AEJMC presentation of "Uniformed Diversity: Analysis of Twitter Response to Trump’s Ban on Transgender in Military Service"

1. ANALYSIS OF TWITTER
RESPONSE TO TRUMP’S
BAN ON TRANSGENDER IN
MILITARY SERVICE
UNIFORMED DIVERSITY By Andrew B Church

3. ABSTRACT / BACKGROUND
▸President Trump announced his controversial and comprehensive ban on transgender
individuals from military service via Twitter on July 26, 2017.
▸What immediately followed was a tweet-storm of reactionary posts on Twitter, ranging from
unequivocal support of the ban, to abhorrence and shame.
▸This pilot study uses framing theory to qualitatively analyze frames used by these tweets
on the day it was announced in order to draw conclusions about the differences between
public opinion and government policy concerning transgender military service.
▸Comparisons are drawn between Don’t Ask, Don’t Tell (DADT) legislation, Truman’s 1948
executive order for racial integration, and the current U.S. military service banning policies
in order to historically contextualize present-day events.
▸Results show that the majority of tweets oppose the ban and frame the issue using anti-
other political rhetoric.
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4. ▸Executive Order 9981: 1948 executive order by President Truman that
officially desegregated the U.S. military.
▸Uniform Code of Military Justice (10 U.S.C. Ch. 47 § 877.125): reasoning
has shifted away homosexuality as perversion to homosexuality as disabling
military function.
▸Groshek & Holt (2017): DADT policy merely closeted the issue of
homosexuality in the military.
▸Krebs (2005): integration of LGBTQ+ relates to racial desegregation, not
socially but certainly politically, i.e. the politics of identity.
MILITARY POLICY
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5. ▸Goffman (1970s): media shape and project content in news stories using framing.
▸Entman (2007): framing is “the process of culling a few elements of perceived reality and
assembling a narrative that highlights connection among them to promote a particular
interpretation” (p. 164)
▸Meyers (1996): Washington Post often framed racial integration as a cultural battle
between the North and South.
▸Steele (1997): media framed the issue of lifting the ban on homosexuals in the military,
such that viewers were left with two dominant and opposing sides.
▸Zaller (1992): polar frames: homosexuality as a psychological disorder; or, homosexuality
as a civil rights concern.
FRAMING
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6. ▸Blackstone et al. (2017): framing of tweets helped propel the 2014 story of
the death of Michael Brown toward a global conversation about “the racial
health of the nation, the established policies of law enforcement, and the
challenge of elected leaders to resolve explosive domestic problems” (p.
611).
▸Burch et al. (2015): Twitter is a dissemination hub of counter-frames and
allows users to reshape public opinion.
TWITTER & FRAMING
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7. ▸RQ1: How do Twitter users frame the ban on transgender servicemembers?
▸RQ2: What are the commonly recurring themes found in these frames?
▸RQ3: Do the majority of Twitter users who joined the conversation about
transgender servicemembers after Trump’s announcement support or oppose
the transgender ban?
RESEARCH QUESTIONS
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8. ▸Search of Twitter using TweetDeck
▸Search terms: “trans OR transgender AND military”
▸Search filtered by date, language — retweets excluded
▸Every 15th tweet gathered into Twitter collection using TweetDeck software
(https://twitter.com/abcfoto1980/timelines/921829327834177536).
▸Twitter collection → Excel document
▸Content analysis using three-cycle focused coding of 200 tweets.
▸Qualtrics was used to codify sample and manage data.
METHODOLOGY
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9. ▸A content analysis of the sample (n = 200) was conducted using cyclical,
focused coding technique.
▸Included:
▸General examination of each tweet to reveal recurring themes, tone, and
stance.
▸Reduction of themes were reduced to find narrower characterizations of the
transgender ban.
▸Condensation of the characterizations → frames.
MEASURES
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10. ▸Tone
▸Disposition of visual and textual elements toward positivity, neutrality, or negativity.
