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Solitary or Sociable?

Segmenting Mobile Micro-Moments with Social Media Geoforensics

ABSTRACT

The consumer’s shift to mobile has introduced greater complexity in their journeys to reach decision goals. As a

result, marketers are interested in learning about micro-moments that help understand consumers’ mindsets and states

of momentary needs. In this paper, authors demonstrate a way to segment mobile micro-moments experienced by

consumers by extracting insights from location based social media data from 9 types of locations and six cities. The

method of extracting tweets by location, and interpreting their meaning and significance based on the activity space

the tweet was generated from, and the user visit history across multiple locations is referred to as social media

geoforensics. In the process they design two metrics. The first metric is the outward-inward directedness ratio, which

reflects the number of tweets store-visitors post about their immediate surroundings as a ratio of the number of

personal tweets they post about themselves. The second metric is venue variability, which relates to the diversity of

check-ins users have on Foursquare. The higher outward-inward ratios correlate with increased company of friends,

tweets and sociability, and the more diverse check-in portfolios show greater category connectedness of users.

Findings demonstrate that different types of locations have different degrees of outward-inward ratios, and

differences in category connectedness. Based on these findings, authors construct a sociability-connectedness matrix

to segment micro-moments by types of location.

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Introduction

The ubiquitous use of mobile devices and social media has proliferated across the world. The diffusion of portable

electronic communication has expanded the window of access between businesses and consumers. Users are now

actively reachable anytime, anywhere, creating both opportunities and challenges for marketers. While the constant

availability appears to be an opportunity, the irritation and perceived intrusion of interruptive push advertising also

constitute substantial reasons for ad avoidance. Marketers address this problem as one of understanding micro-

moments, which are defined as those “critical touchpoints within a consumer’s journey that add up to become a

destination” (Adams, Burkholder, Hamilton, 2016). Though Google identifies four types of micro moment segments

based on what users want to find, do, buy or navigate to as demonstrated by search terms, digital marketers consider

search, email, display, social and native as relevant channels for capturing micro-moment information (Jebbit, 2016).

In this paper, authors suggest another way to view micro-moments based on voluntary disclosures or content shared

via location based social media.

When Red Roof Inn realized there were 90,000 passengers stranded every day due to flight delays, their marketing

team developed a way to track flight delays real-time and deploy targeted search ads for accommodation near airports

(Gupta, 2015). For example, ads that said “Stranded at the airport? Come stay with us!” or “I-need a- hotel - ASAP”.

They delivered with relevance on what people immediately needed. Though the leading component of micro-moment

marketing is local search and discovery, social media interactions also provide opportunities for discovering new

markets. As digital domains capture more and more granular, voluntary shares of consumer data, these revelations

add further to existing market opportunities. While Google primarily targets intent and context based on explicit

search terms, in this paper authors identify consumers’ engagement with their immediate surroundings based on the

content they post on social media and the complementarity of their store visits to other business categories based on

check-in history.

Mobile social media usage currently stands at 1.685 Billion of the 7.2 Billion global population. The increase in

penetration in mobile social media has been so significant that social media referrals have overtaken search traffic

volumes (European Publishers Council, 2015). Such rapid adoption has also enabled addition of new data such as

location co-ordinates to shared user generated content. This technological change has boosted the location based

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marketing industry, comprising of location triggered advertisements, notifications, location based applications,

check-in based games, contests, location search advertising, branded and embedded applications as well as proximity

marketing. All these changes have witnessed an increasing availability of users’ location based data.

This paper incorporates such hyperlocal data, i.e., users’ shared content and interaction history with their

surroundings on business premises to design metrics that aid marketers learn about customer mindsets regarding

immediate consumption experiences. Due to the forensic nature of collecting and connecting multiple virtual data

points embedded in physical location in an effort to build a holistic but hybrid (virtual-physical) picture of consumer

insights, we name this approach “Social Media Geoforensics.” Overall, social media geoforensics provides marketers

insights about consumer’s momentary mental states, and informs how to virtually identify, reach and assess

customers in different types of physical locations, contexts, orientations, and hence micro-moments.

