Hi, everybody, my name is Amber DeJohn.
I am a first year PhD student here at U
of T, and Lauren asked me to talk to you
all today about a study I conducted
while I was an undergrad
at Michigan State University.
I did this study in collaboration
with a health geographer, Dr.
Amber Pearson, at MSU,
and then also in collaboration with
three researchers
from the Human Development
and Family Studies Department.
So, yeah, let's just jump into it.
So just a little bit about me.
I got my bachelors from Michigan State
University in political theory.
I had an additional major in economic
geography and a minor in G.I. science
at the time I conducted the study
but also for most
of my undergraduate career,
I was working in a GIS department.
So I was really into geographic
information systems and mapping,
which is kind of something that I
got to use for this study.
And then after my undergrad,
I moved to U of T directly
to get my masters in human geography
and I just finished that up
last August, so August 2019.
And I moved directly into my PhD,
which is where I'm at now.
So just before I get into the study
a little bit, I just wanna talk about why
/ when did I conduct this research?
Basically, I was a geography major
and I was taking a quantitative methods
course which was required to graduate.
And the professor kind of just announced
that she was looking to work
with undergraduate researchers
and that if we were interested in doing
undergraduate research,
we should contact her.
Which is what I did because I was
interested in going to grad school.
And we actually ended up applying
for an undergraduate research grant
that my undergraduate university,
Michigan State,
which is where I was at, funded.
And I think U of T has
a very similar program.
So just something for you to know,
I guess, if you're interested in that.
And we ended up getting funding.
So I worked on this over the summer in,
I believe, 2017.
And Dr.
Pearson's lab and then we published
it my fourth year of university.
We started publishing it.
Publishing takes a long time.
So that's kind of,
why / when I did this.
This project wasn't really,
I guess, my idea.
It was something that Dr.
Pearson had been wanting to do.
And so she kind of just
asked me to help with it.
And then I ended up doing
the majority of the work.
So that is why I am the first
author on this paper.
OK.
So just like a little bit into kind
of the background of this study and then
also how it kind of connects
to your greater course.
So the study is kind of along this line of
understanding how individuals use virtual
environments or virtual communities
to connect about mental health.
And there has been a series of work done
by non geographers about how people use
virtual or social media communities
to talk about difficult subjects.
So a study in 2017
by Berry et al.
looked at the hashtag
Why We Tweet Mental Health.
So they actually, the researchers in this
study actually asked people to use
the hashtag to tell them why they're,
you know, using Twitter to talk
about their mental health.
And they ended up finding that people
said that they felt a sense of community
online and they saw it as a space
for expression
and an area where they could get
empowerment from other people
experiencing similar things.
And it helped them to cope with their
mental health issues.
So that was a qualitative study.
And then in 2013,
another group of researchers in Korea did
a study of Twitter users with depression,
and they were trying to compare how
Twitter users, users with depression
view the platform relative to people
who do not have depression.
What they ended up finding is that Twitter
users who have depression view Twitter as
a space for social awareness
and emotional interaction.
And this was different than people
who did not have depression.
They kind of just viewed Twitter as
a place to chat with friends,
and they didn't quite see it as much
of an emotional support system as
Twitter users with depression.
And the users with depression said
that they felt less isolated and uplifted
when they used Twitter to talk
about their depression.
So you can kind of see
the formulation for why
we wanted to do this
study in part, is that these virtual
communities, these places where you can
connect with people who aren't necessarily
with you physically,
are growing in popularity and importance
for coping and connecting about mental
health issues and depression specifically.
But at the end of the day,
we're geographers, right.
If you're in this class,
you're a geographer.
I'm a geographer and we care about place
and virtual communities kind of pose
a challenge to geographers because
they're a-spatial in a lot of ways.
In that you can connect
with somebody anywhere.
They don't have to be with you physically.
So we also brought in an understanding
of place that is founded in Cutrona
et al.'s 2006 study about
community characteristics.
So that what Cutrona found is
that communities that are lacking
neighborhood amenities may have fewer
destinations for social interaction.
And I think that this one
is pretty self evident.
