Social Media Mining
for Public Health Monitoring and Surveillance
At Pacific Symposium on Biocomputing (PSB) 2016
January 4-8, 2016
The Big Island of Hawaii
The use of social media for health monitoring and surveillance has indeed many drawbacks and difficulties, particularly if done automatically. For example, traditional NLP methods that are used on longer texts have proven to be inadequate when applied to short texts, such as those found in Twitter. Something seemingly simple, such as searching and collecting relevant postings has also proven to be quite challenging, given the amount of data and semantic heterogeneity (how people refer to the topic of interest in colloquial terms) inherent to the media.
This session invites researchers who are interested in automatic methods for the collection, extraction, representation, analysis, and validation of social media data for public health surveillance and monitoring, including epidemiological and behavioral studies. It serves as a unique forum to discuss novel approaches to text and data mining methods that respond to the specific requirements of social media and that can prove invaluable for public health surveillance.
Topics of interest include, but are not limited to:
· Detection and extraction of adverse drug reaction mentions in social media
· Mapping of disease and symptoms mentions in social media to standardized vocabularies
· Deriving prescription drug use and off-label use trends from social media
· Social media in precision medicine
· Virus spread monitoring using social media
· Myths and truths about disease discussed in social media
· Drug abuse and alcoholism incidence monitoring in social media
· Prediction/detection of abusive behavior using social media postings
· Disease incidence studies using social media
· Sentinel event detection using social media
· Search, collection, and analysis techniques for public health informatics using social media
· Natural language processing methods for health-related consumer-generated (colloquial) text
· Classifying health-related messages in social media
· Sentiment analysis of social media messages for disease surveillance
Graciela Gonzalez (Arizona State University)
Matthew Scotch (Arizona State University)
Karen Smith (Regis University)
John Brownstein (Harvard Medical School)
Abeed Sarker (Arizona State University)
Michael Paul (Johns Hopkins University)
Azadeh Nikfarjam (Arizona State University)
Please note that the submitted papers are reviewed and accepted on a competitive basis. At least three reviewers will be assigned to each submitted manuscript.
July 27, 2015 August 3, 2015
Notification of paper acceptance: September 14, 2015
Final paper deadline: October 5, 2015 at 11:59pm PT
Registration opens: August 1, 2015 at noon PT
Travel award applications
PSB has been able to offer travel support to many attendees in the past. However, please note that no one is guaranteed travel support.
Available: August 1, 2015 at noon PT
Deadline: October 5, 2015 at noon PT
Decisions will be announced in mid-October.
For details, please see http://psb.stanford.edu/keydates.html
Poster presenters will be provided with an easel and a poster board 32"W x 40"H (80x100cm). One poster from each paid participant is accepted.
Available: August 1
Deadline: November 17, 2015 at noon PT
Decisions will be announced in mid-October.
Please see the PSB paper format template and instructions at http://psb.stanford.edu/psb-online/psb-submit.
The file formats we accept are: postscript (*.ps) and Adobe Acrobat (*.pdf)). Attached files should be named with the last name of the first author (e.g. altman.ps or altman.pdf). Hardcopy submissions or unprocessed TeX or LaTeX files will be rejected without review.
Each paper must be accompanied by a cover letter. The cover letter must state the following:
• The email address of the corresponding author.
• The specific PSB session that should review the paper or abstract.
• The submitted paper contains original, unpublished results, and is not currently under consideration elsewhere.
• All co-authors concur with the contents of the paper.
Submitted papers are limited to twelve (12) pages in our publication format. Please format your paper according to instructions found at http://psb.stanford.edu/psb-online/psb-submit/. If figures cannot be easily resized and placed precisely in the text, then it should be clear that with appropriate modifications, the total manuscript length would be within the page limit.
Social Media Mining Shared Task
This year we hold a social media mining shared task which consists of different sub-tasks related to classification and extraction of adverse drug reaction information from social media. More details about the task descriptions can be found here: