1. Shared Deliberation in Facebook Support Groups for Sickle Cell Patients and Caregivers
Rosta Farzan and Keyang Zheng, School of Computing and Information
Aisha Walker, Vascular Medicine Institute
Charles Jonassaint, Department of Medicine
University of Pittsburgh

2. Social media: an important resource for seeking health-related information and social support
People influence each other while socializing offline or online.
But… We do not know how!
We aim to answer: How does interactions on online health support groups like Facebook influence the participants, especially their decisions and attitudes towards their health concerns.
Particularly, we are studying two Facebook groups, Sickle Cell Warrior and Sickle Cell Unite dedicated to Sickle cell patients and caregivers

3. Users’ discussions on online health discussion forums often involve strong sentiment
This sentiment can change as a result of information exchange with other members
The change of sentiment can be an indication of members’ influence on each other
RQ: how does participation in online discussions and interaction with others converge or diverge the valence of the discussion?

4. Research Methods To answer our research question:
Collect posts and comments data using Facebook public API
Generate a labeled dataset using Mechanical Turk
Developed a computational model to classify each message to positive or negative.[1]
Analyze the sentiment changes within interaction around posts
[1] Qiu, B., Zhao, K., Mitra, P., Wu, D., Caragea, C., Yen, J., … Portier, K. (2011). Get Online Support, Feel Better -- Sentiment Analysis and Dynamics in an Online Cancer Survivor Community IEEE Third Int’l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int’l Conference on Social Computing, 274–281.

5. Gathering posts and comments from Facebook
With the permission of both groups’ moderator or owner, we extracted all posts and comments for a period of 10 months
From Jun 2015 to April 2016
Sickle Cell Warriors (moderated public Facebook page)
Sickle Cell Unite (closed private group)

6. A typical example of a Facebook post interaction
Post and comments come from Sickle Cell Warriors, Inc

7. Computational model for automatic sentiment classification of posts and comments
Features of the message:
Length of the message (word count)
Average length of words
Occurrences of people’s name
Punctuations: question or exclamation marks
Positive and negative words
Strength of the positive & negative words (e.g. “so supportive”) [2]
Hi warriors, thank you all for being so supportive and sharing. I got a question about evenflo. do you use evenflo ? does it work and how, what exactly it does ? I don't have pain crisis, but I easily get pneumonia. Does evenflo work for pneumonia ?
[2] Thelwall, M., Buckley, K., Paltoglou, G. Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544–2558.

8. Collecting Training Data through Mechanical Turk
Labeled data is needed for learning what features are associated with positive or negative sentiment
Mechanical Turk is a crowdsourcing platform allowing to post micro task to be completed by a large number of users
We used Mechanical Turk to label 262 randomly selected Facebook posts or comments.
Every posts and comments were judged by 5 different users.

9. Collecting Training Data through Mechanical Turk

10. How positive or negative discussion are in general?
Comments of positive posts
Unite
Warriors
Positive comments
81.1%
71.0%
Negative comments
13.6%
20.7%
Unite
Warriors
Total number of posts
2854
1,063
Positive posts
61.3%
52.6%
Negative posts
27.5%
36.7%
Posts with comments
44.3%
58.0%
Comments of negative posts
Unite
Warriors
Positive comments
69.3%
58.7%
Negative comments
23.9%
33.0%

11. Sentiment changes over time
Original post
All comments in response to a post are chronologically listed (first comment would be comment 1) Replies to comments also considered as comments in chronological order they have been posted 1 3
comments 2 4 5

12. Classify valence of posts and comments
Observe the dynamic of discussions over time: how sentiment changes as a result of members’ interaction Distinguishing dynamics of interaction in response to positive vs. negative original post Applied to posts from each group separately to compare the dynamics of public vs. private groups 1 3 2 4 5

13. Discussions are more likely to converge to positivity
Sentiment Probability
As more discussion happens, both groups’ comment sentiment start to converge to a relatively positive point.
Post with negative sentiment influence the interaction, first few comments show more negative than the positive posts
Private group seams have a overall more positive environment, but with only a bit more.
nth comment

14. Discussions are more likely to converge to positivity
Sentiment Probability
As more discussion happens, both groups’ comment sentiment start to converge to a relatively positive point.
Post with negative sentiment influence the interaction, first few comments show more negative than the positive posts
Private group seams have a overall more positive environment, but with only a bit more.
nth comment

15. Discussions are more likely to converge to positivity
Sentiment Probability
As more discussion happens, both groups’ comment sentiment start to converge to a relatively positive point.
Post with negative sentiment influence the interaction, first few comments show more negative than the positive posts
Private group seams have a overall more positive environment, but with only a bit more.
nth comment

16. Discussions are more likely to converge to positivity
Sentiment Probability
As more discussion happens, both groups’ comment sentiment start to converge to a relatively positive point.
Post with negative sentiment influence the interaction, first few comments show more negative than the positive posts
Private group seams have a overall more positive environment, but with only a bit more.
nth comment

17. Next step… Finer grained analysis
Specifically, with respect to posts focusing on discussions of therapy and medications (e.g. HU)
Tracking repeated participation of the same member
Key role of participants shifting the valence in one direction of other

18. Keyang Zheng kez20@pitt.edu
Sentiment analysis can be used as a lens to understand online health support groups
Thank you! Questions and Comments Please 
Keyang Zheng