If you live in a place where there are no
public spaces, no public parks,
maybe no libraries and places
to go hang out with friends,
you might feel like you have nowhere
to socially hang out with people or
interact, which is kind of part of, again,
the rationale for this study,
which is that depression has many causes.
But one of the causes of depression is
lack of interaction
with other individuals.
So if you're living in a space where there
aren't destinations for interaction, you
might in turn be experiencing depression.
So this kind of all feeds into
the conceptualization of place.
In this study, which is that
even though there are these virtual
communities that are in some ways
a-spatial, people interact
with them in a place.
So when you send a tweet,
you're sending that tweet from somewhere.
And that's the geographic
component to this study.
So that kind of leads
into our research questions.
Can we predict Twitter usage to connect
about depression based on demographic
variables commonly
associated with depression?
Are there geographic differences
in Twitter users seeking connections about
depression and what community
characteristics are correlated with over
or under tweeting based
on our regression results?
That's a mouthful and I'm going to try
and break it down for you guys.
So before we start any study, you have to,
you know, assemble your data, especially
because this is a quantitative study.
So we needed a lot of information about
the communities that the people who were
tweeting were in and then information
about the demographic
profiles of those places.
So we compiled a bunch of census
statistics and then,
you know,
through just a lot of elbow grease,
we also found a lot of other really good
data sets, such as
the location of museums,
schools where people were voting... basically
where people were like...
we want to find where there were places
to go and where people were
active in their community.
That's kind of captured
in the percent active voter.
And then some of these other
characteristics can be attributed
to a vibrant social community, such as
maybe nonprofits or places of worship.
Vacant housing rates are kind
of the inverse of this,
where there's a lot of vacant housing
we would assume there'd be a less
vibrant social atmosphere.
So I just, I want to, you're supposed
to say your data sources anyway.
So I'm going and I'm giving them
to you for, you know, transparency.
But then also to say that this is probably
going to be if you're, if you're
doing any sort of research or
study like this, data collection takes
up a lot of time, most of your time.
So that's just something to be aware of.
But moving into our first question,
can we predict Twitter usage to connect
about depression based on demographic
variables commonly
associated with depression?
So the methods that we used... I know that
I just whatever data sources.
But I want to talk about
the tweets really quick.
So we pulled the tweets from Twitter.
It was a random sample.
And for the study that I've kind
of screenshot and put on the slide
here, they used the same
data set that we use.
This is a different study.
But what they did,
which actually came first,
is they did a qualitative
content analysis of the tweets.
So they actually looked at what the people
were saying in the tweets using
the hashtag My Depression looks like,
which was a trending tweet on Twitter
during Mental Health Awareness Week
in June 2016.
So they actually used something
like 3000 tweets for that study.
But as some of you might know,
if you use Twitter,
not everybody geotags their tweets,
which is to say not everybody provides
a location for where they're
sending the tweet from.
So, we actually had to cut down
that original dataset from three thousand
to one hundred and four tweets because
only one hundred and four tweets actually
had a geotag, which means those were all
the tweets that we were able
to actually place somewhere.
And again, this idea of place influencing
who uses social media was
at the core of our study.
So once we had our tweets,
we performed a Poisson regression,
which is just a fancy way of saying
we looked for relationships,
between where are those tweets were
or accounts of those tweets
and the demographic profile
to address our question.
So let me just really quickly,
I wanted to show you guys a map
of the tweets that we had.
So you can see that most counties,
these are counties don't have any tweets.
And that's not to say that there
weren't any tweets there.
There just weren't any
geotagged tweets there.
So we couldn't
we couldn't look at the tweets in those
locations, but most counties had one or
two and then some had in excess of of 3s.
I think New York is the place where we
had the most, which is twenty five
in a single county.
So we took those counts demonstrated
by the map and demographic
variables related to depression
and we estimated the relationship between
those variables with a regression.
So,
as you can see,
being aged 15 to 44, female, or living
alone had significant relationships with
tweeting to connect about depression.
White individuals and those living
below the poverty level did not
have statistically
significant relationships.
So I'm not going to get too
much into the numbers here.
