The painful tweet: Text, sentiment, and community structure analyses of tweets pertaining to pain

Journal of Medical Internet Research

Volumn 17, Issue 4, 2015, Pages e84-

  Tighe, Patrick J ^a   Goldsmith, Ryan C ^a   Gravenstein, Michael ^a   Bernard, H Russell ^b   Fillingim, Roger B ^b  

^a UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

^b UNIVERSITY OF FLORIDA   (United States)

Author keywords

Emotions; Social networks; Text mining; Twitter messaging

Indexed keywords

HUMAN; INTERNET; PAIN; PHILIPPINES; SOCIAL MEDIA; SOCIAL NETWORK;

HUMANS; INTERNET; PAIN; PHILIPPINES; SOCIAL MEDIA; SOCIAL NETWORKING;

EID: 84929259083     PISSN: None     EISSN: 14388871     Source Type: Journal    
DOI: 10.2196/jmir.3769     Document Type: Article

References (77)

1. https://business.twitter.com/en/basics/what-is-twitter/. 2014. What is Twitter? URL: https://business.twitter.com/twitter-101 [accessed 2014-05-22] [WebCite Cache ID 6PlSlpqgg]
2. Barber MJ. Modularity and community detection in bipartite networks. Phys Rev E Stat Nonlin Soft Matter Phys 2007 Dec;76(6 Pt 2):066102. [Medline: 18233893]
3. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech 2008 Oct 09;2008(10):P10008. [doi: 10.1088/1742-5468/2008/10/P10008]
4. Chew C, Eysenbach G. Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak. PLoS One 2010;5(11):e14118 [FREE Full text] [doi: 10.1371/journal.pone.0014118] [Medline: 21124761]
5. Lancichinetti A, Fortunato S. Community detection algorithms: a comparative analysis. Phys Rev E Stat Nonlin Soft Matter Phys 2009 Nov;80(5 Pt 2):056117. [Medline: 20365053]
6. Borgatti SP, Mehra A, Brass DJ, Labianca G. Network analysis in the social sciences. Science 2009 Feb 13;323(5916):892-895. [doi: 10.1126/science.1165821] [Medline: 19213908]
7. Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal Complex Systems 2006:1695.
8. Hawe P, Webster C, Shiell A. A glossary of terms for navigating the field of social network analysis. J Epidemiol Community Health 2004 Dec;58(12):971-975 [FREE Full text] [doi: 10.1136/jech.2003.014530] [Medline: 15547054]
9. Krishnamurthy B, Gill P, Arlitt M. A few chirps about Twitter. In: Proceedings of the first workshop on Online social networks. New York: ACM Press; 2008 Presented at: ACM SIGCOMM 2008; August 17-22, 2008; Seattle, WA p. 19-24. [doi: 10.1145/1397735.1397741]
10. Molloy M, Reed B. The size of the giant component of a random graph with a given degree sequence. Combinator Probab Comp 1998;7(3):295-305. [doi: 10.1017/S0963548398003526]
11. Nigam K, Hurst M. Towards a robust metric of opinion. In: Proceedings of the AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications. 2004 Presented at: AAAI Spring Symposium on Exploring Attitude and Affect in Text: Theories and Applications; 2004; Stanford, CA.
12. Omurtag K, Jimenez PT, Ratts V, Odem R, Cooper AR. The ART of social networking: how SART member clinics are connecting with patients online. Fertil Steril 2012 Jan;97(1):88-94 [FREE Full text] [doi: 10.1016/j.fertnstert.2011.10.001] [Medline: 22088209]
13. Apfelbaum JL, Chen C, Mehta SS, Gan TJ. Postoperative pain experience: results from a national survey suggest postoperative pain continues to be undermanaged. Anesth Analg 2003 Aug;97(2):534-40, table of contents. [Medline: 12873949]
14. Connors AFJ, Dawson NV, Desbiens NA. The SUPPORT Principal Investigators. A controlled trial to improve care for seriously iii hospitalized patients: the study to understand prognoses and preferences for outcomes and risks of treatments (SUPPORT). JAMA 1995;274:1591.
15. Committee on Advancing Pain Research, Care, and Education, Institute of Medicine. Relieving Pain in America: A Blueprint for Transforming Prevention, Care, Education and Research. Washington, DC: The National Academies Press; Jun 29, 2011.
16. Bollen J, Mao H, Zeng X. Twitter mood predicts the stock market. J Computat Sci 2011;2(1):1-8. [doi: 10.1016/j.jocs.2010.12.007]
17. Borondo J, Morales AJ, Losada JC, Benito RM. Characterizing and modeling an electoral campaign in the context of Twitter: 2011 Spanish Presidential election as a case study. Chaos 2012 Jun;22(2):023138. [doi: 10.1063/1.4729139] [Medline: 22757545]
18. Dodds PS, Harris KD, Kloumann IM, Bliss CA, Danforth CM. Temporal patterns of happiness and information in a global social network: hedonometrics and Twitter. PLoS One 2011;6(12):e26752 [FREE Full text] [doi: 10.1371/journal.pone.0026752] [Medline: 22163266]
19. Melville P, Gryc W, Lawrence RD. Sentiment analysis of blogs by combining lexical knowledge with text classification. In: KDD '09 Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. New York: ACM Press; 2009 Presented at: 15th ACM SIGKDD international conference on Knowledge discovery and data mining; June 28-July 1, 2009; Paris, France. [doi: 10.1145/1557019.1557156]
20. Mitchell L, Frank MR, Harris KD, Dodds PS, Danforth CM. The geography of happiness: connecting twitter sentiment and expression, demographics, and objective characteristics of place. PLoS One 2013;8(5):e64417 [FREE Full text] [doi: 10.1371/journal.pone.0064417] [Medline: 23734200]
21. Prabowo R, Thelwall M. Sentiment analysis: a combined approach. J Informetr 2009;3:143-157. [doi: 10.1016/j.joi.2009.01.003]
22. Bollen J, Pepe A, Mao H. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011 Jul Presented at: Fifth International AAAI Conference on Weblogs and Social Media; July 17-21, 2011; Barcelona, Spain.
23. Dufty N. Using social media to build community disaster resilience. Austral J Emerg Mgmt 2012;27(1):40-45.
24. Kumar S, Barbier G, Abbasi M, Liu H. Tweet tracker: an analysis tool for humanitarian and disaster relief. In: Fifth International AAAI Conference. 2011 Jul Presented at: AAAI Conference on Weblogs and Social Media; July 17-21, 2011; Barcelona, Spain p. 1-2.
25. Manyez-Ortiz M, Albo-Portero C. Twitter in emergencies. Follow our roadmap. BMJ 2012;345:e4814. [Medline: 22826588]
26. Prochaska JJ, Pechmann C, Kim R, Leonhardt JM. Twitter=quitter? An analysis of Twitter quit smoking social networks. Tob Control 2012 Jul;21(4):447-449 [FREE Full text] [doi: 10.1136/tc.2010.042507] [Medline: 21730101]
27. Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One 2011;6(5):e19467 [FREE Full text] [doi: 10.1371/journal.pone.0019467] [Medline: 21573238]
28. Sullivan SJ, Schneiders AG, Cheang CW, Kitto E, Lee H, Redhead J, et al. 'What's happening?' A content analysis of concussion-related traffic on Twitter. Br J Sports Med 2012 Mar;46(4):258-263. [doi: 10.1136/bjsm.2010.080341] [Medline: 21406451]
29. Thelwall M, Buckley K, Paltoglou G. Sentiment in Twitter events. J Am Soc Inf Sci 2010 Dec 06;62(2):406-418. [doi: 10.1002/asi.21462]
