2nd presentation

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2. You Tweet What You Eat:
Studying Food Consumption Through Twitter
Sofiane Abbar, Yelena Mejova, Ingmar Weber
Qatar Computing Research Institute
사용자 경험 연구실 유혜수
Understanding Health Through Online Behavior
서울대학교 융합과학기술대학원
Abbar, Sofiane, Yelena Mejova, and Ingmar Weber. "You tweet what you eat: Studying food consumption through twitter." arXiv preprint arXiv:1412.4361 (2014).
2015- 2 Lab Meeting

3. Authors Keywords: ACM Classification Keywords:
Dietary Health; Obesity;
Food; Twitter; Social
Networks
Content Analysis and Indexing; Online
Information Services; Life and Medical
Sciences; Health

4. -Sentiment Analysis
-Social Media
-“You Are What You Eat”
* Questioned myself: Am I destined to read?
-Understanding Health Through Online Behavior
Why This Paper?

5. Fun Fact

6. Most tweeted food in US

7. on top of which is PIZZA!

8. Average caloric values

9. Sofiane Abbar, Yelena Mejova, Ingmar Weber
-Qatar Computing Research Institute
“We examine the potential of Twitter to provide insight into US-wide dietary choices by linking the tweeted dining experiences of 210K users to their
interests, demographics, and social networks. We validate our approach by relating the caloric values of the foods mentioned in the tweets to the
state-wide obesity rates, and link this data to societal and economic factors, such as education and income. Finally, we address the somewhat
.controversial issue of the social nature of obesity by inducing two social networks using mentions and reciprocal following relationships”
Authors

10. Background
Method
Data
Build a model to predict county-wide obesity and diabetes
statistics based on a combination of demographic variables and
food names mentioned on Twitter
Link the data to societal and economic factors
(education and income)
Address somewhat controversial issue of the social
nature of obesity by inducing two social networks
Summarization
• Social media is notorious for providing its users with a means of documenting the minutiae of their daily lives, including their dietary choices
201K users in the US Tracked 50 M tweets
This data allowed them to consider
- nutritional value of the foods mentioned in these tweets, demographic characteristics of the users who tweet them, their interests, and
the social network induced by their interaction
Food
- Integral part of lives, cultures and well being
- Major interest to public health
Collection of daily nutritional data involves detailed and periodic surveys
-> limited in scope & reach
• Twitter: a potential to provide insight into US-wide dietary choices by linking the tweeted dining experiences
Relate the caloric values of the foods mentioned in the tweets to the state wide obesity
Significant Improvement on HCI

11. DATA
A large scale analysis of 201K Twitter Users in the US
Consider the nutritional value of the foods mentioned in the tweets
- demographic characteristics of the users who tweet them, their
interests, and the social network induced by their interaction
50M tweets were collected through the Twitter Streaming API
Duration: 2013 Oct 19 - 2013 Nov 29
Selected all geo-tagged & american tweets
= 892 K tweets posted by 400K users
Improve the detection of food- related tweets
- Examine 1000 most frequent terms
- Handselected terms unambiguously related to food
- Crowdsourcing (Crowdsourcing platform)
- Published a total 2157 tweet examples

12. You Might Wonder…
• Is data sensitive enough to pick out more personal dietary variations?
• 2010 US Census - demographic information
• Extrapolate users’ gender using their screen names, characterise users’ zip code as
being either urban and rural
• Does social network influence personal dietary habits of users?
• Traditional Surveys
• Social networks = Network structure

13. Urban and Rural
Chracterizing Users
- Income, Education and Gender
DATA

14. Interesting point of view: it clearly shows us how much information can be inferred from our data
available online (even if it isn’t very exact). For example, in order to assess the sensitivity of data
related to more specific personal dietary variations, the scientists used the demographic information
from the 2010 US Census. Based on this data, they extrapolated the gender information of Twitter
users using their screen names only (!). User-provided ZIP codes were used to classify particular
persons as being either urban or rural.
Distinguishing foods between uses in rural and urban areas
DATA

15. Discussion & Future Work
Using data from social media to study sociological phenomenon and to
“nowcast” variables has both advantage and disadvantages
Big Data Analysis: High precision
Advantage - time and timeliness
- Hundreds of thousands of users can be obtained at relatively low costs
- Analyzed in a matter of days, or even hours rather than weeks/months
for comparable surveys
Qualitative
- Availability of network information
- “homophilly” : social mechanisms

16. Conclusion
A large scale study of Twitter sphere
Monitored US-wide nutritional behavior
Daily food #tweets = predictive of the national obesity & diabetes
Calories tweets are linked to user interest & demographic indicator
Conclusion
More needs to be done:
-develop sensitive and accurate tools for user characterization
-Twittter = documentation of users’ interests, opinions, and behaviours
-this studies show another example of the potential Twitter has for public health research
Targeting Twitter users with a particular behaviour could be a promising step for interventions such
as public health awareness campaigns
Future Works

17. Take Away Messages
You Might Wonder…
• Is data sensitive enough to pick out more personal dietary variations?
• 2010 US Census - demographic information
• Extrapolate users’ gender using their screen names, characterise users’ zip code as being
either urban and rural
• Does social network influence personal dietary habits of users?
• Traditional Surveys
• social networks = network structure