But basically what we can take away
from this is that
the larger the proportion of a county's
population that is aged 15 to 44
and female, the higher the tweets
or the rate of tweets and then
the lower the percent of single person
households in a county,
the higher the rate of tweets.
So the first two have a direct
relationship and the third one
has a indirect relationship.
So that our next question moving forward
was are there geographic differences
in Twitter users seeking
connection about depression?
So what we did here,
this is really where they the second
half of the place issue comes in.
So first we took our regression results
that I just showed you on the last slide,
and we assigned tweeting index values
that were based on the error
from the regression model.
So if any of you have already taken your
quantitative methods course that's
offered in this department,
you'll know that regression models only
approximate relationships and sometimes
they don't do a great job.
An example of this would be if
a county in our study had two tweets.
But our model predicted that that
county would have five tweets.
So technically, that county
is under tweeting vis-a-vis
what our model says that there should be
there, given the fit of the model
and the observed data that we have.
So that's kind of what we did.
So TIV of one means that the county was
under tweeting relative
to what the model predicted.
And then a TIV of two
means tweeting as expected.
So the model predicted what we observed.
And then TIV three is the model predicted
a lower number of tweets
than what we observed.
So I hope that's not too confusing.
And technically
those are called residuals.
If you read the paper, you saw
that that term being thrown around.
But it's basically
the error from the model.
So then we took those tweeting index
values that we had that we developed
from the regression results.
And then we mapped them.
And that's seen on your screen here.
So on the right hand of your screen
and we were looking for geographic
variation in
the residuals basically.
So what you can kind of see is that more
of the under tweeting is
occurring on the coastline.
And then
on the kind of the western side of our
study region, there are more
counties tweeting as expected.
And then the one that really gets kind
of confusing here is that over tweeting
was kind of just random and dispersed
throughout the region.
There's a small cluster kind
of in the New Hampshire, Maine area of
over tweeting, but otherwise most areas
of over tweeting or kind of random.
So then moving on to our third research
question, what community characteristics
are correlated with over or under tweeting
based on a regression results?
We took those tweeting indexed values
that I just showed you
mapped on the last slide.
And we performed a Pearsons correlation
test between those TV values and then
the various community amenities that I
showed you many slides ago
that we went out and collected.
Again, for those of you who have taken
a quantitative course,
you might remember this.
But for those of us who are less familiar
on the left hand of your screen,
I've put just a little example of what
correlation tests are looking for.
So if you have a correlation value
of zero, basically there's
no real relationship.
And you can see that in that little
chart in the upper right hand corner.
That's an R of zero.
So that means no correlation
between the two variables.
But if you have a really strong
relationship, either negative or positive,
you'll see that the points
become more linear.
So as one point goes up
or, sorry, variable goes up,
another variable goes up.
So that tells us a very
strong relationship
there
Or a very strong, sorry, correlation there,
but we can't really find anything
else out from a correlation test
such as like,
as tweeting
index value goes up,
nonprofit organizations increased by 10.
Just to give a random example.
That's something that you can tell
with a regression, which we did not do.
We're just looking for trends here.
So here's just a table of our
Pearson correlation results.
You'll see that I've organized it
with the tweeting index values on top.
And then the community characteristics are
down the side on the right
hand of the table.
You'll see
our  'r' value, which is the Pearson's
correlation coefficient.
And that basically tells us how linear or
how cloud-like the relationship
or the correlation is.
And then you have your P value, which is
the statistical significance.
So I've gone ahead-
all the variables
that have color coded
cells are the ones that were significant
at the 0.05 level.
So as you can see,
nonprofits are one of the most
statistically significant
Pearson relationships.
So the P value is less than 0.001
which is
highly significant.
It has a middle of the road correlation
coefficient of 0.23
And as you can see,
there is a linear relationship.
So communities that were under tweeting
had a lower rate of nonprofits
and communities that were over tweeting
had a higher rate of nonprofits.
This continues for some of these
subcategories, including human services
nonprofits and public or
societal benefit nonprofits.
