Course: CS 8803 Data Analytics for Well-being
Term: Spring 2016
Location: College of Computing 102
Time: Monday & Wednesday 3:00 - 4:25 pm
Office Hours: Wednesday 1-2pm (TSRB 341), or by appointment
Overview
The primary goal of this course is to develop a broad understanding of the emergent field of modeling and analyzing large-scale data, such as online and social media data sources, for improved well-being and health. We will:
- Cover behavioral and social science theories that bear relevance to this area;
- Review state-of-the-art data analytic and modeling techniques to understand health behaviors and well-being states;
- Apply computational and machine learning driven approaches to infer and analyze well-being and health related attributes;
- Analyze large-scale data (e.g., obtained from social media, mobile health sensors) to understand, model or infer a health phenomenon;
- Identify the theoretical, empirical, analytical and ethical challenges in employing data analytics and modeling for understanding and modeling well-being and health.
Pre-requisites. No formal pre-requisites are needed, but background in programming (e.g., Python, R) and in application of off-the-shelf machine learning tools/methods is required.
Format. The pedagogical structure of the class will include in-class and off-class discussions around class readings, hands-on classroom activities, take-home assignments, in-class presentations, a writing critique, and a term project. There will be no exam, however participation in class is key. Students will need to participate in both in-class and off-class discussion to demonstrate their understanding of the class material, as well as raise interesting questions or points. Off-class discussion will take place on a private social media page (an instructor moderated blog e.g., Tumblr or Piazza). This social media page will also be used to share reading reflections on class readings by students – students can pick to write reflections on any ten of the suggested class readings in schedule. Readings will typically be research papers. Critique writing will involve taking an issue of attention or interest in popular media, relating to the topics covered in class, and developing a critical reflection of it from a technical perspective. The final presentations of the term projects will be in the form of an open poster presentation.
Submission Policy. All materials (except reading reflections and presentations) will need to be submitted via the course’s T-Square site.
Late Policy. Students need to submit all of their materials on or before the deadline to qualify for 100% credit. 24 hours delay will result in 25% penalty; 48 hours late submissions will incur 50% penalty. Materials submitted past 48 hours will not be accepted, and will entered a zero grade.
Academic Integrity. All assigned work is expected to be individual, except where explicitly indicated otherwise. You are encouraged to discuss the assignments with your classmates; however, what you hand in should be your own work. For more information, please review the Georgia Tech Honor Code.
Grading Criteria
Off-class discussion, participation in class blog –10% (5% will go toward the 10 reading reflections)
In-class participation and discussion - 10%
Class presentation of readings – 5%
Critique writing –5%
Take-home assignments – 30% (three, 10% each)
Term project –40% (project proposal and literature review 5%; development 25%; mid-term presentation 5%; final presentation 5%)
Class Schedule *
| Week 1 | ||
| Jan 11, 2016 | Introduction | |
| Jan 13, 2016 | Background: Big Data and Well-being | |
| Week 2 | ||
| Jan 18, 2016 | MLK Day | |
| Jan 20, 2016 | Behavioral Science, Health and Well-being | |
| Week 3 | ||
| Jan 25, 2016 | Social and Emotional Support | |
| Jan 27, 2016 | Networks and Well-being | |
| Week 4 | ||
| Feb 1, 2016 | Methods: Data Modeling I | |
| Writing Critique due | ||
| Feb 3, 2016 | Methods: Data Modeling II | |
| Week 5 | ||
| Feb 8, 2016 | Methods: Data Modeling III | |
| Feb 10, 2016 | Methods: Data Modeling IV | |
| Week 6 | ||
| Feb 15, 2016 | Methods: Data Modeling V | |
| Feb 17, 20 16 | Methods: Data Modeling VI | |
| Week 7 | ||
| Feb 22, 20 16 | Analytical Review I | |
| Feb 24, 20 16 | Analytical Review II | |
| Project Proposals due | ||
| Week 8 | ||
| Feb 29, 2016 | Guest Lecture (Travel to CSCW 2016) | |
| Mar 2, 2016 | Guest Lecture (Travel to CSCW 2016) | |
| Week 9 | ||
| Mar 7, 2016 | Prediction I | |
| Assignment I due | ||
| Mar 9, 2016 | Prediction II | |
| Week 10 | ||
| Mar 14, 2016 | Prediction III | |
| Mar 16, 2016 | Search Logs I | |
| Mid-term Progress Reports due | ||
| Week 11 | ||
| Mar 21, 2016 | Spring Break | |
| Mar 23, 2016 | Spring Break | |
