Social Media for Health
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The document summarizes a panel presentation on social media and health. It discusses how social media can help connect healthcare professionals to patients and communities. It also explores how blogs can communicate specialized health information and how social media enables online communities of healthcare workers. The panel outlines how social media is used in health contexts like disseminating information during crises and supporting patient communities. It also covers practical considerations for social media use in healthcare.
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Social Media for Health
- 1. Social Media & Health Panel Presentation for the Integrated Health Research Training Partnership (IHRTP) Anatoliy Gruzd Associate Professor, Director of Social Media Lab Dalhousie University Philip Mai Sarah Visintini Research & Communications Manager, Social Media Lab Dalhousie University System Administrator, Social Media Lab Dalhousie University
- 2. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 2
- 3. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 3
- 4. Dalhousie University Faculty of Management School of Information Management 4
- 6. Growth of Social Media and Social Networks Data Social Media have become an integral part of our daily lives! Facebook Twitter 1B users 500M users 6
- 7. How to Make Sense of Social Media Data? 7
- 8. How to Make Sense of Social Media Data? Social Network Analysis (SNA) Nodes = Group Members/People Edges /Ties (lines) = relations / Connections 8
- 9. Advantages of Social Network Analysis • Reduce the large quantity of data into a more concise representation • Makes it much easier to understand what is going on in a group Once the network is discovered, we can find out: • How do people interact with each other, • Who are the most/least active members of a group, • Who is influential in a group, • Who is susceptible to being influenced, etc… 9
- 10. Social Media Use during the 2011 Canadian Federal Election 10
- 11. Political Polarization on Social Media 11
- 12. #1b1t Twitter Book Club
- 14. Social Media for Health • Communication of specialized health-related information in blogs • Health-related online communities 14
- 15. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 15
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- 17. 17 (Photo credit: “The dangers of social media” Pamela S.)
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- 20. Hospitals can use social media to monitor emergencies and provide real time announcements and information during crisis situations. Boston Marathon Bombing 2013 Health institutions and organizations can increase the timely dissemination of high-quality health information and health education campaigns. #omgsti: Gonorrhea Awareness Social Media Campagin Patients with various conditions can share information and experiences, compare treatments, and provide support to one another. #BCSM: The Intersection of Breast Cancer and Social Media 20 (Photo Credit: “Boston Memorial” Eva Wood; #BCSM; Your Sexual Health)
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- 25. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 25
- 26. Gruzd, A., Black, F.A., Le, Y., Amos, K. (2012). Investigating Biomedical Research Literature in the Blogosphere: A Case Study of Diabetes and HbA1c. Journal of the Medical Library Association 100(1): 34-42. DOI: 10.3163/1536-5050.100.1.007 26
- 27. Gruzd, A., Black, F.A., Le, Y., Amos, K. (2012). Investigating Biomedical Research Literature in the Blogosphere: A Case Study of Diabetes and HbA1c. Journal of the Medical Library Association 100(1): 34-42. DOI: 10.3163/1536-5050.100.1.007 27
- 28. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 28
- 29. Haythornthwaite,C. and Gruzd, A. (2013). Enabling Community through Social Media. Journal of Medical Internet Research 15(10):e248. doi: 10.2196/jmir.2796. PubMed PMID: 24176835. Health Care Social Media Canada (#hcsmca) Twitter Community
