | Fitness Center
8, Park & Outdoor | Park | Neighborhood | Scenic Lookout | Plaza | Beach
9, Hotel & Bar | Hotel | Lounge | Cocktail Bar | Roof Deck | Airport
10, Commute | Train Station | Subway | Train | Platform | Bus Station
TABLE III: 15 Hidden patterns with their assigned names and top 5 weighted
categories of POIs of each pattern.

We also employ the NMF method to detect these hidden patterns. Instead of a
temporal activity matrix, we decompose a spatial activity matrix $A$ in this
case. $A$ is a $(N_{roc}+N_{nyc})$ by $M$ matrix, where $M$ is the number of
categories of POIs. The decomposition generates two result matrices $L$ and
$W$. $L$ records the spatial lifestyles that are lived by the people in
different cities, $W$ contains the information of the preference of each
resident to these spatial lifestyles. $k$ is empirically set to 5 to achieve a
good tradeoff between granularity and interpretability.

We report the lifestyles extracted from the data in Table III. For each
pattern we list the top 5 weighted categories of POIs and assign a name to the
pattern. We can sense the clear connection between the POI categories within a
hidden pattern. Take pattern one as an example. The top three weighted
categories are College Residence Hall, Co-working Space and College Lab. This
is a common mobility pattern of college students. Pattern seven describes a
pattern of people who like to exercise, where the top three categories are
Gym, Yoga Studio and Athletics & Sports. For pattern ten, the top three
weighted categories are Train Station, Subway and Train. This is a typical
movement pattern of people who commute a lot.

It’s natural to see one’s behaviors as a combination of several lifestyles.
For example, for a college student we may find he/her lives first lifestyle in
Table III (College lifestyle) as well as the third (Bar & Pub lifestyle) and
the seventh (Sport lifestyle) with different weights. By “weight” we mean the
importance of a certain lifestyle to one’s daily life. Rows in $W$, indicated
by $w_{i}$, are such weight vectors. $w_{i}$ is a 15 dimensional vector,
indicating the quantified preferences of $i_{th}$ user to 15 spatial
lifestyles. In order to gain a group level understanding of lifestyles, we
employ a clustering method on $w_{i}$s. The center of each cluster denotes the
mean lifestyle combination for a group of individuals. Moreover, by analyzing
the component of a group we are able to determine the tastes for lives of
residents cities of different size. We set the number of clusters to 5
empirically.

Figure 12: Components and corresponding percentage of lifestyle 1 and the
people in this lifestyle.

We plot the components of two of groups of people and the percentages of males
and females from both cities. Note that all ratios are normalized by the
number of users in each city. For the people who are in the first group (Fig.
12), home is the absolute center of their life. Additionally, this group is
comprised more of people from small cities (56%) than of those from large
cities (44%). People in the second group (Fig. 13) tend to visit office and
entertainment venues more often. People in large cities (73%) prefer this
lifestyle more than in small cities (27%). These results suggest that for
people in small cities, home is a prominent location in life; while in large
cities, people tend to spend more time at office.

Figure 13: Components and corresponding percentage of lifestyle 2 and the
people in this lifestyle.

##  7 Composite Aspects of Lifestyles

Some lifestyle patterns may be seen a combination of individuals’ daily,
weekly, and spatial habits. In this section, we consider the analysis of
tensors $T_{1}$ and $T_{2}$. Each tensor $T_{i}\in\mathbb{R}^{N\times M\times
P}$ may be factorized into any number of components $k=[2,min\\{N,M,P\\}]$ .
Recall that for both $T_{1}$ and $T_{2}$, $N$ indexes check-in counts by user
id and $P=100$ indexes by category. For $T_{1}$, $M=24$ for indexing by time
of day, and for $T_{2}$, $M=7$ for days in a week.

There is a significant trade off that exists with choosing tuning parameter
$k$: with a smaller $k$, fewer lifestyle patterns may be identified, and some
components may be mixtures of multiple theoretically distinct lifestyle
patterns. With higher $k$, we run into issues of redundancy, where multiple
highly similar lifestyle patterns are extracted, and low interpretability,
where some extracted patterns include very disparate behaviors. For both
$T_{1}$ and $T_{2}$, we tested a wide range of possible $k$ values.

Individuals with fewer check-ins than some threshold $h$ were pruned to remove
outlier noise. In our experiments, we found very little difference between
many components when $h=5$ from when $h=30$. However, with $h=5$, some
patterns emerge more apparently, and components are more distinct overall.

Figure 14: Component weights $L_{M}$ by hour for tensor $T_{1}$; 5 out of
$k=12$ total components are shown. Component labels are added a posteriori,
and weights shown are normalized by min-max normalization. Each curve in the
upper part represents the trend of a POI category along 24 hours of a day.
Figure 15: Component weights $L_{M}$ by day for tensor $T_{2}$ with $k=5$.
Component labels are added a posteriori, and weights shown are normalized by
min-max normalization. Each curve in the upper part represents the trend of a
POI category along 7 days of a week.

###  7.1 Time-of-Day & Location

$T_{1}$ offers the most interpretable results of third-order tensor
decomposition, most clearly when $k=12$, shown in Fig. 14. Across a wide range
of values for $k$, we see a few distinct lifestyle patterns emerge. One
component assigns highest weight to the Arts & Entertainment category and
significantly lower for all others, with time-of-day beginning around 10am,
peaking at 9pm, and tailing off in the hours following midnight. This matches
the intuitive assumption that individuals usually visit these sort of venues
later in the day, primarily around evening and night times. The next component
assigns highest weight to the High School category, significantly lower for
all others, with time-of-day peaking significantly at 7am, and some additional
weight for the hours of 8am - 3pm. This range directly corresponds to the
standard school day for high school students in the U.S. Although NYC check-
ins are only collected for the month of June, the school year for New York
public schools continues through June 26th.

Two “college student” lifestyle patterns consistently emerge. The first
assigns the most weight to College Residence Hall, with time-of day gradually
peaking around 6am, and decreasing significantly from 8pm through 2am. The
second has highest weight on College Rec. Center, with time-of-day increasing
gradually from 9am to 9pm, peaking from 10pm to 2am, and tailing off quickly
thereafter. Both these lifestyles assign some weight to other college-related
POIs, for example College Lab, Co-working Space, and College Cafeteria. The
former of these seems to model the pattern “early bird” college students,
where the latter models “night owls”.

Finally, we also see a Gym lifestyle emerge, where Gym is assigned a very high
weight, and all other categories are assigned low weight. This lifestyle
pattern also gives high weight to most hours of the day, with a significant
dip from the hours of 10pm through 5am. This also makes intuitive sense, since
it is unlikely that many people go to the gym during these hours.

###  7.2 Day-of-Week & Location

We find noteworthy patterns when decomposing $T_{2}$ into 5 components, shown
in Fig. 15. We see one pattern with highest weight assigned to category Bar,
high weights to Cafe, American Restaurant, Private Home, and Music Venue, and
moderate weights to a number of similar categories such as Rock Club; this
pattern assigns very little weight to Monday and Tuesday, and the highest
weight on Sunday. Common sense tells us that people work harder the first few
weekdays, and usually visit recreational venues such as these more later in
the week. It is not surprising that the highest weight is assigned on a
weekend night, since this includes check-ins both the night of that day, and
activities past midnight from the night before.

We also see a component with very high weight assigned to Arts &
Entertainment, low weight assigned to other categories; very low weight is
assigned to Monday, no weight to Sunday, and relatively uniform weights across
other days of the week. Many entertainment venues are closed on Sunday, and
fitting with the common notion that people recreate less on Mondays. Two
distinct “college student” patterns emerge with as few as 4 components, both
with considerably higher weights assigned to weekends than weekdays. It is
plausible that college students, especially in NYC, go off-campus to engage in
alternate lifestyle behaviors during the weekdays when they’re not in class,
and stay on campus to study during the weekends.