Something to note is that public or
societal benefit non-profits had
the highest Peerson correlation
coefficient of 0.24,
but all were similarly
statistically significant.
We also saw a U-
Shape relationship,
and I've gone ahead and drawn your
attention to the museum's relationship
as this was the most statistically
significant of the U-
Shaped relationships.
As you can see, it hasn't somewhat strong,
although not as strong as the
as the nonprofit's correlation
coefficient of 0.2
But what we're actually seeing
across our TIV is that the average
museum rate per 100,000 people for under
and over tweeting communities were lower
than that for communities
that were tweeting as expected.
So this U-
Shape relationship is somewhat puzzling
by actually has been demonstrated
in other literature.
And you can see I haven't
highlighted it for you,
but you can see that
percent active voter actually has
the inverse where communities that were
under or over tweeting have a higher
average percent active voter than
communities tweeting as expected.
So just moving in to some closing
thoughts, wanted to give you guys a
summary of all of the results in concert.
So first we found that there were
correlations between demographic variables
and tweeting to connect about depression.
The statistically significant demographics
were the population aged 15 to 45 and women.
We found that having higher proportions
of those populations were associated with
tweeting to connect about depression.
We also found that having a lower
percentage of single person households was
related with tweeting about depression.
We also found for our second question
about geographic trends that there were
some weak geographic trends
to the dispersion of tweeting about
depression. Along the east
coast of the region
ee saw a lot of communities
that were under tweeting.
And then when you moved the interior
of the region, you saw more
communities tweeting as expected.
What was kind of maybe surprising was
that the communities that were over
tweeting were somewhat randomly
distributed around the region with a small
cluster in the New Hampshire-Maine area.
For our Pearson
tests that were looking at relationships
between tweeting about depression
and community characteristics,
we found that there were a lot of U-
shaped trends that indicate that some
amenities or community characteristics
relate to both virtual isolation or under
tweeting and virtual connection
seeking or over tweeting.
But more perhaps interestingly,
we found that the presence of nonprofits
was strongly and linearly
correlated with over tweeting.
So,
the more nonprofits there are per 100000
people, the more likely that community is
to be sharing more about depression online
or seeking connection
about depression online.
And then some limitations
and areas for future research.
The spatial temporal scope
of this study should be expanded.
So that just means moving beyond
the northeast United States
and for a longer duration.
Again, we only collected tweets for one
week and we could have had many more
geotagged tweets if we were collecting
them for a longer period of time.
And we actually did have tweets
for the entire United States.
But because of the geotag problem
that kind of comes from just
the way that Twitter works,
and then also like the small
sample size that we had, the northeastern
United States just had the most tweets,
so we focused in on that.
Additionally, a lot of other community
characteristics might be relevant,
such as crime.
A lot of work has shown that crime
influences whether or not people feel
comfortable going out to their community.
And that's kind of at
the core of the study.
And we didn't really
capture that very well.
I also think that if you look at all
of the variables that we look at,
like that's how we're capturing place.
But you as individuals like you,
probably have a lot of reasons why you go
out into your community that aren't
captured in this study.
So, I mean, beyond parks, beyond
places of worship, beyond nonprofits.
And so that's just a part,
one of the shortcomings, rather,
of a quantitative study is that it's
a little bit rigid in how you can
conceptualize and demonstrate place.
So then moving into maybe like the last
shortcoming is that we're not really
looking at the demographics
of the people on Twitter.
We don't we didn't really include any
demographic information in the tweets
that we included in the study just because
the demographic information
is very incomplete
when you pull tweets from Twitter.
In addition, we didn't really look at,
you know, who uses Twitter more or less
like obviously younger
people use Twitter more.
So maybe that's self-evident that we'd
have a correlation between that and then
areas with more young people.
I will say that other studies have not
found differences in women using Twitter,
which does indicate that maybe women are
more likely to share their mental
health experiences online than men.
Which I think in some ways is pretty
intuitive, considering that women
in general are more likely to share their
emotions with others instead of men just...
in general.
And then I kind of invite you all to think
about what are some other shortcomings
of the study maybe situated against
your other readings this week or readings
that you've already done in this course?
