Twitter, MyFitnessPal and #FoodPorn: Using Social Media for Healthy

Lifestyle Tracking

Ingmar Weber

Qatar Computing Research Institute

Desert

Forrest

24% Indian

16% Nepali

15% Qatari

11% Filipino

Social Computing Areas Understanding multi-cultural societies through social media - Cross-lingual communication, political violence, …

Computing for human development and crisis management

- Crisis mapping, international migration, …

Analytics for journalism, online news, and social media

- Forecasting news interest, creating personal relevance, …

Urban mobility and smart cities [starting]

- Understanding traffic abnormalities, studying spatial segregation, …

Selected Demos

http://tinyurl.com/SGP-Twitter (ICWSM ’14 Demo Paper)

http://fast.qcri.org/ (CSCW ‘14 Demo Paper)

http://aidr.qcri.org/ (WWW ‘14 Demo Paper)

Motivation: Obesity in Qatar

• >75% of Qatari adults are overweight

• ~50% of Qatari adults are obese (BMI >= 30)

• 16% diabetes

You Tweet What You Eat: Studying Food Consumption

Through Twitter

Joint work with S. Abbar and Y. Mejova

CHI 2015

“Pointless Babble” == Great Data! “Twitter Study Reveals Interesting Results - About Usage 40%

is Pointless Babble” (Pear Analytics, 2009)

Research Questions

• Can we use social media to study food consumption and obesity at the macro level?

• Can we identify markers that are associated with healthy lifestyles of individuals?

• Does the social network play a role in obesity?

Data Acquisition • Start with a streaming API filter for terms such as

“eat”, “cook”, “lunch”, “breakfast”, …

• Collect 50M tweets during Nov 2013

• 892K geo-tagged tweets from 400K users

– Use (lat, long) to map to ZIP and census data

• Get data for 210K random subset

• 3,200 public tweets, profile, friends, followers

• 503M tweets, 32M distinct friends

Food Dictionary & Calories Mapping • Start with “small” initial word list, eat, lunch, dinner, …

• Look at co-occurring terms to extend list

• Use CrowdFlower to label tweets

• Get 811 positive, 1,636 negative tweets

• Build a NB classifier, get common terms – 460 labeled as “food” (available online)

• Look up food mentioned and map to calories – Pizza 478, fruit salad 99, … *link]

• Compute per-person average value

Calories vs. Obesity

Zooming-In to Counties

• Try to predict county-level obesity

– avCal

– Food names

– LIWC categories (re Culotta’14)

– Demographic

• Ridge regression with 5-fold cross validation

Prediction Performance

Markers in Profiles/Tweets

Good and Bad Interests

[link]

Social Network Effects

• Call a user in predicted top 10% “active”

Conclusions

• Macro-level food consumption and obesity trends are traceable through social media

• Sedentary interests largely bad

• Difference between spectator and participatory sports

• Social assortativity of obesity

#FoodPorn: Obesity Patterns in Culinary Interactions

Joint work with Y. Mejova, H. Haddadi and A. Noulas

Under review

Food on Instagram

Research Questions

• Is there a link between (the presence of) fast food restaurants and obesity?

• Can we understand perceptions of food, e.g. what is healthy?

• What is the effect of “social approval” on posting (un-)healthy photos?

Data Collection

• 194k food places on FourSquare

• 164k found on Instagram

• Queried from Sep’04 to Nov’04

– 21M photos, 3.3M users, 316 US counties

• Obesity and other variables

– County Health Rankings (CHR)

Additional Data Labeling

• Fast food or not

– Combining 4sq categories with names of chains

• Labeled #Hashtags

– Healthy, unhealthy, social, emotion

Fast Food Density vs. Obesity

r = .267 r = .424

Food, Emotions and Perceptions

“Sensual” Food

[link]

Obesity and Hashtags

Obesity and Image Popularity

Food Category and Popularity

Conclusions

• Presence of fast food linked with obesity

• Though #healthy photos attract more likes than #unhealthy ones, the opposite holds for categories

– “pat on the back” for healthy

• Some (more) evidence for the social dimension of food and obesity

Determinants of Persistent Social Sharing of Fitness Apps in Twitter

Joint work with K. Park, M. Cha and C. Lee

Under review

MyFitnessPal and Twitter

[link]

Research Questions

• What determines long-term fitness app usage?

• How important are social network features?

Data Collection

• Used a combination of Decahose and Twitter Streaming API to identify 7.5k Twitter users for #myfitnesspal

– 6 months later crawled their data

• Then subset further to English, <10k followers and long-term temporal coverage

– 3.2k users, 4.8M tweets

#MyFitnessPal Tweets Examples

“Persistent” vs. “Dormant”

• 2.6k persistent

• 0.5k dormant

Gradual “Churning”

Indicators of Persistence

Good vs. Bad Friends

When a Fitness-Network Helps

Conclusions

• Fitness-network helps, but general feedback potentially hurts

• Early adoption linked to persistence

• Celebrity/TV interests are (again) a bad sign

• Fitness networks give biggest persistence boost to less-exercising users

So What About Qatar?

• Currently collecting Instagram data for Qatar

– Twitter turned out to be too limited

• The “social” dimension will be crucial

– Food is consumed communally

– Refusing food can be offensive

• Working with medical doctors

• Developing culturally-aware interventions

Come Talk to Me

• Health, Food, Mobile Sensing

– Work presented today

• Multi-Cultural Societies

– Links across languages, nationalities, ethnicities

• Polarization and Online Conflicts

– ISIS, Egypt, IL-PA

• Mobility and Smart Cities