The Existing State of Micro-Moment Marketing

Marketers have been busy gathering insights on the needs of the consumer as they proceed on different stages of the

consumer journey, especially looking to identify moments where consumers acquire information relevant to their

present or future decisions. Traditional consumer mental models followed by marketers accounted for a stimulus,

such as a newspaper ad a potential buyer is exposed to a “first moment of truth” (a term used by P&G), which

involved the customer going to the store, learning about and buying the advertised product and the “second moment

of truth”, which involved experiencing the product or service (Solis, 2015). Mobile internet allowed for disruptions

in this process, such as search activity between the stimulus and the first moment of truth. It could be a traveler

stranded at an airport searching accommodation nearby or a sports fan in a sporting goods store, watching videos

and reviews of new sporting equipment on his mobile phone. These moments introduced additional search and

information between ad exposure or stimulus and purchase, and have been called “Zero Moments of Truth” or ZMOT

(Lecinski, 2011). Companies have taken advantage of this perspective by tracking travelers and vacation consumers

by their dreaming (exploration) stage, planning stages, booking and experience stages through their search metrics

(Kraemer, 2016). A recent Google study revealed that 90% consumers are not sure about brands they want to buy,

and almost 33% purchased from brands different than the one they initially had in mind, because of the information

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provided (Adams, Burkholder, Hamilton, 2016). These numbers indicate that companies’ initiatives to track micro-

moments relevant to consumer needs should be profitable, as they can provide inroads into market opportunities.

Why We Need Social Media Geo Forensics (SMG)

The key to enhanced promotional effectiveness has always been an improved understanding of relevance to

consumers. Relevance makes marketing stimulus more accessible to the consumer (Baker and Lutz, 2000) and

triggers more motivated attention and comprehension processes (Celsi & Olson, 1988). If we view the value of

micro-moments to be that of finding the timeliest opportunities where marketers can learn about unfulfilled needs

relevant to consumers, the pursuit of discovering relevance faces two hurdles. First, relevance is both personal and

contextual (Grewal, Marmonstein and Sharma 1996), and traditionally the simplistic metric of proximity, such as

“near me” micro moments, has been used to determine contextual relevance and convenience. However, such

relevant promotions can also be perceived as intrusive when applied as push-based marketing (Banerjee and

Dholakia, 2008). While proximity marketers tend to deal with privacy more as a legal and compliance obligation, it

is important to note that privacy perceptions are a significant determinant of the effectiveness of contextually

“relevant” promotions. The “creepiness factor” (Barnard, 2014) explains the basis of ad-tailoring and customization

and how that can be perceived to be “creepy”.

If intrusiveness acts as a deterrent that prevents consumers from taking advantage of convenient proximal deals,

marketers often rely on hyperlocal data or rich contextual cues to identify user preferences. Whereas hyperlocal

advertising is known to address small specific homogeneous audiences based on location, hyperlocal data describes

information retrieved and used to target the same. This often leads to a second hurdle, because advertisers often

simplify their targeting practices with concrete information, such as demographics and crowdedness. However,

relying on simple concrete contextual cues doesn’t always help predict behavior, as human behavior is a product of

complex interactions between personal and contextual factors. For example, Andrews, Luo, Fang and Ghose (2015)

measured mobile ad responses across crowdedness and found, that counterintuitively, individuals were more likely

to respond to mobile ads in crowded trains than empty ones because it provided them relief and escape from the

anxiety of personal space invasion by strangers. These hurdles reinforce the need for complex and abstract behavioral

constructs, and need for marketers to understand which locations inspire such closely guarded, private mindsets or

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open, sociable states that one is more or less likely to feel intruded upon. One can be in a public location but still

feel like they are in a solitary, private zone. Similarly, one can be in a private location and still feel open and sociable.

Therefore, it is insufficient to target customers purely based on geographical co-ordinates, private or public nature

of spaces, or on simple contextual cues. It is important to understand what mindsets consumers are in when they are

embedded within an environment. This is the purpose of the present study.