- 30. Background • #hcsmca is a vibrant community of people interested in exploring social innovation in health care. We share and learn, and together we are making health care more open and connected • #hcsmca hosts a tweet chat every Wednesday at 1 pm ET. The last Wednesday of the month is our monthly evening chat at 9 pm ET. Source: http://cyhealthcommunications.wordpress.com/hcsmca-2/ 30
- 31. Research questions 1. What accounts for the relative longevity of this particular online community? – – Is it because of the founder’s leadership and her continuing involvement in this community? Or is there a core group of members who are also actively and persistently involved in this community? 2. What is the composition of this community? Does one’s professional role/title determine a person’s centrality within this community. 31
- 32. Step 1: Data Collection Data: Public Twitter messages that mentioned the #hcsmca hashtag/keyword Collection Period: November 12 – December 13, 2012 Software: Netlytic http://netlytic.org 32
- 33. Topics Covered (1) Nov 14, 2012 T1: Challenge of engaging SM to inform a research agenda T2: Use of innovation, SM, and gamification to encourage uptake of self-care 33
- 34. Topics Covered (2) Nov 21, 2012 T1 Healthcare blogs should we or shouldn’t we, what have we learned, what are the benefits? T2 Are healthcare blogs a useful tool for education and knowledge transfer? 34
- 35. Topics Covered (3) Nov 28, 2012 T1: How has social media made you healthier? Unhealthier? Has social media made our health choices more numerous and this overwhelming? T2: What messaging would motivate you to make a positive health change? Who would you listen to? 35
- 36. Automated Discovery of Online Social Networks Example: Tweets @John Nodes = People Ties = “Who retweeted/ replied/mentioned whom” Tie strength = The number of retweets, replies or mentions @Peter @Paul 36
- 37. #hcsmca Communication Network on Twitter (Nov 12 - Dec 13) Net viz in Netlytic: http://netlytic.org/gephi/sigma.php?c=0ZnbSm6D23u07bT0&viz=2 37
- 38. #hcsmca Communication Network on Twitter (Nov 12 - Dec 13) Roles Count SM health content providers 110 Unaffiliated individual users 89 Communicators - not specifically health related 74 Communicators - Health related 59 Healthcare professionals 50 Health institutions 31 Advocacy 30 Students 16 Educators, professors 13 Researchers 10 Government and health policy makers 4 *Roles are assigned manually Node size = In-Degree Centrality 38
- 39. #hcsmca Communication Network on Twitter Nodes are automatically grouped based on their roles No apparent clustering among people in the same role (notice cross-group ties) Procedure: Analysis of Variance Density Test using UCINET 39
- 40. Panel Outline • About the Social Media Lab • How social media can help you better connect to your patients and to your community • The role of weblogs in the communication of specialized health-related information, to both lay and expert communities • Enabling communities of healthcare professionals through social media • Practical Considerations 40
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- 46. Websites & Articles • • • • • • • HLWIKI: Health Care Managers & Social Media Centers for Disease Control and Prevention: Social Media Tools, Guidelines & Best Practices SMiCH: Sharing Info on Social Media in Canadian Healthcare #hcsmca Symplur’s The Scoop in Healthcare Social Media 20 Hospitals with Inspiring Social Media Strategies (PR Daily) Social Media: its Antics, its Power and its Expanding Necessity in Health Care (Medcrunch) Twitter Accounts @hcsmca @PatientsCanada @colleen_young @Emily_Nicholas8 @HealthSocMed @TheRounds Blogs • Found in Cache: Social Media Resources for Health Care Professionals from Ed Bennett • KevinMD 46
- 48. SocialMediaAndSociety.com Toronto, Sep 27-28, 2014 48
- 49. This presentation is available on Slideshare at http://www.slideshare.net/primath/presentations Anatoliy Gruzd Gruzd@dal.ca @Gruzd Philip Mai Philip.Mai@dal.ca @PhMai Sarah Visintini Sarah.Visintini@dal.ca @SVisin 49
- 50. #BCSM. (2014) Home. Retrieved from http://www.bcsmcommunity.org/ Bennett, E. (2011). Social media and hospitals: from trendy to essential. From Futurescan: Healthcare trends and implications 20112016. Chicago: Health Administration Press. Bennett, E. (2009). Social media in crisis – Scott & White and the Fort Hood shootings. Found in Cache [weblog]. Retrieved from http://ebennett.org/scott-white-fort-hood/#ixzz2rbqzX4Sb Britt, D. (2011). Healthcare professionals and social networking. The Social Media Issue 2. Retrieved from http://source.southuniversity.edu/healthcare-professionals-and-social-networking-33211.aspx Cassa CA, Chunara R, Mandl K, Brownstein JS. (2013). Twitter as a sentinel in emergency situations: lessons from the Boston Marathon explosions. PLOS Currents Disasters. 1. doi: 10.1371/currents.dis.ad70cd1c8bc585e9470046cde334ee4b. Retrieved from http://currents.plos.org/disasters/article/twitter-as-a-sentinel-in-emergency-situations-lessons-from-the-boston-marathonexplosions/ Centers for Disease Control and Prevention. (2011). The Health communicator’s social media toolkit. Retrieved from http://www.cdc.gov/socialmedia/tools/guidelines/pdf/socialmediatoolkit_bm.pdf Centers for Disease Control and Prevention. (2012). CDC Social media tools, guidelines & best practices. Retrieved from http://www.cdc.gov/socialmedia/Tools/guidelines/ Change Foundation. (2011). Using social media to improve healthcare quality: A guide to current practice and future promise: part 1: introduction and key issues in the current landscape. Toronto, Ont: Change Foundation. 50
- 51. CNN. (2009). Officials: Fort Hood shooting suspect alive; 12 dead. CNN. Retrieved from http://www.cnn.com/2009/US/11/05/texas.fort.hood.shootings/index.html Deloitte Center for Health Solutions. (2010). Social networks in health care: communication, collaboration and insights. Retrieved from http://www.deloitte.com/assets/Dcom-UnitedStates/Local%20Assets/Documents/US_CHS_2010SocialNetworks_070710.pdf Fox, Susannah. (2013). Pew Internet: Health. Retrieved from http://www.pewinternet.org/Commentary/2011/November/PewInternet-Health.aspx Hernandez, D. (2013, February 5). How Facebook is transforming science and public health. Wired. Retrieved from http://www.wired.com/business/2013/02/how-facebook-is-changing-science-and-health-care/ HLWIKI International. (2013). Evidence-based web 2.0. Retrieved from http://hlwiki.slais.ubc.ca/index.php/Evidence-based_web_2.0 Kinsey, M.J. (2014) What happens in the hospital doesn’t stay in the hospital. Slate. Retrieved from http://www.slate.com/articles/technology/future_tense/2014/01/doctors_on_social_media_share_embarrassing_photos_details_of_ patients.2.html Kowalczyk, L. (2013) Hospitals size up the lessons of Marathon attacks. Boston Globe. Retrieved from http://www.bostonglobe.com/lifestyle/health-wellness/2013/07/27/boston-hospitals-confronted-challenges-identifying-patientsafter-marathon-bombing/7fFWuivM3tTKbIFAyn1BIJ/story.html Larson, Eric. (2013). Should this doctor have slammed her patient on Facebook? Mashable. Retrieved from http://mashable.com/2013/02/11/doctor-patient-facebook/ Mayo Clinic. (2010). Legal issues (Part 4): specific suggestions when drafting your policies. Retrieved from http://socialmedia.mayoclinic.org/2010/08/09/legal-issues-part-4-specific-suggestions-when-drafting-your-policies/ Mayo Clinic. (n.d.). Mayo Clinic Center for Social Media. Retrieved from http://socialmedia.mayoclinic.org/ Murray, N. (2013). Social media used to fight ‘trending’ gonorrhoea. Irish Examiner. Retrieved from http://www.irishexaminer.com/archives/2013/1210/ireland/social-media-used-to-fight-apostrendingapos-gonorrhoea-252171.html 51
- 52. SMiCH. (2013). Hospital Social Network List. Retrieved from http://www.smich.ca/ Smith, T. (2013) Boston hospitals share lessons from marathon bombing. NPR. Retrieved from http://www.npr.org/blogs/health/2013/09/19/224049730/boston-hospitals-share-lessons-from-marathon-bombing Statistics Canada. (2013) Table358-0153 - Canadian Internet use survey, Internet use, by age group, Internet activity, sex, level of education and household income, occasional (percent), CANSIM (database). Retrieved from http://www5.statcan.gc.ca/cansim/pickchoisir?lang=eng&p2=33&id=3580153 Timimi, F.K. (2012). Medicine, morality and health care social media. BMC Medicine 10, 83. doi:10.1186/1741-7015-10-83 Vanderbilt University Medical Center. (2012). Social media toolkit. Retrieved from http://www.mc.vanderbilt.edu/root/vumc.php?site=socialmediatoolkitz Your Sexual Health. (2014). OMG: Gonorrhoea…it’s trending. Retrieved from http://www.yoursexualhealth.ie/ 52
Editor's Notes
- Add Dalhousie Logo (Right corner)**Philip & Anatoliy’s positionsAdd emails as well
- As many of you are aware, in a very short period of time, Social Media has become an integral part of our daily lives. FB now has 1 Billion users. Twitter 500 Million. All of these users are generating an incredible amount of information. For example, Twitter now handles over 400 million tweets per day.All of this data is a rich resource for researchers
- One way to do this is to represent the data in the form of a network. This method is call Social Network Analysis. In SNA : nodes represent group members, and lines– called edges (or ties) connect group members by means of various types of relations.There are
- 2011 Canada Federal Election & Social Media
- Dr. Amy Dunbar, 33, of St. John's Mercy Medical Center in St. Louis, Mo., posted on the social networking site in late January about a patient who continually showed up late to her scheduled appointments. Dunbar wrote:"So I have a patient who has chosen to either no-show or be late (sometimes hours) for all of her prenatal visits, ultrasounds and NSTs. She is now 3 hours late for her induction. May I show up late to her delivery?"When asked in the comments why she didn't cancel the procedure or transfer the woman to another doctor, Dunbar stated that the patient had previously endured a stillbirth. The post was publicly visible and not restricted to Dunbar's 470 friends.http://mashable.com/2013/02/11/doctor-patient-facebook/
- Social Media Monitoring & Improving Services Example:Disaster Preparedness Example: Boston Marathon Bombing“Social networking was also helpful on the day of the Boston Marathon bombing. Doctors near the finish line tweeted accounts of the attack to local emergency personnel six minutes before official announcements were made, giving staff critical time to prepare for the arrival of victims.”http://www.slate.com/articles/technology/future_tense/2014/01/doctors_on_social_media_share_embarrassing_photos_details_of_patients.2.htmlOne other lesson from the marathon bombing is the value of social media. Mass General got an early indication of trouble when an ER doctor saw a tweet from a friend at the finish line, just about a minute after the blast. That prompted the ER to hold off on surgeries that were about to begin, saving precious time and space for the victims that would flood in.http://www.npr.org/blogs/health/2013/09/19/224049730/boston-hospitals-share-lessons-from-marathon-bombingMonitoring Twitter to increase disaster reactivityhttp://currents.plos.org/disasters/article/twitter-as-a-sentinel-in-emergency-situations-lessons-from-the-boston-marathon-explosions/Fort Hood shooting in Texashttp://www.cnn.com/2009/US/11/05/texas.fort.hood.shootings/index.html“This was followed by a continuous string of updates that included information on Emergency Room access, Hospital operation status, re-tweets from the Red Cross, dialog with local reporters and other resources for visitors. In addition to Twitter, Scott and White used a Blog and YouTube to keep everyone informed.http://ebennett.org/scott-white-fort-hood/#ixzz2rbqzX4SbCommunity Support Example: #bcsm#bcsm: Breast Cancer Social Media http://www.bcsmcommunity.org/Online community that discusses treatment and survivorship of breast cancer, asking discussion questions like:“Q1A: What did you need to really KNOW about the medical system to navigate cancer? #bcsm”“Q1: What are the three things you wish someone had told you when you were diagnosed? Or one thing? Or five. As many as you have. #bcsm”They use the following disclaimer:Disclaimer:#BCSM is not a forum for medical advice. We strive to present evidence-based information about breast cancer. The experience of those participating on the chat are not endorsements. Questions directed to any physician or health care professional participating about your personal health is discouraged.Education Example:Gonorrhea social media campaignhttp://www.irishexaminer.com/archives/2013/1210/ireland/social-media-used-to-fight-apostrendingapos-gonorrhoea-252171.html
- What accounts for the relative longevity of this particular online community? Is it because of the founder’s leadership and her continuing involvement in this community? Or is there a core group of members who are also actively and persistently involved in this community? Second, we are interested in learning more about the composition of this community in general, as well as more specifically whether one’s professional role/title determines a person’s centrality within this community. This will allow us to understand generally how professional roles affect online conversational dynamics, and more specifically whether this online community is a welcoming place for a wide range of professionals or instead primarily dominated by professionals from a particular group.
- The method here is based on finding “Who retweeted/ replied/mentioned whom”The higher number of exchanged messages is usually interpreted as stronger ties between people.Since tweet is an instance of a social interaction between two or more people who know (or will know) each other, it is reasonable to assume that the number of messagesexchanged between two people is a good indicator of the existence and the strength of their social tie.
- the total number of messages contributed to the discussions during the studied periodthe number of times a person is mentioned or replied to; this is known in SNA as in-degree centrality, and signifies the prestige given to that individual by others in the networkthe number of times a person mentions or replies to others; this is known in SNA as out-degree centrality, and signifies the influence that person has as they make their views known to others (Hanneman & Riddle, 2005, see chapter 10)
- Prof.role influence In-DegreeTOOLS>STATISTICS>ANOVA--------------------------------------------------------------------------------Dependent variable: "twitter_hcsmca_attr" Col 3Independent variable: "twitter_hcsmca_attr" Col 1# of permutations: 5000Random seed: 13335 ANALYSIS OF VARIANCE Source DF SSQ F-Statistic Significance ============== ============== ============== ============== ============== Treatment 10 501.57 3.5968 0.0030 Error 475 6623.83 Total 485 7125.40R-Square/Eta-Square: 0.070----------------------------------------Running time: 00:00:01Output generated: 20 Jan 13 11:49:32Copyright (c) 2002-11 Analytic Technologies*******************************Prof.roles don’t’ relate to OUT-DEGREEOOLS>STATISTICS>ANOVA--------------------------------------------------------------------------------Dependent variable: "twitter_hcsmca_attr" Col 4Independent variable: "twitter_hcsmca_attr" Col 1# of permutations: 5000Random seed: 9933 ANALYSIS OF VARIANCE Source DF SSQ F-Statistic Significance ============== ============== ============== ============== ============== Treatment 10 100.50 1.8325 0.0778 Error 475 2604.90 Total 485 2705.40R-Square/Eta-Square: 0.037----------------------------------------Running time: 00:00:01Output generated: 20 Jan 13 11:52:11Copyright (c) 2002-11 Analytic Technologies*************************************There is no preferential attachment between groupsNETWORK AUTOCORRELATION WITH CATEGORICAL ATTRIBUTES--------------------------------------------------------------------------------Network/Proximities: twitter_hcsmca (C:\_work\_Ongoing research\Health Care Social Media Twitter\Network\Jan20\twitter_hcsmca)Attribute(s): "twitter_hcsmca_attr" Col 1Method: Structural Blockmodel# of Permutations: 5000Random seed: 482Warning: Attribute vector has been recoded.Here is a translation table: Old Code New Code ======== ======== 1 => 1 2 => 2 3 => 3 4 => 4 5 => 5 6 => 6 7 => 7 8 => 8 9 => 9 10 => 10 11 => 11Density Table 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 1 1 0.004 0.006 0.000 0.004 0.001 0.005 0.002 0.000 0.000 0.000 0.000 2 2 0.002 0.002 0.000 0.004 