| Week 12 | ||
| Mar 28, 2016 | Search Logs II | |
| Assignment II due | ||
| Mar 30, 2016 | Analytical Review III | |
| Week 13 | ||
| Apr 4, 2016 | Major Life Events | |
| Apr 6, 2016 | Self-Disclosure | |
| Week 14 | ||
| Apr 11, 2016 | Online Health Communities | |
| Apr 13, 2016 | Mobile Health I | |
| Week 15 | ||
| Apr 18, 2016 | Mobile Health II | |
| Assignment III due | ||
| Apr 20, 2016 | Health Interventions | |
| Week 16 | ||
| Apr 25, 2016 | Privacy and Ethics | |
| May 2, 2016 | Final Term Project Presentations | |
| Final Project Reports due |
Weekly Readings *
Week 1 (Jan 13): Background: Big Data and Well-being
From smart to cognitive phones [pdf]
How social media will change public health [pdf]
Week 2 (Jan 20): Behavioral Science, Health and Wellbeing
Writing about Emotional Experiences as a Therapeutic Process [pdf]
The Collective Dynamics of Smoking in a Large Social Network [pdf]
Week 3 (Jan 25): Social and Emotional Support
Forum77: An Analysis of an Online Health Forum Dedicated to Addiction Recovery [pdf]
#thyghgapp: Instagram Content Moderation and Lexical Variation in Pro-Eating Disorder Communities [pdf]
Week 3 (Jan 27): Networks and Well-being
Network Analysis in Public Health: History, Methods, and Applications [pdf]
Week 4 (Feb 1): Data Modeling I
Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter [pdf]
Week 4 (Feb 3): Data Modeling II
Detecting influenza epidemics using search engine query data [pdf]
Discovering Health Topics in Social Media Using Topic Models [pdf]
Week 5 (Feb 8): Data Modeling III
Modeling Spread of Disease from Social Interactions [pdf]
The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city [pdf]
Week 5 (Feb 10): Data Modeling IV
Facebook Displays as Predictors of Binge Drinking: From the Virtual to the Visceral [pdf]
nEmesis: Which Restaurants Should You Avoid Today? [pdf]
Week 6 (Feb 15): Data Modeling V
Unraveling abstinence and relapse: smoking cessation reflected in social media [pdf]
Detection of Cyberbullying Incidents on the Instagram Social Network [pdf]
Week 6 (Feb 17): Data Modeling VI
"Narco" Emotions: Affect and Desensitization in Social Media during the Mexican Drug War [pdf]
Resilience in collaboration: technology as a resource for new patterns of action [pdf]
Week 9 (Mar 7): Prediction I
Predicting Depression via Social Media [pdf]
Quantifying mental health signals in twitter [pdf]
Week 9 (Mar 9): Prediction II
Estimating county health statistics with Twitter [pdf]
Analyzing the Language of Food on Social Media [pdf]
Week 10 (Mar 14): Prediction III
Automatic Crime Prediction using Events Extracted from Twitter Posts [pdf]
Using matched samples to estimate the effects of exercise on mental health from Twitter [pdf]
Week 10 (Mar 16): Search Logs I
Early identification of adverse drug reactions from search log data [pdf]
Cyberchondria: Studies of the Escalation of Medical Concerns in Web Search [pdf]
Week 12 (Mar 28): Search Logs II
Seeking insights about cycling mood disorders via anonymized search logs [pdf]
From Cookies to Cooks: Insights on Dietary Patterns via Analysis of Web Usage Logs [pdf]
Week 13 (Apr 4): Major Life Events
Using Facebook after losing a job: Differential benefits of strong and weak ties [pdf]
Major Life Changes and Behavioral Markers in Social Media: Case of Childbirth [pdf]
Week 13 (Apr 6): Self-Disclosure
Mental Health Discourse on Reddit: Self-disclosure, Social Support, and Anonymity [pdf]
Understanding Social Media Disclosures of Sexual Abuse Through the Lenses of Support Seeking and Anonymity [pdf]
Week 14 (Apr 11): Online Health Communities
Neo-tribes: The power and potential of online communities in health care [pdf]
Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data [pdf]
Week 14 (Apr 13): Mobile Health I
StudentLife: Assessing Mental Health, Academic Performance and Behavioral Trends of College Students using Smartphones [pdf]
Stress and Multitasking in Everyday College Life: An Empirical Study of Online Activity [pdf]
Week 15 (Apr 18): Mobile Health II
Trajectories of Depression: Unobtrusive Monitoring of Depressive States by means of Smartphone Mobility Traces Analysis [pdf]
Sensing the 'Health State' of a Community [pdf]
Week 15 (Apr 20): Interventions
Defining Internet-Supported Therapeutic Interventions [pdf]
The Role of Facebook in Crush the Crave, a Mobile- and Social Media-Based Smoking Cessation Intervention: Qualitative Framework Analysis of Posts [pdf]
Week 16 (Apr 25): Privacy and Ethics
Engineering the public: Big data, surveillance and computational politics [pdf]
Data, privacy, and the greater good [pdf]
* Topics to be covered and the corresponding readings are subject to change. Please always check the online schedule.