30. Forgeron PA, McGrath P, Stevens B, Evans J, Dick B, Finley GA, et al. Social information processing in adolescents with chronic pain: my friends don't really understand me. Pain 2011 Dec;152(12):2773-2780. [doi: 10.1016/j.pain.2011.09.001] [Medline: 21963240]
31. Karp JF, Shega JW, Morone NE, Weiner DK. Advances in understanding the mechanisms and management of persistent pain in older adults. Br J Anaesth 2008 Jul;101(1):111-120 [FREE Full text] [doi: 10.1093/bja/aen090] [Medline: 18487247]
32. Lok EYC, Mok CC, Cheng CW, Cheung EFC. Prevalence and determinants of psychiatric disorders in patients with rheumatoid arthritis. Psychosomatics 2010;51(4):338-338.e8. [doi: 10.1176/appi.psy.51.4.338] [Medline: 20587762]
33. Maxwell SK, Barnett C, Kokokyi S, Leung JC, Yu JJ, Bril V, et al. Association of social support with quality of life in patients with polyneuropathy. J Peripher Nerv Syst 2013 Mar;18(1):37-43. [doi: 10.1111/jns5.12005] [Medline: 23521642]
34. Meldrum ML, Tsao JCI, Zeltzer LK. "I can't be what I want to be": children's narratives of chronic pain experiences and treatment outcomes. Pain Med 2009 Sep;10(6):1018-1034 [FREE Full text] [doi: 10.1111/j.1526-4637.2009.00650.x] [Medline: 19594848]
35. Saariaho TH, Saariaho AS, Karila IA, Joukamaa MI. Early maladaptive schemas in Finnish adult chronic pain patients and a control sample. Scand J Psychol 2011 Apr;52(2):146-153. [doi: 10.1111/j.1467-9450.2010.00849.x] [Medline: 21054422]
36. Soklaridis S, Cartmill C, Cassidy D. Biographical disruption of injured workers in chronic pain. Disabil Rehabil 2011;33(23-24):2372-2380. [doi: 10.3109/09638288.2011.573056] [Medline: 21504406]
37. Deter HC. Psychosocial interventions for patients with chronic disease. Biopsychosoc Med 2012;6(1):2 [FREE Full text] [doi: 10.1186/1751-0759-6-2] [Medline: 22293471]
38. Carr JL, Moffett JAK. The impact of social deprivation on chronic back pain outcomes. Chronic Illn 2005 Jun;1(2):121-129. [Medline: 17136918]
39. Moore CC, Romney AK, Hsia TL, Rusch CD. The universality of the semantic structure of emotion terms: methods for the study of inter- and intra-cultural variability. Amer Anthropol 1999;101:529-546. [doi: 10.1525/aa.1999.101.3.529]
40. Romney AK, Moore CC, Rusch CD. Cultural universals: measuring the semantic structure of emotion terms in English and Japanese. Proc Natl Acad Sci U S A 1997 May 13;94(10):5489-5494 [FREE Full text] [Medline: 9144265]
41. Heaivilin N, Gerbert B, Page JE, Gibbs JL. Public health surveillance of dental pain via Twitter. J Dent Res 2011 Sep;90(9):1047-1051 [FREE Full text] [doi: 10.1177/0022034511415273] [Medline: 21768306]
42. Linnman C, Maleki N, Becerra L, Borsook D. Migraine tweets - what can online behavior tell us about disease? Cephalalgia 2013 Jan;33(1):68-69 [FREE Full text] [doi: 10.1177/0333102412465207] [Medline: 23136208]
43. McNeil K, Brna PM, Gordon KE. Epilepsy in the Twitter era: a need to re-tweet the way we think about seizures. Epilepsy Behav 2012 Feb;23(2):127-130. [doi: 10.1016/j.yebeh.2011.10.020] [Medline: 22134096]
44. Nicholson C. Palliative care on Twitter: who to follow to get started. Int J Palliat Nurs 2013 Jan;19(1):6-7. [doi: 10.12968/ijpn.2013.19.1.6] [Medline: 23354427]
45. O'Keeffe GS, Clarke-Pearson K, Council on Communications Media. The impact of social media on children, adolescents, and families. Pediatrics 2011 Apr;127(4):800-804 [FREE Full text] [doi: 10.1542/peds.2011-0054] [Medline: 21444588]
46. Sajadi KP, Goldman HB. Social networks lack useful content for incontinence. Urology 2011 Oct;78(4):764-767. [doi: 10.1016/j.urology.2011.04.074] [Medline: 21862115]
47. Cheng Z, Caverlee J, Lee K. You are where you tweet: a content-based approach to geo-locating Twitter users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. 2010 Oct 26-30 Presented at: 19th ACM International Conference on Information and Knowledge Management; October 26-30, 2010; Toronto, ON.
48. Hecht B, Hong L, Suh B, Chi EH. Tweets from Justin Bieber's heart: the dynamics of the location field in user profiles. New York: ACM; 2011 May 7-12 Presented at: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems; May 07-12, 2011; Vancouver, BC.
49. Leetaru K, Wang S, Padmanabhan A, Shook E. Mapping the global Twitter heartbeat: the geography of Twitter. FM 2013 May 06;18(5):4366. [doi: 10.5210/fm.v18i5.4366]
50. Tighe PJ, Fillingim RB, Hurley RW. Geospatial analysis of Hospital Consumer Assessment of Healthcare Providers and Systems pain management experience scores in U.S. hospitals. Pain 2014;155:1016-1026.
51. Pang B, Lee L. Opinion mining and sentiment analysis. FNT in Information Retrieval 2008;2(1-2):1-135. [doi: 10.1561/1500000011]
52. Nielsen FÅ. A new ANEW: evaluation of a word list for sentiment analysis in microblogs. In: Proceedings of the ESWC. 2011 May 30 Presented at: CEUR Workshop Proceedings; May 30, 2011; Heraklion, Crete.
53. Davidov D, Tsur O, Rappoport A. Enhanced sentiment learning using Twitter hashtags and smileys. In: Proceedings of the 23rd International Conference on Computational Linguistics. 2010 Aug 23-27 Presented at: 23rd International Conference on Computational Linguistics; August 23-27, 2010; Beijing, China p. 241-249.
54. Kouloumpis E, Wilson T, Moore J. Twitter sentiment analysis: the good, the bad, and the OMG!. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011 Jul 17-21 Presented at: AAAI Conference on Weblogs and Social Media; July 17-21, 2011; Barcelona, Spain p. 1-4.
55. Liu KL, Li WJ, Guo M. Emoticon smoothed language models for Twitter sentiment analysis. In: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence. 2012 Jul 22-26 Presented at: Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence; July 22-26, 2012; Toronto, ON.
56. Balahur A, Steinberger R. Rethinking sentiment analysis in the news: from theory to practice and back. In: Proceedings of WOMSA at the 2009 13th Annual Conference of the Spanish Association for Artificial Intelligence. 2013 Nov 13 Presented at: 13th Annual Conference of the Spanish Association for Artificial Intelligence; November 2009; Sevilla, Spain.
57. Bermingham A, Smeaton AF. A study of inter-annotator agreement for opinion retrieval. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2009 Jul Presented at: 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval; July 20-22, 2009; Dublin.
58. Keller MC, Fredrickson BL, Ybarra O, Côté S, Johnson K, Mikels J, et al. A warm heart and a clear head. The contingent effects of weather on mood and cognition. Psychol Sci 2005 Sep;16(9):724-731. [doi: 10.1111/j.1467-9280.2005.01602.x] [Medline: 16137259]
59. Jamison RN, Anderson KO, Slater MA. Weather changes and pain: perceived influence of local climate on pain complaint in chronic pain patients. Pain 1995 May;61(2):309-315. [Medline: 7659442]
60. United States Census 2010. URL: http://www.census.gov/2010census/ [accessed 2015-03-16] [WebCite Cache ID 6X50uni9G]
61. United States Census Bureau. 2014. Small Area Health Insurance Estimates URL: http://www.census.gov/did/www/sahie/data/interactive/index.html [accessed 2014-05-22] [WebCite Cache ID 6PlTKSYD8]
62. National Climactic Data Center, National Oceanic and Atmospheric Administration. 1981-2010 US Climate Normals URL: http://www.ncdc.noaa.gov/data-access/land-based-station-data/land-based-datasets/climate-normals/1981-2010-normals-data [accessed 2015-03-16] [WebCite Cache ID 6X511XkLF]
63. Gentry J. twitteR: R Based Twitter Client. 2014. URL: http://cran.r-project.org/web/packages/twitteR/ [accessed 2014-05-22] [WebCite Cache ID 6PlStE6iN]
64. Borgatti SP, Everett MG. A graph-theoretic perspective on centrality. Social Networks 2006;28:466-484. [doi: 10.1016/j.socnet.2005.11.005]
65. Freeman LC. Centrality in social networks conceptual clarification. Social Networks 1978;1:215-239. [doi: 10.1016/0378-8733(78)90021-7]
66. Brandes U. A faster algorithm for betweenness centrality. J Math Sociol 2011;25:163-177. [doi: 10.1080/0022250X.2001.9990249]
67. Tarjan R. Depth-first search and linear graph algorithms. SIAM J Comput 1972;1:146-160.
68. O'Connor B, Balasubramanyan R, Routledge BR, Smith NA. From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the International AAAI Conference on Weblogs and Social Media. 2010 Presented at: International AAAI Conference on Weblogs and Social Media; May 23-26, 2010; Washington, DC p. 122-129.
69. Paul MJ, Dredze M. You are what you tweet: analyzing Twitter for public health. Artificial Intelligence 2011:265-272.
70. Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ Qual Saf 2013 Mar;22(3):251-255. [doi: 10.1136/bmjqs-2012-001527] [Medline: 23349387]
71. Bardach NS, Asteria-Peñaloza R, Boscardin WJ, Dudley RA. The relationship between commercial website ratings and traditional hospital performance measures in the USA. BMJ Qual Saf 2013 Mar;22(3):194-202 [FREE Full text] [doi: 10.1136/bmjqs-2012-001360] [Medline: 23178860]
72. Dodds PS, Danforth CM. Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J Happiness Stud 2010;11:441-456. [doi: 10.1007/s10902-009-9150-9]
73. Helme RD, Gibson SJ. Pain in older people. In: Crombie IK, editor. Epidemiology of Pain. Seattle, WA: IASP Press; 1999.
74. Saito K, Masuda N. Two types of Twitter users with equally many followers. PLOS ONE 2014;9(1):e84265.
75. de la Torre-Díez I, Díaz-Pernas FJ, Antón-Rodríguez M. A content analysis of chronic diseases social groups on Facebook and Twitter. Telemed J E Health 2012;18(6):404-408. [doi: 10.1089/tmj.2011.0227] [Medline: 22650380]
76. Kumar S, Morstatter F, Zafarani R, Liu H. Whom should I follow? Identifying relevant users during crises. In: HT '13 Proceedings of the 24th ACM Conference on Hypertext and Social Media. 2013 Presented at: 24th ACM Conference on Hypertext and Social Media; May 1-3, 2013; Paris, France p. 139-147.
77. Duggan M, Brenner J. The Demographics of Social Media Users - 2012. Washington, DC: Pew Internet & American Research Project; 2013 Feb 14. URL: http://www.pewinternet.org/2013/02/14/the-demographics-of-social-media-users-2012/ [accessed 2014-05-22] [WebCite Cache ID 6PlThW8dC]