visualization mapping for
play

VISUALIZATION MAPPING FOR IMPROVED DIGITAL PUBLIC HEALTH - PowerPoint PPT Presentation

APPLYING NETWORK SCIENCE AND VISUALIZATION MAPPING FOR IMPROVED DIGITAL PUBLIC HEALTH COMMUNICATION Moderated by Brittany Seymour, DDS, MPH Public Health Consultant, MIT Faculty Associate, Berkman Klein Center for Internet and Society at


  1. APPLYING NETWORK SCIENCE AND VISUALIZATION MAPPING FOR IMPROVED DIGITAL PUBLIC HEALTH COMMUNICATION Moderated by Brittany Seymour, DDS, MPH Public Health Consultant, MIT Faculty Associate, Berkman Klein Center for Internet and Society at Harvard Assistant Professor, Harvard School of Dental Medicine

  2. A Story about fluoride

  3. Dental Caries

  4. Top 10 0 Public lic Hea ealth h Achi hievemen ements ts of the e 20 th th Cen entur ury 1. Vaccines 2. Motor vehicle safety 3. Safer workplaces 4. Control of infectious diseases 5. Decline in deaths for coronary heart disease and stroke 6. Safer and healthier foods 7. Healthier mothers and babies 8. Family planning 9. Fluoridation of drinking water 10. Recognition of tobacco use as a health hazard Source: Centers for Disease Control and Prevention

  5. A Story about Fluoride

  6. A Story about Fluoride

  7. A Story about Fluoride Seymour et al, AJPH, 2015

  8. Media Cloud ■ What other tools do: reach and impact metrics ■ What Media Cloud does: influence ■ An open platform, joint project of the Harvard Berkman Klein Center for Internet and Society and the MIT Center for Civic Media

  9. Presenters “Media Influence and Framing the Public Health Narrative Around Unarmed Deaths, Race, and Gun Violence: Mapping the Trayvon Martin Case” Rahul Bhargava, MS Research Scientist, MIT Media Lab MIT Center for Civic Media “Vaccine hesitancy in digital networks: trust and social proof online” Hal Roberts, ts, MS Fellow, Berkman Klein Center for Internet & Society Harvard University “Could Fragmented Communication Networks Reshape the Narrative?: Evidence from Tobacco and e-Cigarette Media Coverage” Laura Gibso son, n, PhD Research Director, Tobacco Center on Regulatory Science University of Pennsylvania

  10. MEASURING MEDIA ATTENTION TO GUN AND POLICE VIOLENCE Rahul Bhargava Research Scientist, MIT Media Lab MIT Center for Civic Media @rahulbot rahulb@mit.edu

  11. Minneapolis, MI 12/3/2015 Ferguson, MI 8/17/2014 New York City, NY 11/28/2014 Baltimore, MD 4/29/2015

  12. A PUBLIC HEALTH APPROACH

  13. Academic Responses JAMA, 2013 NEJM, 12/2016 Public Health, 2015

  14. Public Health Approaches Mozaffarian D, Hemenway D, Ludwig DS. Curbing Gun ViolenceLessons From Public Health Successes. JAMA. 2013;309(6):551-552. doi:10.1001/jama.2013.38

  15. Two examples of how this might work in action. We can help this effort by mapping the media narratives to identify processes and actors. 1. Looking for changes in media coverage of police violence against unarmed people of color. 2. Understanding the fight over how the media told the story of the Trayvon Martin case.

  16. POLICE VIOLENCE AGAINST UNARMED VICTIMS OF COLOR Nathan Matias, Natalie Gyenes, Allan Ko, Ethan Zuckerman, Rahul Bhargava, Hal Roberts

  17. Data Sources Mapping Police Violence The Guardian Washington Post

  18. Michael Brown’s death as a key y event ent in recent history of police killings of unarmed people of color. Sources: Media Cloud articles mentioning victims in the 5 days before, and two weeks after death (n=717,871).

  19. Media coverage of these incidents changed after Michael Brown was killed.

  20. Ne News ws ar arti ticles cles per unarm armed ed bl blac ack k person son killed lled by US police lice in in the days surrounding each death, including before & after the death of Michael Brown Sources: Mapping Police Violence, Washington Post, Guardian 01/01/2013 - 06/29/2016 (n=333). Media Cloud articles in the two week period after death, normalized by total Media Cloud article count. 10 observations within 14 days before Michael Brown’s death are omitted.

  21. An unarmed black person killed by US police received 10.5x .5x th the inci cide dence nce rat ate of Ne News ws Ar Arti ticl cles es after Michael Brown’s death from those killed before Sources: Mapping Police Violence, Washington Post, Guardian 01/01/2013 - 06/29/2016 (n=333). MediaCloud articles in the two week period after death, normalized by total MediaCloud article count. NB Model controls for age, gender, and regional population. p=7.84e^-14

  22. Among unarmed black people killed by US police, the number of news articles per person for June 2016 was not significantly different from Jan 2013 Sources: Mapping Police Violence, Washington Post, Guardian 01/01/2013 - 06/29/2016 (n=333). MediaCloud articles in the two week period after death, normalized by total MediaCloud article count. NB Model controls for age, gender, and regional population.