▸Stance
▸Assessment of the tweet’s tendency toward support of — or opposition to — the
transgender ban.
▸Tone and ban characterization were considered in this assessment.
▸*Tweets with linked content were generally neutral, with the exception of online petitions
or some blogs.
TONE & STANCE
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11. Against the
Ban
53.0%
Neutral
29.0%
Supports
the Ban
18.0%
Stance of Tweets on Transgender Ban
Negative
57.5%
Neutral
30.5%
Positive
12.0%
Overall Tonality of Tweets
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12. THEME USE
Top 10 Themes
Theme Count %
News story / link 30 15.0%
Anti-Trump 20 10.0%
Pro-transgender 13 6.5%
The Viagra argument 12 6.0%
Thankfulness for transgender service 11 5.5%
Financial burden 9 4.5%
Military spending 8 4.0%
Equal rights 8 4.0%
Military efficiency 8 4.0%
Celebrity support 5 2.5%
All Others 76 38.0%
Total 200 100.0%
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13. BAN CHARACTERIZATIONS
Ban Characterizations
Genre Count %
Unavailable 51 25.5%
Unjust 50 25.0%
Discriminatory 22 11.0%
Good for military 18 9.0%
Just 13 6.5%
Unnecessary 13 6.5%
Able to be changed 11 5.5%
Bad for military 7 3.5%
Distraction 4 2.0%
Political victory 3 1.5%
Protective 2 1.0%
Destructive 2 1.0%
Un-American 1 0.5%
Political failure 1 0.5%
Betrayal 1 0.5%
Limited effects 1 0.5%
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14. DOMINANT FRAME USE
30
27
24
23
21
20
19
14
10
7
4
1
0 6 12 18 24 30 36
Anti-other
Finances / spending
Political
Exterior support
Military function
Breaking news
Human rights
Petition
Medical / psychiatric
Minimization
Research
Shock / disbelief
Count
Frame Utilization of Tweets
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15. ▸12 frames were used to portray the transgender ban:
▸The most commonly used frames were the anti-other (15%),
finances/spending (13.5%), and political (12%).
▸The overall tone of the tweets was mostly negative (57.5%)…
▸And most of the tweets opposed the ban (53%).
RESULTS SUMMARY
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16. ▸Most widely used frame was the anti-other frame → topic of transgenders in the military involved
polarized discussion
▸Twitter users framed the issue of transgenders in the military uncannily similar to the ways
racial integration was framed during the 1940s → social experiment, military efficiency, morale
▸Tweets suggest incongruity of political representation and direction, given the 53.0% of
tweets that oppose the ban and the 18.0% that support it.
▸Military inclusion → politics of identity — rather than national security, armed forces
professionalization, or most importantly tolerance of difference.
▸3 of the top 4 tweets with the highest engagement (i.e. number of Likes + number of Retweets
+ number of Comments) were made by Twitter users who are in favor of the transgender ban.
POINTS OF INTEREST
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17. ▸Taken together, the results reflect public disagreement with respect to
transgender inclusion in military service.
▸Tweets = social-media pulse-check for political climate?
▸Future studies may see similar disparities between other policies enacted by the
Trump administration and user-generated content on social media.
▸On a larger scale, social media response to government policy may indicate
political successes or failures.
▸If the LGBTQ+ community hopes to preserve right-to-serve for transgenders, a
different political tactic needs to be made → avoidance of human rights angle.
CONCLUSION
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18. ▸Twitter’s API → meaningful search results & exclusion of repetitious tweet
content
▸Limiting the number of repetitive or unusable results, such as petition tweets,
news story links
▸Competing frames — which one is the dominant frame?
▸Future studies should more directly measure the motivations and opinions of
Twitter users, e.g. using a questionnaire, to more accurately gauge issue
stance.
LIMITATIONS
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19. QUESTIONS?
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