0.005 0.002 0.001 0.003 0.002 0.000 0.008 3 3 0.003 0.001 0.000 0.005 0.003 0.002 0.001 0.001 0.001 0.000 0.000 4 4 0.006 0.005 0.000 0.009 0.007 0.004 0.002 0.007 0.002 0.004 0.000 5 5 0.003 0.002 0.000 0.007 0.010 0.001 0.001 0.011 0.010 0.004 0.005 6 6 0.004 0.003 0.000 0.007 0.001 0.003 0.000 0.001 0.002 0.002 0.000 7 7 0.001 0.002 0.000 0.005 0.005 0.002 0.003 0.005 0.003 0.000 0.003 8 8 0.002 0.000 0.000 0.003 0.011 0.005 0.000 0.000 0.005 0.000 0.000 9 9 0.000 0.002 0.000 0.006 0.014 0.001 0.002 0.005 0.013 0.000 0.000 10 10 0.000 0.007 0.000 0.002 0.002 0.003 0.000 0.000 0.000 0.022 0.000 11 11 0.000 0.008 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.000Density table saved as dataset densitytableExpected values saved as dataset anovadensity_expectedvaluesNumber of permutations performed: 5000MODEL FITR-square Adj R-Sqr Probability # of Obs-------- --------- ----------- ----------- 0.003 0.002 0.0046 235710REGRESSION COEFFICIENTS Un-stdizedStdized Proportion Proportion Independent Coefficient Coefficient Significance As Large As Small ----------- ----------- ----------- ------------ ----------- ----------- Intercept 0.000000 0.000000 0.9998 0.9998 0.7594 1-1 0.004301 0.004833 0.2892 0.2892 0.7944 1-2 0.006452 0.007249 0.1064 0.1064 0.9306 1-3 0.000000 0.000000 0.9680 0.9680 0.0340 1-4 0.003812 0.008159 0.2858 0.2858 0.7656 1-5 0.001290 0.001869 0.8410 0.8410 0.2436 1-6 0.004921 0.007739 0.1676 0.1676 0.8826 1-7 0.001744 0.003068 0.8212 0.8212 0.2576 1-8 0.000000 0.000000 0.8866 0.8866 0.3968 1-9 0.000000 0.000000 0.9094 0.9094 0.3178 1-10 0.000000 0.000000 0.8702 0.8702 0.4856 1-11 0.000000 0.000000 0.8112 0.8112 0.7552 2-1 0.002151 0.002416 0.6358 0.6358 0.5108 2-2 0.002299 0.002499 0.5596 0.5596 0.5808 2-3 0.000000 0.000000 0.9676 0.9676 0.0336 2-4 0.003939 0.008296 0.2500 0.2500 0.7936 2-5 0.004667 0.006651 0.2020 0.2020 0.8604 2-6 0.002260 0.003497 0.6766 0.6766 0.4198 2-7 0.000901 0.001560 0.9296 0.9296 0.1120 2-8 0.002564 0.001868 0.5286 0.5286 0.6938 2-9 0.002083 0.001683 0.6156 0.6156 0.6060 2-10 0.000000 0.000000 0.8640 0.8640 0.4922 2-11 0.008333 0.003369 0.2158 0.2158 0.9260 3-1 0.002900 0.005590 0.5064 0.5064 0.5586 3-2 0.001498 0.002842 0.8152 0.8152 0.2396 3-3 0.000128 0.000410 0.9682 0.9682 0.0316 3-4 0.004903 0.017534 0.0308 0.0308 0.9732 3-5 0.002921 0.007126 0.5222 0.5222 0.5300 3-6 0.002285 0.006045 0.7394 0.7394 0.3042 3-7 0.000911 0.002691 0.9626 0.9626 0.0404 3-8 0.000864 0.001083 0.8208 0.8208 0.2956 3-9 0.000702 0.000975 0.8734 0.8734 0.2156 3-10 0.000000 0.000000 0.9284 0.9284 0.2110 3-11 0.000000 0.000000 0.8534 0.8534 0.5462 4-1 0.006158 0.013180 0.0498 0.0498 0.9592 4-2 0.004848 0.010210 0.1490 0.1490 0.8740 4-3 0.000409 0.001461 0.9682 0.9682 0.0316 4-4 0.008757 0.034489 0.0000 0.0000 0.9998 4-5 0.006727 0.018203 0.0074 0.0074 0.9932 4-6 0.004006 0.011750 0.2160 0.2160 0.8098 4-7 0.002088 0.006835 0.8276 0.8276 0.1978 4-8 0.006993 0.009733 0.0860 0.0860 0.9364 4-9 0.001705 0.002630 0.6986 0.6986 0.3910 4-10 0.003636 0.004442 0.3642 0.3642 0.7190 4-11 0.000000 0.000000 0.8746 0.8746 0.4924 5-1 0.003226 0.004673 0.4386 0.4386 0.6542 5-2 0.002000 0.002851 0.6922 0.6922 0.4214 5-3 0.000000 0.000000 0.9682 0.9682 0.0316 5-4 0.006545 0.017711 0.0026 0.0026 0.9974 5-5 0.010204 0.018549 0.0038 0.0038 0.9974 5-6 0.001017 0.002026 0.9292 0.9292 0.0966 5-7 0.000811 0.001806 0.9580 0.9580 0.0508 5-8 0.010769 0.010122 0.0286 0.0286 0.9828 5-9 0.010000 0.010424 0.0284 0.0284 0.9830 5-10 0.004000 0.003299 0.3546 0.3546 0.7880 5-11 0.005000 0.002609 0.3106 0.3106 0.8538 6-1 0.003827 0.006019 0.3348 0.3348 0.7336 6-2 0.002825 0.004371 0.5240 0.5240 0.5710 6-3 0.000000 0.000000 0.9682 0.9682 0.0316 6-4 0.006934 0.020336 0.0012 0.0012 0.9988 6-5 0.001356 0.002702 0.8806 0.8806 0.1620 6-6 0.003214 0.006892 0.4404 0.4404 0.6086 6-7 0.000458 0.001107 0.9672 0.9672 0.0350 6-8 0.001304 0.001331 0.7262 0.7262 0.4370 6-9 0.002119 0.002398 0.6196 0.6196 0.5166 6-10 0.001695 0.001518 0.6394 0.6394 0.5524 6-11 0.000000 0.000000 0.8266 0.8266 0.6318 7-1 0.001308 0.002301 0.8512 0.8512 0.2144 7-2 0.001802 0.003119 0.7508 0.7508 0.3290 7-3 0.000304 0.000897 0.9682 