ICA San Diego 2017 - The Social Shaping of DTC Genetic Testing: Sentiment Analysis of 23andMe on Twitter

Scholars, educators, regulators, pundits and other observers are advocating for regulation and oversight of ‘direct-to-consumer’ genomic testing (DTC). As a result, the technology has been subject of much public and regulatory controversy. In this article, we explore the sentiment about the DTC company, 23andMe. To describe the nature of the public opinion, we collected Twitter data of over 2000 tweets for a quantitative content analysis and qualitative framing analysis. Our analysis reviews particular frames that seem to underlie social media exchanges related to the company and its technology. Our study shows that people are largely positive towards DTC genomics and 23andMe. We argue that these frames create meaning and play a role in how a technology is interpreted by its users. As social research becomes increasingly driven by large data sets and internet-based research methods, we offer a timely, non-invasive analysis of emerging values associated with DTC genetic testing.

Transcript

1. The social shaping of DTC genetic testing
   Sentiment analysis of 23andMe on Twitter
   Alberto Lusoli; Peter Chow-White; Frederik Lesage; Stephan Struve
   Simon Fraser University. School of Communication. GeNA Lab
   ICA 2017, May 27th, San Diego, USA
   

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2. Image credits: 23andMe Offic
   

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3. 3
   ALBERTO LUSOLI – ICA 2017 SAN DIEGO
   0
   20
   40
   60
   80
   100
   120
   "23andMe" Search volume
   Market
   Agreement
   Source: Google Trends
   .
   

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4. Objective
   Explore the discourses
   around 23andMe.
   Understand how this debate
   about personalized
   genomics is influencing the
   development of the
   technology
   

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5. Theoretical approach
   Social Construction Of
   Technology (SCOT)
   (Bijker, Hughes, & Pinch, 1984)
   

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6. Because it allows unmediated and
   Non-invasive access to the
   discourse developing around
   23andMe
   (Cheng, Fleischmann, Wang, Ishita, & Oard, 2010;
   Fleischmann, Oard, D.W., Cheng, A.S., Wang, & Ishita, 2009;
   Morris, 1994; Weber, 1990)
   

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7. 7
   ALBERTO LUSOLI – ICA 2017 SAN DIEGO
   RQ1
   What are the user
   sentiments about 23andMe?
   Are they mainly positive or
   negative?
   RQ2
   What are the frames that
   underlie the positive and
   negative sentiments about
   23andMe?
   

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8. 8
   ALBERTO LUSOLI – ICA 2017 SAN DIEGO
   DATA COLLECTION
   (NCAPTURE+NODExl)
   DATA CODING
   (POSITIVE/NEGATIVE)
   FRAME
   IDENTIFICATION
   Unit of analysis
   The single tweet
   and linked webpages
   Data source
   2.027 tweets containing the term “23andMe” collected
   during a 1-week period (Jan.2014).
   research protocol
   CONTENT ANALYSIS OF TWEETS (BERG, 2001)
   

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9. 9
   ALBERTO LUSOLI – ICA 2017 SAN DIEGO
   Positive
   86%
   Negative
   14%
   results
   SENTIMENT ANALYSIS
   

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10. 10
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    

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11. 11
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    results
    FRAME ANALYSIS
    Unqualified excitement about the potential of DTC genomic tests
    Happy to share their results publicly.
    

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12. 12
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    Tweets referring to the concepts of family, roots and belonging.
    Positive attitudes toward the possibility to rediscover the past and reconnect with
    relatives.
    

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13. 13
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    All those tweets which express the possibility to change behaviors on the basis of the
    test results.
    

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14. 14
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    All the tweets which identified as 23andMe as a disruptive innovation, as
    opposed to the paternalistic and conservative power of the FDA.
    

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15. 15
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    Critical frame that questioned the scientific accuracy of the test.
    Tweets often expressed doubt and brought scientific research as counterarguments
    to the supposed validity of the 23andMe test.
    

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16. 16
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    All the tweets which criticized the actual possibility for customers to extract
    meaningful information from the data without proper background knowledge.
    

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17. 17
    ALBERTO LUSOLI – ICA 2017 SAN DIEGO
    results
    FRAME ANALYSIS
    This frame includes tweets that discuss the negative emotions involved in the
    unmediated access to health data (e.g. anxiety, stress) (Su, 2013).
    

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18. 18
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    0
    20
    40
    60
    80
    100
    120
    Maket
    Agreement
    Source: Google Trends
    .
    conclusions and next steps
    

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19. A special thanks to everybody who
    contributed to this project:
     Stephan Struve
     Frederik Lesage
     Peter Chow-White
     Nilesh Saraf
     Amanda Oldring
     Eliot Tran
     Lucas Wu
    THANK YOU
    For more information:
    alusoli@sfu.ca
    twitter.com/albertolusoli
    genalab.org
    Photo Credits:
    All pictures used in this presentation are protected by Creative Common License
    except where otherwise indicated.
    

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20. Next steps
    1. Assess to what extent marketing and promotional strategies on social media can influence the closure process
    2. Understand the role that “neutral educational Tweets” have in support of positive/negative sentiments
    Image credits: Momondo Official Website
    

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