Skip to main content

Linking Obesity and Tweets

  • Conference paper
  • First Online:
Smart Health (ICSH 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9545))

Included in the following conference series:

Abstract

Obesity has been a public health problem in the United States. The online social media platforms such as Twitter, Facebook, Google+ give users quick and easy way to engage in conversation about issues, problems, and concerns of their daily lives. In this exploratory research, our goal is to determine if the obesity conversation among Twitter users from fattest places is different than that among people from thinnest places. Our hypothesis is that the users in thinnest places would engage more, both in quantity and quality, in Twitter conversation about preventing obesity and promoting health than that of the users in fattest places. We conducted a comparative study of obesity conversations on Twitter by location of top ten fattest and thinnest cities as well as top ten fattest and thinnest states in the United States. Our results show that users in fattest cities and states participate significantly less in conversation covering the topics on and around obesity than that of thinnest cities and states.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.usatoday.com/story/money/business/2014/04/06/americas-thinnestcity/7306199/.

  2. 2.

    http://stateofobesity.org/adult-obesity/.

  3. 3.

    http://www.pewinternet.org/2014/01/08/social-media-update-2013/twitter-users/.

  4. 4.

    www.hashtagify.me.

References

  1. Myslín, M., Zhu, S.H., Chapman, W., Conway, M.: Using Twitter to examine smoking behavior and perceptions of emerging tobacco products. J. Med. Internet Res. 15(8), e174 (2013)

    Article  Google Scholar 

  2. Paul, M.J., Dredze, M.: You are what you tweet: analyzing Twitter for public health. Proc. ICWSM 2011, 265–272 (2011)

    Google Scholar 

  3. Schwartz, H.A., Eichstaedt, J.C., Kern, M.L., Dziurzynski, L., Lucas, R.E., Agrawal, M., Park, G.J., et al.: Characterizing geographic variation in well-being using tweets. In: Proceedings of the ICWSM 2013 (2013)

    Google Scholar 

  4. De Choudhury, M., Counts, S., Horvitz, E.: Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267–3276. ACM (2013)

    Google Scholar 

  5. Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: tracking flu infections on Twitter. In: HLT-NAACL, pp. 789–795 (2013)

    Google Scholar 

  6. Sadilek, A., Kautz, H.A., Silenzio, V.: Predicting disease transmission from geo-tagged micro-blog data. In: AAAI (2012)

    Google Scholar 

  7. Gayo-Avello, D.: A meta-analysis of state-of-the-art electoral prediction from Twitter data. Soc. Sci. Comput. Rev. 0894439313493979 (2013)

    Google Scholar 

  8. Mitchell, L., Frank, M.R., Harris, K.D., Dodds, P.S., Danforth, C.M.: The geography of happiness: connecting Twitter sentiment and expression, demographics, and objective characteristics of place. PLoS ONE 8(5), e64417 (2013)

    Article  Google Scholar 

  9. Lee, K., Palsetia, D., Narayanan, R., Patwary, M.M.A., Agrawal, A., Choudhary, A.: Twitter trending topic classification. In: 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW), pp. 251–258 (2011)

    Google Scholar 

  10. Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with Twitter: what 140 characters reveal about political sentiment. Proc. ICWSM 10, 178–185 (2010)

    Google Scholar 

  11. Yoon, S., Elhadad, N., Bakken, S.: A practical approach for content mining of tweets. Am. J. Prev. Med. 45(1), 122–129 (2013)

    Article  Google Scholar 

  12. Wakade, S., Shekar, C., Liszka, K.J., Chan, C.C.: Text mining for sentiment analysis of Twitter data. In: International Conference on Information and Knowledge Engineering, pp. 109–114 (2012)

    Google Scholar 

  13. Dredze, M., Cheng, R., Paul, M.J., Broniatowski, D.A.: HealthTweets.org: a platform for public health surveillance using Twitter. In: AAAI Conference on Artificial Intelligence (2014)

    Google Scholar 

  14. De Silva, L., Riloff, E.: User type classification of tweets with implications for event recognition. In: Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media, vol. 98. ACL 2014 (2014)

    Google Scholar 

  15. Schmidt, C.W.: Trending now: using social media to predict and track disease outbreaks. Environ. Health Perspect. 120(1), 30–33 (2012)

    Article  Google Scholar 

  16. Chunara, R., Bouton, L., Ayers, J.W., Brownstein, J.S.: Assessing the online social environment for surveillance of obesity prevalence. PLoS ONE 8(4), e61373 (2013)

    Article  Google Scholar 

  17. Ashrafian, H., Toma, T., Harling, L., Kerr, K., Athanasiou, T., Darzi, A.: Social networking strategies that Aim to reduce obesity have achieved significant although modest results. Health Aff. 33(9), 1641–1647 (2014)

    Article  Google Scholar 

  18. Natural Language Toolkit. http://www.nltk.org/

  19. Twitter API. https://dev.twitter.com/overview/documentation

  20. Weka 3: Data Mining Software in Java. http://www.cs.waikato.ac.nz/ml/weka/

  21. Twitter4J: A Java library for the Twitter API. http://twitter4j.org/en/

  22. Adult Obesity Facts. CDC. http://www.cdc.gov/obesity/data/adult.html

  23. Statistica Inc. Facebook: monthly active users 2015. In Statista - The Statistics Portal for Market Data, Market Research and Market Studies, Retrieved July 21, 2015. http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

  24. Statistic Brain. (n.d.). Twitter Statistics. Retrieved July 21, 2015. http://www.statisticbrain.com/twitter-statistics/

  25. Christakis, N.A., Fowler, J.H.: The spread of obesity in a large social network over 32 years. N. Engl. J. Med. 357(4), 370–379 (2007)

    Article  Google Scholar 

  26. Obesity Rates and Rankings Methodology. http://stateofobesity.org/methodology/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mohd Anwar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Anwar, M., Yuan, Z. (2016). Linking Obesity and Tweets. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-29175-8_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-29174-1

  • Online ISBN: 978-3-319-29175-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics