Note: This blog entry mentions the topics of sexual
harassment and assault.
Background:
Over
the last week, #MeToo has been trending on Twitter after a call to action from
actress Alyssa Milano for people to highlight their experiences of sexual
harassment or assault and show “the magnitude of the problem”.[1] The
high volume of #MeToo tweets can put to the test theories of how trends develop
over time on Twitter, providing an empirical basis to a field that currently
has few theories and even fewer good case studies. One area I will examine in
this project is how #MeToo manifests itself over Twitter as the days go by
past its initial peak popularity. I am also
interested in examining what types of Tweets in #MeToo were able to rise to the
top in terms of engagement, and whether there was a shift in their type over
time too. I hope to be able to produce some observations about what type of
Tweet drives trending social movements on Twitter to provide insight on how to
best use social media to create discourse or spread ideas.
Questions to answer:
1.
How did #MeToo evolve over time on Twitter
as a trend?
2.
What type of Tweet drew the most
engagement in #MeToo every day?
Hypothesis:
1. Tweets on #MeToo become more about social
commentary and less of solidarity/sharing stories
Drawing intuitions purely
from news media coverage of the social trend, the hashtag seemed to initially
be limited to a focus on Harvey Weinstein and issues of sexual assault and
harassment in the U.S. entertainment industry.[2] This
initial reason for the hashtag to trend was then usurped by the broader response
that spanned way beyond Hollywood as many more users shared their own experiences
with sexual assault and harassment. Currently, a broader discussion also has sprouted
out on cultural issues that created the enabling environment for the incidents
to happen. Over the next week or so we will probably see a decrease in the
number of shared stories and the hashtag will reflect more instances of social
commentary. Eventually the hashtag will see momentary resurgences when similar
news events about sexual assault and harassment capture the attention of the
media, much like how Black Lives Matter as a slogan tends to waver in terms of
trends according to whether there are further accounts of police brutality. A
visualization of the total tweets of #MeToo per day over time may look like an
inverted U Shape that eventually tapers off into smaller oscillations as the
news cycle captures new stories of sexual assault or harassment.
2. Tweets with a “call to action” draw more engagement
2. Tweets with a “call to action” draw more engagement
While the number of
engagements a tweet receives will definitely depend on the number of followers
the account already has, the content of the Tweet will also be a significant
determinant. Tweets that suggest a course of action, such as for men to speak
out or for more resources to report sexual misconduct will appear to add more
facets to the discussion and draw a bigger response. Retweets of
#MeToo, while critical in contributing to the movement over time will draw
less attention than other tweets that provide additional input. While a
compelling personal story may also draw lots of response too, it may be harder
to shape a powerful story in just 140 letters compared to calls to action so
the calls to action Tweets will in aggregate draw more engagement.
Methodology:
Data
collection will be assisted by NodeXL, using its limit of drawing 18,000 tweets
per day that used the hashtag on #MeToo. A snapshot of tweets will be taken once
every day, as this is the full capacity of Tweets that can be mined. With
this dataset of Tweets the attributes of location, user’s number of followers,
user’s number of tweets, retweets of the tweet and favorites of the tweet will
be indexed.
Using
the attribute data, I will extract the top 20 and bottom 20 Tweets in terms of
total retweets and favorites from each day as Tweets of interest. I will index
the Tweets according to what type of response they were communicating with
#MeToo. On a broader level this approach has been applied by by Zubiaga et. that
created a typology on trending topics on Twitter to be categorized into News,
Ongoing Events, Memes, and Commemoratives.[3] This approach of categorizing trends will be
extended to illustrate what types of engagement with the topic drew the most
and least attention. After browsing over the raw data I currently have these
are the 8 types of categories I intend to sort by:
1. Retweets only of #MeToo
1. Retweets only of #MeToo
2. Critiques or social commentary
3. Longer-formed personal narrative about own
experiences
4. Expressions of remorse or guilt from men
5. Doubts, backlash,
and negative responses
6. Humorous responses
7. Corporate/verified accounts
making statements
8. Media outlet coverage.
In addition to this sorting, I will
have a binary index of whether these top Tweets included a “call to action” in
them. A final level of analysis I will bring in is to identify if the top tweets
were responses to a longer thread and how long of a thread were they a part of.
This part is where the visualization of social network analysis may be very
helpful as top responses can all be a part of the same thread of replies. The
network I will be looking at thus includes just the selected Tweets with most
and least engagement. While the top engagement Tweets are more of interest, the
least engaging ones will also be analyzed in the same methodology to
potentially strengthen the confidence of any observations. The most
important network measure of all will be the attribute data of what type of
response the Tweets were under my categorization.
With
these methods, I will be focusing on whether the types of responses that get
the most engagement change over time. This will directly test the hypothesis
that the type of top engagement shifts more towards an activism/social critique
direction over time beyond just sharing of personal experiences. Furthermore,
the proportion of tweets that include a call to action will test the second
hypothesis that prompting action from others may resonate more in terms of
garnering more Twitter engagements.
Conclusion/Challenges and Limitations:
The
limitations of this methodology will be Tweets that fall through the cracks, missing the top trending Tweets
that do not appear during the one data collection point per day. Since the
18,000 Tweets are only the most recently available ones, they cannot track the
entire activity around #MeToo as its total Tweets currently vastly exceed
18,000 a day. However, it may be interesting to see if over the course of the
next few weeks the daily number of Tweets eventually fall below 18,000. A
further limitation is the identifying information like location or type of person
handling each account cannot be verified for accuracy unless they have a blue
check mark. One may describe themselves as “a student from Paris” but it is
pretty hard to take this information at face value. I will be sure to keep this
in mind while making qualitative observations. All in all, I hope this project will produce some viable takeaways on what type of
activity on Twitter receives the most engagement on trending topics.
Additional Sources:
Derek L. Hansen, Ben Shneiderman and Marc A. Smith,
Chapter 2 - Social Media: New Technologies of Collaboration, In Analyzing
Social Media Networks with NodeXL, Morgan Kaufmann, Boston, 2011, Pages 11-29,
ISBN 9780123822291, https://doi.org/10.1016/B978-0-12-382229-1.00002-3.
(https://www.sciencedirect.com/science/article/pii/B9780123822291000023)
Kawash, Jalal. Online Social Media Analysis and
Visualization. Lecture Notes in Social Networks (Unnumbered). 2014.
1 comment:
Nice work, Mathew, and the beginnings of a rewarding project. You point out the benefits and shortcomings of doing this analysis clearly, and I think you've got realistic expectations of the types of conclusions you can draw. As we discussed in class, you need to work on your Key Question; doing that will help you manage the scope.
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