While proximity is a geometric approach to understand location from a computational perspective, social media

geoforensics examines the richness of the location variable, combining the type of activity space, type of user, content

shared from there and number of friends that users are with to understand their needs more holistically. In the process

SMG allows us to measure specific locations on two dimensions. The first is how solitary or sociable the users

perceive the environment to be at any point of time, and the second is category connectedness, i.e., the extent to

which the visitors to a business participate in multiple categories of activity spaces on social media. In order to

generate the above dimensions, we design two metrics based on location based social media interactions. Further,

we design a “sociability-connectedness matrix” to decide how to market to customers in different types of locations

or spaces. Since human behavior is shaped by an interaction between individual traits and the situations they are

embedded in, marketing to specific locations based on behavior of users inside them can better address their needs

and make advertising more effective. In order to describe the process and rationale for designing the new metrics,

we first describe the variables, the data, and how they were collected.

Perceived Sociability of the Moment

The first aspect of the micro-moment relates to how solitary or sociable a mindset the consumer is in. Research on

voluntary self-disclosures in social media finds a connection between situational cues activating disclosure goals,

shared content and public-private divides (Bazarova and Choi, 2014). Some moments can trigger disclosures

selectively to trusted others (Pearce and Sharp, 1973), whereas certain moments can induce disclosures even to

complete strangers (Albrecht and Aldeman, 1987). Prior research on environmental influences on behavior (Belk,

1975) also suggests potential effects of physical locations, environments and situations on users’ behavior, which in

this case is social media check-ins and posts. Locations, atmospherics and ambiences, tend to have effects on

unconscious goals and activities they trigger, consumers perceived sociability, emotions and behaviors (Wardono,

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Hibino and Koyama 2012). Among those sharing information after checking into locations, some users tend to share

information that is personal to them, whereas others share information about people, events, products, objects and

experiences in their immediate surroundings. While the former appear to share information as if in a trusted, private

setting, the latter appears more sociable and open to exchange. Therefore, we categorize tweet content tied to

Foursquare check-ins as a product of the individual’s environmental embeddedness in their surroundings. The more

introspective and self-reflective their shared content is, the more we expect them to be in a private moment/mindset,

whereas the more externally oriented the content is, the more sociable we expect them to be.

Cross Category Connectedness

Another aspect to the micro-moment relates to whether an individual represents an experience or a service by itself

or as a part of a combination of many experiences. Research on consumption constellations reveals that products or

brands may not be consumed in isolation, but as a part of a broader network of purchases. Consumption activities

can be used to signify, or perform a social role, to exhibit symbolic, functional, aesthetic or sociocultural

complementarity with other activities or products (Englis and Solomon, 1996). For example, a person may go for a

movie and dinner with a loved one, where both consumption experiences recorded by the respective businesses as

separate are actually a part of one connected experience. Or, a person may visit the gym on weekdays and dine out

on weekends. Whether an activity is participated in as an isolated act or as a part of connected activities entrenched

in a single event or weekly subjective routines, is decided by the consumer’s nature. Identifying constellations of

consumption helps to understand the symbolic value of a business. It also identifies how the business is a part of a

consumer’s lifestyle. This, in turn, can improve the positioning of the business and its products, as well as help in

cross promoting products as parts of a bundle rather than market it on its own. From our data, we develop a measure

that helps us determine to what extent a particular activity or business category is made visible on the social media

platform as a consumption experience either in isolation, or as a part of a network of connected experiences.

Database and Variables in SMG

The data for this research was obtained by combining Foursquare check-in data with associated shared content from

Twitter. Foursquare is a LBSN (Location Based Social Network) application which has a large user base, and

requires users to check-in to locations, where the check-ins function as “consumption sensors” for marketers

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revealing consumer choice patterns at different times of the day (Banerjee, Viswanathan, Raman and Ying, 2013).

We collected data by monitoring specific locations within 6 geographical areas, and recording check-ins with shared

content from visitors. By monitoring these location-aware tweets we were able to extract and interpret the content

of the tweets.

Our sample comprised of 320 users who shared 2340 tweets from check-ins at 9 categories of venues, i.e. arts

and entertainment, college and university, food, nightlife, outdoors, professional, residence, shop and service as well

as travel and transportation. From the user information in the check-ins and tweets we were able to capture the user

name, location, gender and the number of people they were with at the time of consumption. Locations monitored

included restaurants, bars, museums, offices, residences, parks and many other venues across six regions in USA

including Central Park in New York (NY), Harvard Square in Cambridge (MA), Market Street Twitter office in San

Francisco (CA), Capitol Hill in Seattle (WA), Union Station in Chicago (IL) and Union Station in Washington D.C.

We chose these specific regions because of the high volume of check-ins emanating from them on foursquare. We

tracked check-ins from April to July. The variables we extracted, included the following:

i) City,

ii) Type of venue (Arts and Entertainment, Food,

etc.)

iii) Specific venue,

iv) Tweet content,

v) User id,

vi) Number of friends they were with at the time of

the tweet,

vii) Date and time

New Metrics

Based on the above data, we developed two new metrics. Both metrics give us some information about how the

location is perceived when viewed virtually. In unison, both metrics help marketers decide how to approach

consumers visiting different types of locations.

a. The first is Perceived Sociability of the immediate moment. Though most of the data we retrieve are from

public places, some locations seem to inspire more tweets that describe their immediate environment,

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company of friends and specific information about their surroundings that they feel comfortable sharing on

a public platform. Some other locations, in contrast, inspire voluntary self-disclosures which seem

autobiographical in nature, and personal trivia that would not be shared unless they felt they were in a closed,

private zone. In other words, some spaces appear more solitary, and some more sociable.

b. The second is Cross Category Connectedness. In some locations, users who tweet do so from only few and

similar types of venues. In these, users’ check-in into only food or some other type of venue, giving an

impression that the location is populated by users who specialize or focus on a particular type of activity

space. In some other locations, users who tweet demonstrate prior patterns of checking in to venues across

different categories, like outdoors, transportation, shopping, to give an impression that the location attracts

users who also connect to other business or activity categories.

Metric Design: Tweet Content Categorization

We classify tweets in the database into four distinct categories as depicted in Table 1. The uniqueness of this

categorization is that it interprets the typology of the tweet based on the physical context of the tweet.

i) Category one is the most “abstract” where the consumer is lost in thought, thinking about a topic that

doesn’t relate to himself or the environment he is in. This could relate to global news concerning politics,

sports, or economy, television shows or movies, or any other media related content. A tweet in this

category is likely to indicate that the tweeter is absorbed in some kind of media.

ii) Category two defines thoughts about the tweeter’s personal life, schedules, plans, and relationships,

social occasions among others. These updates contain trivia that may sound autobiographical to a reader/

viewer as if the user is sharing excerpts from his or her diary.

iii) Category three is about the geographical region, neighborhood, block around the person’s current

location. This could even include observations about the weather around a place.

iv) Category four is the most “concrete” category, where consumers’ attention is on his or her immediate

surroundings. A person sitting in a bar could tweet about the interior décor, the plate or the glass or

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silverware served in, or the crowd, or the music band playing, the smell of smoke. This category defines

how closely the individual is grounded in his immediate environment.

In essence, the same tweet related to a sports team could belong to different category if the user was watching

the game checked into a physical sports arena instead of watching it on TV in a sports bar. In the former case, it

would belong to category four, whereas in the latter, category one.

------------------------------INSERT TABLE 1 HERE-----------------------------------------------------

Based on the above categorization a frequency distribution of tweet content categories revealed that category two

and category four comprised 94.5% of the tweet content in this sample. Category four contained the outward tweets

which contained details about the immediate surroundings, whereas category two contained the inward,

autobiographical tweets containing self-disclosures. We further calculated an Outward-Inward ratio (O/I) that

indicates to what extent individuals checked into one location are inclined to tweet about outward directed content,

i.e. details from the physical surroundings, for every inward directed autobiographical tweet. The overall O/I ratio

for all six cities was 1.36, which means on an average we expect 1.36 outward tweets for every inward directed

tweet.

O/I Ratio= Category Four (Outward Directed) Tweets/ Category Two (Inward Directed) Tweets

------------------------------INSERT TABLE 2 HERE-----------------------------------------------------

About O/I Ratios: In other words, when the O/I Ratio is greater than the average (in this case, > 1.36), the locations

generate more tweeted content about specifics regarding the surroundings, i.e., products, events, and people inside

the location. And when < 1.36, the locations generate more tweeted content about individuals inside that location

and their personal lives. So a high O/I values indicate more sociable moments, whereas low O/I values indicate more

solitary and private moments.

Findings

When we examine an aggregate picture of different activity spaces plotted with number of friends, we find that travel

and transportation, which represents the mostly commuting crowd, demonstrates the most inward or autobiographical

tweets, implying the privacy of those moments. Though we visualize crowded commuter trains as public places, the

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tweets capture the mindset individuals occupy in those environments. Some prior research (Andrews, Luo, Fang and

Ghose 2015) has found that commuters use mobile internet in trains to avoid eye contact with fellow passengers.

Similarly, nightlife appears more private, and outdoors, shopping, and arts and entertainment appear to be more

public spaces.

In the following sections, we further discuss the O/I ratio and its relationship to other constructs. In Figures 1 and 2,

we illustrate the relationship between O/I ratios and number of friends’ visitors to these venues physically were with,

across cities and activities, to show that higher O/I ratios relate to more company of friends. In Figure 3, 4 and 5, we

illustrate the state of category connectedness across multiple activities and cities.

------------------------------INSERT FIGURE 1 HERE-----------------------------------------------------

Activities, O/I Ratios and Company of Friends: We can see from Table 2 that for all the cities, arts and

entertainment had consistent high values above 2.00. Also, travel and transport consistently displayed values lower

than one. Further, Figure 1 demonstrates transport and arts and entertainment to be the two extremes. When we

plotted some data points from the above table in order to further break down the aggregate numbers from the previous

figure to a visual of how individual cities score, this is what appeared. It validates our expectations that the O/I Ratio

indicates how private or public a mindset an individual is in, when present in those locations.

------------------------------INSERT FIGURE 2 HERE-----------------------------------------------------

Arts and Travel by City and Sociability: Figure 2 clearly shows two patterns. One, irrespective of city, arts and

entertainment locations generate more tweets, tweets generated from there are in the company of more friends, and

more directed to specific details regarding what is going on inside the location. Two, compared to arts and

entertainment, those in travel and transport locations tweet much less, their tweets are more about personal lives and

they are in company of fewer friends. The above validates our premise that using data from the content of tweets and

knowing the number of people users are with, one can gauge how public or private the location and activity is

perceived to be. Similarly, table 2 reveals that nightlife is also more of an inward-directed, private activity.

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Cross category connectedness findings: In order to measure the cross category connectedness of the business, we

first measure the venue-variability of the users, which indicates the category (Art-Entertainment, Food, and other

broader groups) diversity of their past check-in portfolio. We also measure the time variability of their check-ins to

locations to know to what extent their visits to multiple businesses are synchronized (same-day) or sporadic.

Variability computations for users are made based on an index of qualitative variation. The generated scores for

users are further organized in high medium and low categories and for each location, the proportion of number of

high to low score individuals checked in used as an estimate of cross-category connectedness.

------------------------------INSERT FIGURE 3 HERE-----------------------------------------------------

Activity by Category Connectedness: The visual above demonstrates that locations which are frequented by users

with high category connectedness, can be both disciplined, such as travel and transport, professional work, as well

as sporadic, such as outdoors. Also, food seems to be the least multi-category connected activity. In other words,

those who check into transportation locations such as commuter train stations connect to diverse activity spaces and

make them visible, whereas those who visit restaurants do not, they are exclusively foodies. This provides insights

about spaces like outdoor parks and train stations where users may be open to receiving cross promotions of other

categories of activity.