And I invite you to reflect on this
question when you're reading any
academic study.
They all have shortcomings and some of
them are not apparent to the researcher.
So this is really something
that we as members of the
academic community, but also you can do
to really evaluate the rigor of a study.
And then my second question is,
can these results be repeated?
In some ways?
Yeah, they can.
You can pull Twitter data at any time
using any keyword search.
So you could
easily repeat the study.
You could do it now, at the next
Mental Health Awareness Week.
You can use a different hashtag or
the same hashtag and you could pull the
updated community characteristic data.
Maybe you include crime this time.
Maybe you focus in at
a lower spatial scale.
We used counties which were quite,
quite large for a study like this.
So maybe you want to focus in on the
GTA and you want to use
dissemination areas instead.
Or maybe a different
conceptualization of place.
The study could be repeated
in a variety of ways.
OK, so just a little bit of wrap up here.
What kind of talk about where I'm
at now in my work
and then some final thoughts
about undergraduate research.
As is natural,
I no longer do work,
really,
kind of like what you just read or what I
just presented on. When I did this work,
I was really new to research and I really
thought that quantitative approaches
were the only approaches.
And obviously, since then,
I have learned a lot about other
methodologies and methods
that other researchers employ.
So my work today is a lot
less quantitatively rigid.
I, for my PHD work, I'm doing
multiple, or sometimes called mixed,
methods, which include qualitative
inquiries such as a semi-structured
interviews or hopefully maybe using some
photo voice or something like that.
So
I think that using qualitative work
in conjunction with quantitative
approaches help you really understand the
subject of inquiry a little bit better.
I'm studying social isolation,
which is a kind of a nebulous concept
in the literature right now.
So I'm still in the space
about social connectedness.
I'm still looking at issues
surrounding technology use and,
you know, ideas that were in this
study about mental health.
But I've moved beyond kind of the
conceptualization in this paper.
I focus more on older adults now who are
at higher risk for social isolation.
And I'm also incorporating ideas about
mobility just because transportation is
something that I was passionate about
and studied in my Master's.
So incorporating these ideas of mobility,
navigating your immediate
built environment,
and then also incorporating qualitative
work is kind of where I'm
at now in my research program.
And then kind of just some final
thoughts about undergraduate research.
It was a great experience.
I got to present this work at two
conferences, one of which
I've put a photo on the
slide for you all.
This is an undergraduate
research symposium at my
undergraduate institution.
I also presented this work
at the American Association
of Geographers Conference in New Orleans.
So, yeah, those are two
really good experiences.
They helped me figure out that I did
really like doing research and it was also
something that I kind
of had a personality for.
Research is kind of an isolating activity.
And it's, it's very different from other
types of work in that you're kind of just
self leading,
self motivating a lot of the time.
And I think that this experience helped
me figure out if that was something that I
was actually built for and that it's
OK if you're not built for it.
Everybody has their own
needs in the workplace.
I had friends who did undergraduate
research who ultimately decided that it
wasn't really something that they liked
doing, that it wasn't really
something that made them happy.
And and so they've opted to do
professional masters or go directly
into the job, the job market,
and find a more traditional jobs.
And that's just something that we all
kind of have to figure out for ourselves.
But doing undergraduate research kind
of helps you figure it out before
you get into a master's program.
So, yeah,
If if it's something that's available
to you, and it's not always available,
I recommend it if you have time.
But at the end of the day,
I think that it was a helpful thing for me
and it moved me ahead of my
progression as an academic.
So, yeah, that's kind of what I
had prepared for you guys today.
I don't have any other slides
with my contact info, but I do just want
to say that if you have any questions
about this study or I know that you
guys are doing literature review.
So if you're doing
a topic for your literature review that's
kind of closely related
to what I'm researching.
I would be more than happy to chat
with you, recommend papers, whatever.
So my email is Amber.DeJohn@mail.utoronto.ca
So it's on the first slide.
I'm also on the Geography
and Planning Departments Web site
so you can go there to find
my contact information as well.
Beyond that, I hope that you guys enjoy
the other guest lecturer this week and
have a good summer.