0.9682 0.0316 7-4 0.005037 0.016485 0.0264 0.0264 0.9784 7-5 0.004595 0.010237 0.1556 0.1556 0.8754 7-6 0.002061 0.004982 0.7770 0.7770 0.2680 7-7 0.002592 0.006951 0.5968 0.5968 0.4398 7-8 0.005198 0.005939 0.2052 0.2052 0.8508 7-9 0.002534 0.003211 0.5448 0.5448 0.5696 7-10 0.000000 0.000000 0.9136 0.9136 0.2628 7-11 0.003378 0.002144 0.3766 0.3766 0.7854 8-1 0.002481 0.001837 0.5642 0.5642 0.6700 8-2 0.000000 0.000000 0.8898 0.8898 0.3738 8-3 0.000000 0.000000 0.9558 0.9558 0.0812 8-4 0.002797 0.003893 0.5300 0.5300 0.5870 8-5 0.010769 0.010122 0.0152 0.0152 0.9924 8-6 0.005215 0.005323 0.2018 0.2018 0.8780 8-7 0.000000 0.000000 0.9444 0.9444 0.1252 8-8 0.000000 0.000000 0.8276 0.8276 0.6984 8-9 0.004808 0.002559 0.3572 0.3572 0.8532 8-10 0.000000 0.000000 0.8208 0.8208 0.7052 8-11 0.000000 0.000000 0.7868 0.7868 0.8650 9-1 0.000000 0.000000 0.9116 0.9116 0.2932 9-2 0.002083 0.001683 0.6374 0.6374 0.5946 9-3 0.000000 0.000000 0.9604 0.9604 0.0596 9-4 0.006250 0.009644 0.0592 0.0592 0.9578 9-5 0.013750 0.014333 0.0002 0.0002 0.9996 9-6 0.001059 0.001199 0.8372 0.8372 0.2948 9-7 0.001689 0.002140 0.7686 0.7686 0.3616 9-8 0.004808 0.002559 0.3656 0.3656 0.8596 9-9 0.012500 0.007146 0.0710 0.0710 0.9648 9-10 0.000000 0.000000 0.8324 0.8324 0.6572 9-11 0.000000 0.000000 0.7904 0.7904 0.8448 10-1 0.000000 0.000000 0.8806 0.8806 0.4432 10-2 0.006667 0.004260 0.2100 0.2100 0.9130 10-3 0.000000 0.000000 0.9468 0.9468 0.1292 10-4 0.001818 0.002221 0.7254 0.7254 0.4186 10-5 0.002000 0.001649 0.6386 0.6386 0.5940 10-6 0.003390 0.003036 0.4414 0.4414 0.7224 10-7 0.000000 0.000000 0.9316 0.9316 0.1832 10-8 0.000000 0.000000 0.8190 0.8190 0.7060 10-9 0.000000 0.000000 0.8332 0.8332 0.6522 10-10 0.022222 0.007782 0.0438 0.0438 0.9876 10-11 0.000000 0.000000 0.7782 0.7782 0.9000 11-1 0.000000 0.000000 0.8182 0.8182 0.7194 11-2 0.008333 0.003369 0.2438 0.2438 0.9378 11-3 0.000000 0.000000 0.8844 0.8844 0.4032 11-4 0.000000 0.000000 0.9584 0.9584 0.3440 11-5 0.000000 0.000000 0.8090 0.8090 0.5910 11-6 0.004237 0.002402 0.4194 0.4194 0.8350 11-7 0.000000 0.000000 0.8222 0.8222 0.4802 11-8 0.000000 0.000000 0.7762 0.7762 0.8564 11-9 0.000000 0.000000 0.7794 0.7794 0.8356 11-10 0.000000 0.000000 0.7714 0.7714 0.8978----------------------------------------Running time: 00:38:18Output generated: 20 Jan 13 11:38:15UCINET 6.339 Copyright (c) 2002-11 Analytic TechnologiesOUt-Degree ->Inv/Group (Individual Accounts are more likely to mention someone, have a higher in degree centrality)TOOLS>STATISTICS>T-TEST--------------------------------------------------------------------------------Dependent variable: "twitter_hcsmca_attr" col 4Independent variable: "twitter_hcsmca_attr" col 2# of permutations: 10000Random seed: 12137Basic statistics on each group. 1 2 Group 1 Group 2 -------- -------- 1 Mean 1.253 1.626 2 StdDev 1.530 2.629 3 Sum 183.000 553.000 4 Variance 2.340 6.910 5 SSQ 571.000 3249.000 6 MCSSQ 341.623 2349.562 7 Euc Norm 23.896 57.000 8 Minimum 0.000 0.000 9 Maximum 11.000 27.000 10 N of Obs 146.000 340.000 11 N Missing 340.000 146.000SIGNIFICANCE TESTS Difference ...One-Tailed Tests... Two-Tailed in Means Group 1 > 2 Group 2 > 1 Test ============== ============== ============== ============== -0.373 0.954 0.051 0.1168************************************************************NETWORK AUTOCORRELATION WITH CATEGORICAL ATTRIBUTES--------------------------------------------------------------------------------Network/Proximities: twitter_hcsmca (C:\Documents and Settings\an578458\My Documents\_Ongoing research\Health Care Social Media Twitter\Network\Jan20\twitter_hcsmca)Attribute(s): "twitter_hcsmca_attr" Col 1Method: Variable Homophily# of Permutations: 5000Random seed: 850Warning: Attribute vector has been recoded.Here is a translation table: Old Code New Code ======== ======== 1 => 1 2 => 2 3 => 3 4 => 4 5 => 5 6 => 6 7 => 7 8 => 8 9 => 9 10 => 10 11 => 11Density Table 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- ----- 1 1 0.004 0.006 0.000 0.004 0.001 0.005 0.002 0.000 0.000 0.000 0.000 2 2 0.002 0.002 0.000 0.004 0.005 0.002 0.001 0.003 0.002 0.000 0.008 3 3 0.003 0.001 0.000 