------------------------------INSERT FIGURE 4 HERE-----------------------------------------------------

Food and Outdoors by Category Connectedness and City: As we see in Figure 4 above, users from all cities who check in to

restaurants are less connected to other categories. In contrast, those who check into Outdoors and Recreation, across all cities,

exhibit more diverse check-in portfolios. Figure 5 shows that of those who check into restaurants, almost 37% of the check-ins

are exclusively in the food category, whereas of those who check-in to Outdoors, only about 18% of the check-ins are in the

Outdoors category. Hence the cross category connectedness of Outdoor is much higher than that of Food.

------------------------------INSERT FIGURE 5 HERE-----------------------------------------------------

Based on the analysis presented above, we make three major observations:

a. When categorized based on how embedded an individual is within an environment, majority of messages

belong to self-disclosures and context-related disclosures. The former is made in the presence of fewer

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friends, appear inward directed and solitary, whereas the latter are made in the company of more friends,

seem outer directed as well as sociable.

b. Arts and Entertainment makes the most sociable environment, whereas transportation and commuting makes

the most solitary.

c. Outdoors and Recreation seem to be the most cross-category-connected venues whereas Food seems to be

the least connected across different categories.

------------------------------INSERT FIGURE 6 HERE-----------------------------------------------------

Implications:

In travel, transport and nightlife locations, most consumers appear to be lost in introspection, and absorbed in

personal spaces and private domains despite being in public spaces. For travelers and commuters, it may have to do

with being between spaces and for nightlife visitors it may have to do with the time of the day. What is common

among both is that a large number of social media users that inhabit these spaces are there after work. Those who

inhabit these spaces should be reached through inbound marketing, such as search marketing and search engine

optimization. Popular keyword search terms and search goals can be identified and campaigns designed accordingly.

Despite being in solitary states, these individuals demonstrate higher category connectedness, so they may value

promotions and offers for new products and ideas that are unrelated from the purpose of the space they occupy. For

example, a commuter taking a train home after work may be interested in a new restaurant offering home delivery

near his or her residence.

The findings indicate that consumers experiencing arts, entertainment, outdoors and recreation, are the most sociable

and connected social media users of all spaces. As they are sociable, they can be reached by outbound marketing,

whether it is via text alerts or mobile coupons, for products related to other categories (dinner-movie deals) and

activities. Since they are also in the company of more friends, group deals and discounts should appeal to them as

well. Those checked into food locations and restaurants are the most attentive to the nature of activity at hand, i.e.

are dedicated foodies. They tend to describe what they are eating, interiors of the restaurants they are at and the

company of friends they are with. Being highly sociable, they are open to receiving outbound marketing, but given

the exclusivity of food-related locations they prefer to check in at, targeting them will be more effective for food

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related businesses. An interesting difference between outdoor/arts and food customers is the former are likely to

react positively to food related offers, but the latter may not be as interested in arts, or outdoors. It’s important to

note that the implications we draw are not meant to apply to all marketing for all consumers of those businesses, but

only i) marketing to specific locations and ii) the consumers who check-in on social media to indicate their presence

in those locations. Hence by segmenting micro-moments, social media geoforensics can inform marketers regarding

effectiveness of marketing tactics (inbound, outbound), timing as well as promotional design (group deals, cross-

promotions) based on content users share to represent themselves on social media.

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Appendix

Table 1: Tweet Categories

Check-

in and

Tweet Tweet Content Tweet Content Description

Tweet

Location

Tweet

Category

1

"Euro2012. Battle of the

(almost) insolvent. (@ The

Wren w/ 8 others)" News, Sports, Celebrities, Movies, Games

The Wren,

restaurant

Global

Media

Related

2

"Discussions of Lent. (@

Caribou Coffee)"

Family, friends, relationships, Schedules

about their daily or weekly plans, sequences

of tasks or events, day ahead or day so far,

Possessions, lifestyle, food habits, activity,

work related comments, need or occasion

for purchase

Caribou

Coffee Personal

3

"Great visibility today @

twin peaks" City, neighborhood, area, region, weather

Twin peaks

-outdoors Region

4

"Stopped over for some pie

and chicken biscuits"

Infrastructure, ingredients, products,

features, ambience, atmospheres,

transactions and events

Pies- n-

Thighs

restaurant

Venue

related

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Table 2: O/I Ratios and Sample Sizes by City and Venue Type

City/ Venue

Type

Arts

&

Ent

College &

University Food

Nightlife

Spot

Outdoors

&

Recreation Work Home

Shop

&

Service

Travel &

Transport

Cambridge,

Harvard Sq.

