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International Conference on Social Informatics

SocInfo 2020: Social Informatics pp 192–205Cite as

Impact of Online Health Awareness Campaign: Case of National Eating Disorders Association

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12467)

Abstract

National Eating Disorders Association conducts a NEDAwareness week every year, during which it publishes content on social media and news aimed to raise awareness of eating disorders. Measuring the impact of these actions is vital for maximizing the effectiveness of such interventions. This paper is an effort to model the change in behavior of users who engage with NEDAwareness content. We find that, despite popular influencers being involved in the campaign, it is governmental and nonprofit accounts that attract the most retweets. Furthermore, examining the tweeting language of users engaged with this content, we find linguistic categories concerning women, family, and anxiety to be mentioned more within the 15 days after the intervention, and categories concerning affiliation, references to others, and positive emotion mentioned less. We conclude with actionable implications for future campaigns and discussion of the method’s limitations.

Keywords

  • Health informatics
  • Health interventions
  • Twitter
  • Social media
  • Mental health
  • Eating disorders

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  • DOI: 10.1007/978-3-030-60975-7_15
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Notes

  1. 1.

    https://www.nationaleatingdisorders.org/blog/announcing-national-eating-disorders-awareness-week-2020.

  2. 2.

    https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/names/babies-first-names/full-lists-of-babies-first-names-2010-to-2014.

  3. 3.

    https://www.ssa.gov/oact/babynames/limits.html.

  4. 4.

    https://google.github.io/CausalImpact/CausalImpact.html.

  5. 5.

    https://en.wikipedia.org/wiki/International_Women’s_Day.

References

  1. Attai, D.J., Cowher, M.S., Al-Hamadani, M., Schoger, J.M., Staley, A.C., Landercasper, J.: Twitter social media is an effective tool for breast cancer patient education and support: patient-reported outcomes by survey. J. Med. Internet Res. 17(7), e188 (2015)

    CrossRef  Google Scholar 

  2. Brodersen, K.H., Gallusser, F., Koehler, J., Remy, N., Scott, S.L.: Inferring causal impact using Bayesian structural time-series models. Ann. Appl. Stat. 9, 247–274 (2014). http://research.google.com/pubs/pub41854.html

  3. Chancellor, S., Lin, Z.J., Goodman, E.L., Zerwas, S., De Choudhury, M.: Quantifying and predicting mental illness severity in online pro-eating disorder communities. In: Conference on Computer-Supported Cooperative Work and Social Computing (CSCW) (2016)

    Google Scholar 

  4. Chou, W., Oh, A., WP, K.: Addressing health-related misinformation on social media. JAMA 320, 2417–2418 (2018). http://dx.doi.org/10.1001/jama.2018.16865

  5. Ciocarlan, A., Masthoff, J., Oren, N.: Qualitative study into adapting persuasive games for mental wellbeing to personality, stressors and attitudes. In: Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization. UMAP 2017, pp. 402–407. Association for Computing Machinery, New York (2017). https://doi.org/10.1145/3099023.3099111

  6. Ciocarlan, A., Masthoff, J., Oren, N.: Kindness is contagious: study into exploring engagement and adapting persuasive games for wellbeing. In: Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. UMAP 2018, pp. 311–319. Association for Computing Machinery, New York (2018). https://doi.org/10.1145/3209219.3209233

  7. Csardi, G., Nepusz, T.: The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006). http://igraph.org

  8. De Choudhury, M.: Anorexia on tumblr: a characterization study. In: Proceedings of the 5th International Conference on Digital Health 2015, pp. 43–50. ACM (2015)

    Google Scholar 

  9. De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. ICWSM 13, 1–10 (2013)

    Google Scholar 

  10. De Choudhury, M., Kiciman, E., Dredze, M., Coppersmith, G., Kumar, M.: Discovering shifts to suicidal ideation from mental health content in social media. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 2098–2110. ACM (2016)

    Google Scholar 

  11. Guntuku, S.C., Preotiuc-Pietro, D., Eichstaedt, J.C., Ungar, L.H.: What Twitter profile and posted images reveal about depression and anxiety. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 236–246 (2019)

    Google Scholar 

  12. Himelboim, I., Han, J.Y.: Cancer talk on Twitter: community structure and information sources in breast and prostate cancer social networks. J. Health Commun. (2014). https://doi.org/10.1080/10810730.2013.811321

    CrossRef  Google Scholar 

  13. Holland, G., Tiggemann, M.: “Strong beats skinny every time”: disordered eating and compulsive exercise in women who post fitspiration on Instagram. Int. J. Eat. Disord. (2017). https://doi.org/10.1002/eat.22559

    CrossRef  Google Scholar 

  14. Kiciman, E., Counts, S., Gasser, M.: Using longitudinal social media analysis to understand the effects of early college alcohol use. In: Twelfth International AAAI Conference on Web and Social Media (2018)

    Google Scholar 

  15. Kıcıman, E., Thelin, J.: Answering what if, should i and other expectation exploration queries using causal inference over longitudinal data. In: Conference on Design of Experimental Search and Information Retrieval Systems (DESIRES) (2018)

    Google Scholar 

  16. Kulshrestha, J., Zafar, M.B., Noboa, L.E., Gummadi, K.P., Ghosh, S.: Characterizing information diets of social media users. In: Ninth International AAAI Conference on Web and Social Media (2015)

    Google Scholar 

  17. Levine, A.S.: Eating disorders and obesity-a comprehensive handbook. Am. J. Clin. Nutr. (2003). https://doi.org/10.1093/ajcn/77.5.1343

    CrossRef  Google Scholar 

  18. Mackenzie, C.S., Gekoski, W., Knox, V.: Age, gender, and the underutilization of mental health services: the influence of help-seeking attitudes. Aging Ment. Health 10(6), 574–582 (2006)

    CrossRef  Google Scholar 

  19. Magno, G., Weber, I.: International gender differences and gaps in online social networks. In: Aiello, L.M., McFarland, D. (eds.) SocInfo 2014. LNCS, vol. 8851, pp. 121–138. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-13734-6_9

    CrossRef  Google Scholar 

  20. McCloud, R.F., Kohler, R.E., Viswanath, K.: Cancer risk-promoting information: the communication environment of young adults. Am. J. Prev. Med. 53(3), S63–S72 (2017)

    CrossRef  Google Scholar 

  21. McNeill, A.R., Briggs, P.: Understanding Twitter influence in the health domain: a social-psychological contribution. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 673–678 (2014)

    Google Scholar 

  22. Naslund, J., Aschbrenner, K., Marsch, L., Bartels, S.: The future of mental health care: peer-to-peer support and social media. Epidemiol. Psychiatr. Sci. 25(2), 113–122 (2016)

    CrossRef  Google Scholar 

  23. Olteanu, A., Varol, O., K\(\backslash \)ic\(\backslash \)iman, E.: Distilling the outcomes of personal experiences: a propensity-scored analysis of social media. In: Proceedings of The 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (2017)

    Google Scholar 

  24. Pedersen, T.L.: ggraph: an implementation of grammar of graphics for graphs and networks (2019). https://CRAN.R-project.org/package=ggraph, r package version 2.0.0

  25. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: Liwc 2001Lawrence Erlbaum Associates, Mahway, vol. 71, no. 2001, p. 2001 (2001)

    Google Scholar 

  26. Radloff, L.S.: The CES-D scale: a self-report depression scale for research in the general population. Appl. Psychol. Meas. 1(3), 385–401 (1977)

    CrossRef  Google Scholar 

  27. Saha, K., Sugar, B., Torous, J., Abrahao, B., Kıcıman, E., De Choudhury, M.: A social media study on the effects of psychiatric medication use. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 13, pp. 440–451 (2019)

    Google Scholar 

  28. Storch, E.A., Milsom, V.A., DeBraganza, N., Lewin, A.B., Geffken, G.R., Silverstein, J.H.: Peer victimization, psychosocial adjustment, and physical activity in overweight and at-risk-for-overweight youth. J. Pediatr. Psychol. 32(1), 80–89 (2007)

    CrossRef  Google Scholar 

  29. Striegel-Moore, R.H., et al.: Gender difference in the prevalence of eating disorder symptoms. Int. J. Eat. Disord. 42(5), 471–474 (2009)

    CrossRef  Google Scholar 

  30. Torkamaan, H., Ziegler, J.: Rating-based preference elicitation for recommendation of stress intervention. In: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization. UMAP 2019, pp. 46–50. Association for Computing Machinery, New York (2019). https://doi.org/10.1145/3320435.3324990

  31. Vraga, V., et al.: Cancer and social media: a comparison of traffic about breast cancer, prostate cancer, and other reproductive cancers on Twitter and Instagram. J. Health Commun. (2018). https://doi.org/10.1080/10810730.2017.1421730

    CrossRef  Google Scholar 

  32. West, R., White, R.W., Horvitz, E.: From cookies to cooks: insights on dietary patterns via analysis of web usage logs. In: Proceedings of the 22nd international conference on World Wide Web, pp. 1399–1410 (2013)

    Google Scholar 

  33. Yom-Tov, E., Fernandez-Luque, L., Weber, I., Crain, S.P.: Pro-anorexia and pro-recovery photo sharing: a tale of two warring tribes. J. Med. Internet Res. 14(6), e151 (2012)

    CrossRef  Google Scholar 

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Author information

Authors and Affiliations

  1. ISI Foundation, Turin, Italy

    Yelena Mejova

  2. University of Cadiz, Cadiz, Spain

    Víctor Suarez-Lledó

Authors
  1. Yelena Mejova
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  2. Víctor Suarez-Lledó
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Corresponding author

Correspondence to Yelena Mejova .

Editor information

Editors and Affiliations

  1. Max Planck Institute for Demographic Research, Rostock, Germany

    Samin Aref

  2. University of Sheffield, Sheffield, UK

    Prof. Kalina Bontcheva

  3. King’s College London, London, UK

    Marco Braghieri

  4. Umeå University, Umeå, Sweden

    Prof. Dr. Frank Dignum

  5. ISTI-CNR, Pisa, Italy

    Prof. Fosca Giannotti

  6. University of Pisa, Pisa, Italy

    Francesco Grisolia

  7. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Mejova, Y., Suarez-Lledó, V. (2020). Impact of Online Health Awareness Campaign: Case of National Eating Disorders Association. In: , et al. Social Informatics. SocInfo 2020. Lecture Notes in Computer Science(), vol 12467. Springer, Cham. https://doi.org/10.1007/978-3-030-60975-7_15

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  • DOI: https://doi.org/10.1007/978-3-030-60975-7_15

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