The fifth and final component we see assigns highest weight to the Office
category, and weights an order below to a few categories: American Restaurant,
Pub, and Deli. This pattern has a less clear interpretation; one might
speculate that individuals who go to the office on weekends might develop a
habit of checking into social media outlets during these irregular visits, but
not during their daily grind during the weekdays.

##  8 Conclusion and Future Work

In this paper, we extensively study the differences between lifestyles of a
big city (NYC) and of a smaller city (ROC) using social media data. We extract
work-rest habits and lifestyles from user activities. Instead of assigning
people to qualitatively defined work-rest classes, we apply NMF techniques to
discover latent patterns of human diurnal preference. The extracted latent
patterns correspond well to the intuitively defined classes. Also using NMF,
we find hidden features of human movement preference. We then group residents
of two cities into lifestyle clusters based on the weights of hidden features
and analyze the difference between the two distinctive cities. Moreover,
tensor decomposition techniques are applied to find composite life patterns in
our work. Clear and quantifiable differences are found in the lifestyles of
large and small cities.

Lifestyle is a broad, imprecise concept that covers a multitude of aspects of
human behavior, and social media is a flawed representation of individuals’
daily behaviors. These challenges present a number of exciting avenues for
future work: investigating what sorts of lifestyles can be categorized
culturally, or by other sociological factors such as job and age group;
establishing what behaviors can be more characteristic of specific lifestyles;
and exploring the relationship of individuals’ social media activities to
their daily behaviors. Regarding the last point, it may be that certain
individuals’ social media postings are strongly indicative of their behaviors,
whereas other individuals’ postings may be a biased sample of their
activities.

We would like to introduce more dimensions in our future work, such as other
demographic dimensions including income, age and race, as well as adding more
cities to the investigation. Presently, we are collecting data from the San
Francisco Bay Area so that we may compare lifestyle behaviors between
inhabitants of east and west coast cities, and relate our findings to previous
research [34]. We also plan to combine multiple social media data sources to
gain a comprehensive understanding of human behaviors in the big data era
using large-scale social media data.

##  9 Acknowledgment

We would like to thank the support from the New York State through the Goergen
Institute for Data Science, as well as Xerox Foundation.