No of Tweets:

2.20 0.67 1.84 0.74 2.33 1.13 0.00 1.10 1.00

32 20 88 33 10 17 2 21 8

Chicago

No of Tweets:

3.00 3.00 1.64 1.00 1.14 1.57 1.00 0.85 0.90

60 20 156 54 15 36 2 37 40

New York

Central Park

No of Tweets:

2.17 1.50 1.07 0.98 1.33 0.91 3.00 1.19 0.68

73 5 184 87 21 61 4 79 37

San Francisco

No of Tweets:

3.07 NA 1.75 1.45 2.17 2.81 4.00 2.90 0.67

61 2 176 76 19 61 10 39 30

Seattle, Capitol

Hill

No of Tweets:

2.36 NA 0.71 0.54 7.00 1.31 NA 1.30 1.25

47 3 65 20 8 30 0 23 9

Washington

DC Union St

No of Tweets:

3.40 NA 1.07 1.00 1.13 1.48 0.00 3.00 0.70

44 1 85 48 17 52 2 16 39

17

Figure 1: Aggregate view of O/I Ratios, Number of friends and tweets

** In the Bubbles:

Letters represent activities

First number = O/I Ratio

Second number = Number of friends

Third number = Number of tweets

A is Travel and Transport

B is Nightlife

C is Professional work space

D is Food

E is Outdoors and Recreation

F is Residence

G is Shop and Service

H is Arts and Entertainment

18

Figure 2: Cities and Activities Plotted by O/I Ratio, Number of Friends, and Number of Tweets

** In the Bubbles:

Letters represent activities across cities

First number = O/I Ratio

Second number = Number of friends

Third number = Number of tweets

A is New York City Travel and Transport

B is Washington DC Travel and Transport

C is Chicago Travel and Transport

D is San Francisco Travel and Transport

E is Cambridge Travel and Transport

F is Seattle Travel and Transport

G is Cambridge Arts and Entertainment

H is New York City Arts and Entertainment

I is Seattle Arts and Entertainment

J is Chicago Arts and Entertainment

K is San Francisco Arts and Entertainment

L is Washington DC Arts and Entertainment

19

Figure 3: Activities Plotted by Category Connectedness, Venue and Time Variability

** In the Bubbles:

Letters represent activities

First number = Venue Variability

Second number = Time Variability

Third number = Number of tweets

A is Food

B is Residence, I is College (Both very small samples)

C is Nightlife

D is Arts and Entertainment

E is Shop and Service

F is Travel and Transport

G is Professional work places

H is Outdoors

20

Figure 4: Category Connectedness by City and Activity

** In the Bubbles:

Letters represent activities

First number = Venue Variability

Second number = Time Variability

Third number = Number of tweets

A is Food, Chicago

B is Food, San Francisco

C is Food, Seattle

D is Food, New York

E is Food, Cambridge

F is Food, Washington D.C.

G is Seattle, Outdoors and Recreation

H is Washington D.C., Outdoors and Recreation

I is New York City, Outdoors and Recreation

J is Cambridge, Outdoors and Recreation

K is San Francisco, Outdoors and Recreation

L is Chicago, Outdoors and Recreation

21

Figure 5: Share of Check-Ins by Activity- Food Vs Outdoor

22

Figure 6: Segmenting Micro-Momentary Mindsets by Locations

Low

Solitary, Connected

- Travel and Transport

- Nightlife

Sociable, Connected

Arts and Entertainment

Outdoors and Recreation

-

Solitary, Disconnected

-

Sociable, Disconnected

Food

-

Sociability-Connectedness

Cro

ss C

ateg

ory

Co

nn

ecte

dn

ess

High

Perceived Sociability of Environment Low High