  23. Articles about an unarmed black person killed by US police received 49.8x 9.8x th the incidence cidence rat ate e of Total tal Facebook acebook Sha hares es after Michael Brown’s death Sources: Mapping Police Violence, Washington Post, Guardian 01/01/2013 - 06/29/2016 (n=333). Facebook Shares to articles in the two week period after death, normalized by total Media Cloud article count. NB Model controls for age, gender, and regional population. p=2.56e^-12

  24. Articles about an unarmed black person killed by US police received 49.8x 9.8x th the incidence cidence rat ate e of Total tal Facebook acebook Sha hares es after Michael Brown’s death Sources: Mapping Police Violence, Washington Post, Guardian 01/01/2013 - 06/29/2016 (n=333). Facebook shares of articles in the two week period after death, normalized by total MediaCloud article count. NB Model controls for age, gender, regional population, and article count.

  25. A Useful Approach for a Public Health Response 1. 1. Identif entification cation: key event 2. 2. Awaren areness ess: sustained social media interest 3. 3. Pr Prevent ntion ion: easier to talk about

  26. THE TRAYVON MARTIN CASE Erhardt Graeff, Matt Stempeck, Ethan Zuckerman

  27. A fight over the media narrative about this boy’s death.

  28. Data Sources Media Cloud Bit.ly clicks PageOneX Newspaper front pages n=8,643 n=1,233,899 n=1.91 daily avg Tweets n=374,690 The Internet Archive Change.org Petition Google Trends n=2,764 n=2,038,557 n=25k daily avg

  29. A media story best told in 5 acts Act 1: Not a Story Act 2: Building Pressure Act 3: National Exposure Act 4: Political Agenda War Act 5: Tabloid Court Case

  30. Act ct 1: Not a Story Feb 26 – Mar 6

  31. Act ct 2: 2: Building Pressure Mar 7 – 15

  32. Act ct 3: 3: National Exposure Mar 6 – 22

  33. Act ct 4: 4: Political Agenda War Mar 23 – Apr 10

  34. Act ct 4: 4: Plot 1: The Left The Left goes after the “American Legislative Exchange Council”.

  35. Act ct 4: 4: Plot 2: The Right The Right goes after Trayvon Martin’s image; labelling him a “drug dealer”.

  36. Act ct 5: 5: Tabloid Court Case Apr 11 – 30

  37. A Useful Approach for a Public Health Response 1. 1. Traj ajec ector ory: from a clip to national story 2. 2. Pi Piggy-ba back ck: activists shifting to their cause 3. 3. Tran ansmedia smedia: analyzing across media

  38. CONCLUSION

  39. Public Health Responses • Changing media depictions • Media education campaigns Publicizing support options • • Nonprofit advocacy

  40. VACCINE HESITANCY IN DIGITAL NETWORKS: TRUST AND SOCIAL PROOF ONLINE Hal Roberts Fellow, Berkman Klein Center for Internet & Society Harvard University

  41. Research team Brittany Seymour Harvard School of Dental Medicine Rebekah Getman Harvard School of Dental Medicine Mohammad Helmi Harvard School of Dental Medicine Alfa Yansane Harvard School of Dental Medicine Hal Roberts Berkman Klein Center, Harvard University David Cutler Department of Economics, Harvard College

  42. Vaccine Hesitancy

  43. Vaccine Support

  44. Media Cloud Topic Mapper Spider 1. Search Media Cloud archive for ‘vaccine*’ in U.S. sources from 2014-06-01 – 2015-03-01. 2. Parse all links from those ~14,000 stories. 3. Download all unrecognized urls and test for the presence of ‘ vaccin *’ in the text of each. 4. Add any matching stories to the topic. 5. Repeats steps 2. – 4. fifteen times. 6. Analyze resulting set: 49,144 stories, 53,092 story links, 4,817 media sources, 20,979 links between media sources

  45. Link Network

  46. Influential Sources Source Overall ll Pro vaccin ine Vaccine ne hesit itant nt Healt lth h and Scienc nce Mains nstream eam Media ia rank nk 1 cdc.gov scienceblogs.com ncbi.nlm.nih.gov sciencedirect.com nbcnews.com 2 ncbi.nlm.nih.gov sciencebasedmedicine.org nvic.org chemport.cas.org msnbc.com 3 Wikipedia.com Wikipedia.com mercola.com cdc.gov livescience.com 4 ageofautism.com leftbrainrightbrain.co.uk naturalnews.com apps.weofknowledge.co nytimes.com m 5 scienceblogs.com rationalwiki.org whale.to springer.com washingtonpost.com 6 youtube.com braindeer.com youtube.com who.int npr.org 7 nytimes.com pediatrics.aappublications.org greenmedinfo.com jid.oxfordjournals.org latimes.com 8 sciencedirect.com feeds.feedburner.com medalerts.org nature.com huffingtonpost.com 9 naturalnews.com oracknows.blogspot.com healthimpactnews.co jama.jamanetwork.com twitter.com m 10 10 fiercevaccines.com theness.com sanevax.org tandfonline.com cnn.com

  47. Primary Science Authority

  48. Community Authority

Recommend


More recommend