## References

  * [1] B. Cox, M. Blaxter, A. Buckle, N. Fenner, J. Golding, M. Gore, F. Huppert, J. Nickson, S. M. Roth, J. Stark _et al._ , _The health and lifestyle survey. Preliminary report of a nationwide survey of the physical and mental health, attitudes and lifestyle of a random sample of 9,003 British adults._ Health Promotion Research Trust, 1987.
  * [2] D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” _Nature_ , vol. 401, no. 6755, pp. 788–791, 1999\.
  * [3] R. Garcia-Gavilanes, D. Quercia, and A. Jaimes, “Cultural dimensions in twitter: Time, individualism and power,” _Proc. of ICWSM_ , vol. 13, 2013\.
  * [4] T. H. Silva, P. O. de Melo, J. Almeida, M. Musolesi, and A. Loureiro, “You are what you eat (and drink): Identifying cultural boundaries by analyzing food & drink habits in foursquare,” _arXiv preprint arXiv:1404.1009_ , 2014.
  * [5] A. Sadilek and H. Kautz, “Modeling the impact of lifestyle on health at scale,” in _Proceedings of the sixth ACM international conference on Web search and data mining_. ACM, 2013, pp. 637–646.
  * [6] T. Budesa, E. Egnor, and L. Howell, “Gender influence on perceptions of healthy and unhealthy lifestyles,” 2008.
  * [7] C. Randler, “Morningness–eveningness and satisfaction with life,” _Social Indicators Research_ , vol. 86, no. 2, pp. 297–302, 2008.
  * [8] J. Shan, F. Joseph, and G. Marta, _ANALYZING HOUSEHOLD LIFESTYLES, MOBILITY AND ACTIVITY PROFILES:A CASE STUDY OF SINGAPORE_ , 2012, available at http://humnetlab.mit.edu/wordpress/wp-content/uploads/2012/10/jiang_TRB2013_v7jf.pdf.
  * [9] J. A. Horne and O. Ostberg, “A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms.” _International journal of chronobiology_ , vol. 4, no. 2, pp. 97–110, 1975.
  * [10] B. K. Merritt and A. G. Fisher, “Gender differences in the performance of activities of daily living,” _Archives of physical medicine and rehabilitation_ , vol. 84, no. 12, pp. 1872–1877, 2003.
  * [11] M. I. Von Bothmer and B. Fridlund, “Gender differences in health habits and in motivation for a healthy lifestyle among swedish university students,” _Nursing & health sciences_, vol. 7, no. 2, pp. 107–118, 2005.
  * [12] J. A. Horne and O. Ostberg, “A self-assessment questionnaire to determine morningness-eveningness in human circadian rhythms.” _International journal of chronobiology_ , vol. 4, no. 2, pp. 97–110, 1976.
  * [13] C. S. Smith, C. Reilly, and K. Midkiff, “Evaluation of three circadian rhythm questionnaires with suggestions for an improved measure of morningness.” _Journal of Applied psychology_ , vol. 74, no. 5, p. 728, 1989.
  * [14] J. Taillard, P. Philip, and B. Bioulac, “Morningness/eveningness and the need for sleep,” _Journal of sleep research_ , vol. 8, no. 4, pp. 291–295, 1999\.
  * [15] A. Adan, S. N. Archer, M. P. Hidalgo, L. Di Milia, V. Natale, and C. Randler, “Circadian typology: A comprehensive review,” _Chronobiology international_ , vol. 29, no. 9, pp. 1153–1175, 2012.
  * [16] T. H. Monk, D. J. Buysse, J. M. Potts, J. M. DeGrazia, and D. J. Kupfer, “Morningness-eveningness and lifestyle regularity,” _Chronobiology International_ , vol. 21, no. 3, pp. 435–443, 2004.
  * [17] B. P. Hasler, J. J. Allen, D. A. Sbarra, R. R. Bootzin, and R. A. Bernert, “Morningness–eveningness and depression: Preliminary evidence for the role of the behavioral activation system and positive affect,” _Psychiatry research_ , vol. 176, no. 2, pp. 166–173, 2010.
  * [18] A. Noulas, S. Scellato, C. Mascolo, and M. Pontil, “An empirical study of geographic user activity patterns in foursquare.” _ICWSM_ , vol. 11, pp. 70–573, 2011.
  * [19] A. Sadilek, S. Brennan, H. Kautz, and V. Silenzio, “nemesis: Which restaurants should you avoid today?” in _First AAAI Conference on Human Computation and Crowdsourcing_ , 2013.
  * [20] S. Abbar, Y. Mejova, and I. Weber, “You tweet what you eat: Studying food consumption through twitter,” _arXiv preprint arXiv:1412.4361_ , 2014.
  * [21] J. Cranshaw, E. Toch, J. Hong, A. Kittur, and N. Sadeh, “Bridging the gap between physical location and online social networks,” in _Proceedings of the 12th ACM international conference on Ubiquitous computing_. ACM, 2010, pp. 119–128.
  * [22] J. Sang, T. Mei, and C. Xu, “Activity sensor: Check-in usage mining for local recommendation,” _ACM Transactions on Intelligent Systems and Technology (TIST)_ , vol. 6, no. 3, p. 41, 2015.
  * [23] B. Wu, T. Mei, W.-H. Cheng, and Y. Zhang, “Unfolding temporal dynamics: Predicting social media popularity using multi-scale temporal decomposition,” 2016.
  * [24] S. A. Golder and M. W. Macy, “Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures,” _Science_ , vol. 333, no. 6051, pp. 1878–1881, 2011.
  * [25] K. Park, S. Lee, E. Kim, M. Park, J. Park, and M. Cha, “Mood and weather: Feeling the heat?” in _ICWSM_ , 2013.
  * [26] Y. Zheng, T. Liu, Y. Wang, Y. Zhu, Y. Liu, and E. Chang, “Diagnosing new york city’s noises with ubiquitous data,” in _Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing_. ACM, 2014, pp. 715–725.
  * [27] H. A. Schwartz, J. C. Eichstaedt, M. L. Kern, L. Dziurzynski, S. M. Ramones, M. Agrawal, A. Shah, M. Kosinski, D. Stillwell, M. E. Seligman _et al._ , “Personality, gender, and age in the language of social media: The open-vocabulary approach,” _PloS one_ , vol. 8, no. 9, p. e73791, 2013.
  * [28] D. Quercia, M. Kosinski, D. Stillwell, and J. Crowcroft, “Our twitter profiles, our selves: Predicting personality with twitter,” in _Privacy, Security, Risk and Trust (PASSAT) and 2011 IEEE Third Inernational Conference on Social Computing (SocialCom), 2011 IEEE Third International Conference on_. IEEE, 2011, pp. 180–185.
  * [29] L. Rao, _Foursquare Now 3 Million Strong_ , 2010, available at http://techcrunch.com/2010/08/29/foursquare-now-3-million-strong/.
  * [30] T. G. Kolda and B. W. Bader, “Tensor decompositions and applications,” _SIAM review_ , vol. 51, no. 3, pp. 455–500, 2009.
  * [31] R. A. Harshman, “Foundations of the parafac procedure: Models and conditions for an” explanatory” multi-modal factor analysis,” 1970.
  * [32] J. D. Carroll and J.-J. Chang, “Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition,” _Psychometrika_ , vol. 35, no. 3, pp. 283–319, 1970.
  * [33] A. Shahid, K. Wilkinson, S. Marcu, and C. M. Shapiro, “Morningness-eveningness questionnaire,” in _STOP, THAT and One Hundred Other Sleep Scales_. Springer, 2012, pp. 231–234.
  * [34] R. Fincher and J. M. Jacobs, _Cities of difference_. Guilford Press, 1998.