0.005 0.003 0.002 0.001 0.001 0.001 0.000 0.000 4 4 0.006 0.005 0.000 0.009 0.007 0.004 0.002 0.007 0.002 0.004 0.000 5 5 0.003 0.002 0.000 0.007 0.010 0.001 0.001 0.011 0.010 0.004 0.005 6 6 0.004 0.003 0.000 0.007 0.001 0.003 0.000 0.001 0.002 0.002 0.000 7 7 0.001 0.002 0.000 0.005 0.005 0.002 0.003 0.005 0.003 0.000 0.003 8 8 0.002 0.000 0.000 0.003 0.011 0.005 0.000 0.000 0.005 0.000 0.000 9 9 0.000 0.002 0.000 0.006 0.014 0.001 0.002 0.005 0.013 0.000 0.000 10 10 0.000 0.007 0.000 0.002 0.002 0.003 0.000 0.000 0.000 0.022 0.000 11 11 0.000 0.008 0.000 0.000 0.000 0.004 0.000 0.000 0.000 0.000 0.000Density table saved as dataset densitytableExpected values saved as dataset anovadensity_expectedvaluesNumber of permutations performed: 5000MODEL FITR-square Adj R-Sqr Probability # of Obs-------- --------- ----------- ----------- 0.001 0.001 0.0002 235710REGRESSION COEFFICIENTS Un-stdizedStdized Proportion Proportion Independent Coefficient Coefficient Significance As Large As Small ----------- ----------- ----------- ------------ ----------- ----------- Intercept 0.002812 0.000000 0.9998 0.9998 0.0000 Group 1 0.001489 0.001673 0.2126 0.2126 0.7872 Group 2 -0.000514 -0.000558 0.5658 0.4340 0.5658 Group 3 -0.002685 -0.008625 0.0020 0.9978 0.0020 Group 4 0.005945 0.023413 0.0000 0.0000 0.9998 Group 5 0.007392 0.013437 0.0024 0.0024 0.9974 Group 6 0.000402 0.000862 0.3644 0.3644 0.6354 Group 7 -0.000221 -0.000592 0.4862 0.5136 0.4862 Group 8 -0.002812 -0.001296 0.6894 0.3104 0.6894 Group 9 0.009688 0.005538 0.0362 0.0362 0.9636 Group 10 0.019410 0.006797 0.0130 0.0130 0.9868 Group 11 -0.002812 -0.000360 0.9682 0.0316 0.9682----------------------------------------Running time: 00:01:39Output generated: 20 Jan 13 11:57:34UCINET 6.282 Copyright (c) 2002-9 Analytic Technologies
- there is no apparent preferential attachment among people in the same professional group. In other words, the formation of connections among community members is not necessarily constrained by their professional status. This finding was supported by an Analysis of Variance density test using both the Structural Blockmodel technique (that examines “whether the different classes have significantly different interaction patterns”), and also with the Variable Homophily model (which “assumes that each group or class of actors has a different homophilic tendency”, Borgatti et al., 2002; where homophily is the tendency for connection based on social similarity). Based on this test (run with the 5,000 permutations), the professional roles only explain 0.2% of the total variance (p=0.005) when run with the Structural Blockmodel and only 0.1% (p=0.0002) with the Variable Homophily model. This result indicates connections are more prevalent across members with different professional backgrounds and occupations in this community, which in turn may suggest that this is a welcoming environment that stimulates knowledge exchange and learning across professional boundaries. **************************************Betweeness and Roles are not significantTOOLS>STATISTICS>ANOVA--------------------------------------------------------------------------------Dependent variable: "hcsmca_dec8_2012-cent" Col 3Independent variable: "hcsmca_dec8_2012_attr" Col 3# of permutations: 5000Random seed: 13262 ANALYSIS OF VARIANCE Source DF SSQ F-Statistic Significance ============== ============== ============== ============== ============== Treatment 10 0.00 1.7467 0.1002 Error 338 0.00 Total 348 0.00R-Square/Eta-Square: 0.049----------------------------------------Running time: 00:00:01Output generated: 06 Jan 13 09:41:00Copyright (c) 2002-11 Analytic Technologies
- The Social Medial Lab was started in 2010 by Dr. AnatoliyGruzd at Dalhousie University.The lab is a multidisciplinary lab.Our members and collaborators includes of both computer scientist and social scientists.As a group, we study how social media is changing the way people communicate and disseminate information .We are also developing new web applicationsfor discovering and visualizinginformation and online social networks
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