|  Tianran Hu is a second year PhD student in the Computer Science Department
of University of Rochester, advised by Prof. Jiebo Luo. He obtained his M.S.
degree in 2012 from Hong Kong University of Science and Technology and B.Eng.
in 2010 from Sichuan University in China. His research interests are in social
media and data mining.
---|---
|  Eric Bigelow is a first year M.S. student in the Computer Science
Department at the University of Rochester. Previously, he obtained a B.S. in
Brain & Cognitive Sciences and a B.A. in Computer Science from the University
of Rochester in 2014. His research interests span applications and theoretical
foundations of machine learning.
---|---
|  Jiebo Luo joined the University of Rochester in Fall 2011 after over
fifteen years at Kodak Research Laboratories, where he was a Senior Principal
Scientist leading research and advanced development. He has been involved in
numerous technical conferences, including serving as the program co-chair of
ACM Multimedia 2010 and IEEE CVPR 2012. He is the Editor-in-Chief of the
Journal of Multimedia, and has served on the editorial boards of the IEEE
Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions
on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology,
Pattern Recognition, Machine Vision and Applications, and Journal of
Electronic Imaging. Dr. Luo is a Fellow of the SPIE, IEEE, and IAPR.
---|---
|  Henry Kautz is the Robin & Tim Wentworth Director of the Goergen Institute
for Data Science and Professor in the Department of Computer Science at the
University of Rochester. He has served as department head at AT&T Bell Labs in
Murray Hill, NJ, and as a full professor at the University of Washington,
Seattle. In 2010 he was elected President of the Association for Advancement
of Artificial Intelligence (AAAI). His research in artificial intelligence,
pervasive computing, and healthcare applications has led him to be honored as
a Fellow of the American Association for the Advancement of Science (AAAS),
Fellow of the Association for Computing Machinery (ACM), and Fellow of the
AAAI. He holds the Computers & Thought Award from the International Joint
Conference on Artificial Intelligence. In 2013, he received the Ubicomp
10-Year Impact award for his paper ”Inferring High-level Behavior from Low-
level Sensors”.
---|---