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Citing this Article

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Published on 24.10.16 in Vol 2, No 2 (2016): Jul-Dec

This paper is in the following e-collection/theme issue:

Works citing "“When ‘Bad’ is ‘Good’”: Identifying Personal Communication and Sentiment in Drug-Related Tweets"

Raminta Daniulaityte, Lu Chen, Francois R Lamy, Robert G Carlson, Krishnaprasad Thirunarayan, Amit Sheth

JMIR Public Health Surveill 2016 (Oct 24); 2(2):e162 

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.6327):

(note that this is only a small subset of citations)

  1. Crocamo C, Viviani M, Bartoli F, Carrà G, Pasi G. Detecting Binge Drinking and Alcohol-Related Risky Behaviours from Twitter’s Users: An Exploratory Content- and Topology-Based Analysis. International Journal of Environmental Research and Public Health 2020;17(5):1510
    CrossRef
  2. Guiñazú MF, Cortés V, Ibáñez CF, Velásquez JD. Employing online social networks in precision-medicine approach using information fusion predictive model to improve substance use surveillance: A lesson from Twitter and marijuana consumption. Information Fusion 2020;55:150
    CrossRef
  3. Adams N, Artigiani EE, Wish ED. Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List. Journal of Drug Issues 2019;49(3):477
    CrossRef
  4. Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal RN, Wang F. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach. Journal of Medical Internet Research 2020;22(11):e15293
    CrossRef
  5. Ashford RD, Curtis BL. Commentary on Cohn and Colleagues: Discussions of Alcohol Use in an Online Social Network for Smoking Cessation: Analysis of Topics, Sentiment, and Social Network Centrality (ACER, 2019). Alcoholism: Clinical and Experimental Research 2019;43(3):401
    CrossRef
  6. Gohil S, Vuik S, Darzi A. Sentiment Analysis of Health Care Tweets: Review of the Methods Used. JMIR Public Health and Surveillance 2018;4(2):e43
    CrossRef
  7. Metwally O, Blumberg S, Ladabaum U, Sinha SR. Using Social Media to Characterize Public Sentiment Toward Medical Interventions Commonly Used for Cancer Screening: An Observational Study. Journal of Medical Internet Research 2017;19(6):e200
    CrossRef
  8. Young SD, Padwa H, Bonar EE. Social Big Data as a Tool for Understanding and Predicting the Impact of Cannabis Legalization. Frontiers in Public Health 2019;7
    CrossRef
  9. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
    CrossRef
  10. Mamidi R, Miller M, Banerjee T, Romine W, Sheth A. Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic. JMIR Public Health and Surveillance 2019;5(2):e11036
    CrossRef
  11. Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
    CrossRef
  12. Taylor J, Pagliari C. Mining social media data: How are research sponsors and researchers addressing the ethical challenges?. Research Ethics 2018;14(2):1
    CrossRef
  13. O'Connor K, Sarker A, Perrone J, Gonzalez Hernandez G. Promoting Reproducible Research for Characterizing Nonmedical Use of Medications Through Data Annotation: Description of a Twitter Corpus and Guidelines. Journal of Medical Internet Research 2020;22(2):e15861
    CrossRef
  14. van Draanen J, Tao H, Gupta S, Liu S. Geographic Differences in Cannabis Conversations on Twitter: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(4):e18540
    CrossRef
  15. Lamy FR, Daniulaityte R, Zatreh M, Nahhas RW, Sheth A, Martins SS, Boyer EW, Carlson RG. "You got to love rosin: Solventless dabs, pure, clean, natural medicine." Exploring Twitter data on emerging trends in Rosin Tech marijuana concentrates. Drug and Alcohol Dependence 2018;183:248
    CrossRef
  16. Kim SJ, Marsch LA, Hancock JT, Das AK. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353
    CrossRef
  17. Daniulaityte R, Lamy FR, Barratt M, Nahhas RW, Martins SS, Boyer EW, Sheth A, Carlson RG. Characterizing marijuana concentrate users: A web-based survey. Drug and Alcohol Dependence 2017;178:399
    CrossRef
  18. He L, Yin T, Hu Z, Chen Y, Hanauer DA, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125
    CrossRef
  19. Singh T, Roberts K, Cohen T, Cobb N, Wang J, Fujimoto K, Myneni S. Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review. JMIR Public Health and Surveillance 2020;6(4):e21660
    CrossRef
  20. Pavan Kumar C, Dhinesh Babu L. Fuzzy based feature engineering architecture for sentiment analysis of medical discussion over online social networks. Journal of Intelligent & Fuzzy Systems 2021;40(6):11749
    CrossRef
  21. Oyebode O, Lomotey R, Orji R. “I Tried to Breastfeed but…”: Exploring Factors Influencing Breastfeeding Behaviours Based on Tweets Using Machine Learning and Thematic Analysis. IEEE Access 2021;9:61074
    CrossRef
  22. Tsai MH, Wang Y. Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19. International Journal of Environmental Research and Public Health 2021;18(12):6272
    CrossRef
  23. Obiedat R, Al-Qaisi L, Qaddoura R, Harfoushi O, Al-Zoubi AM. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021;13(12):2287
    CrossRef
  24. Najafizada M, Rahman A, Donnan J, Dong Z, Bishop L. Analyzing sentiments and themes on cannabis in Canada using 2018 to 2020 Twitter data. Journal of Cannabis Research 2022;4(1)
    CrossRef
  25. Scaboro S, Portelli B, Chersoni E, Santus E, Serra G. Increasing adverse drug events extraction robustness on social media: Case study on negation and speculation. Experimental Biology and Medicine 2022;247(22):2003
    CrossRef
  26. Black J, Margolin ZR, Bau G, Olson R, Iwanicki JL, Dart RC. Web-Based Discussion and Illicit Street Sales of Tapentadol and Oxycodone in Australia: Epidemiological Surveillance Study. JMIR Public Health and Surveillance 2021;7(12):e29187
    CrossRef
  27. Lokala U, Lamy F, Daniulaityte R, Gaur M, Gyrard A, Thirunarayan K, Kursuncu U, Sheth A. Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study. JMIR Public Health and Surveillance 2022;8(12):e24938
    CrossRef
  28. Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850
    CrossRef
  29. Rahim AIA, Ibrahim MI, Chua S, Musa KI. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare 2021;9(12):1679
    CrossRef
  30. Boukobza A, Burgun A, Roudier B, Tsopra R. Deep Neural Networks for Simultaneously Capturing Public Topics and Sentiments During a Pandemic: Application on a COVID-19 Tweet Data Set. JMIR Medical Informatics 2022;10(5):e34306
    CrossRef
  31. A. Rahim AI, Ibrahim MI, Musa KI, Chua S, Yaacob NM. Assessing Patient-Perceived Hospital Service Quality and Sentiment in Malaysian Public Hospitals Using Machine Learning and Facebook Reviews. International Journal of Environmental Research and Public Health 2021;18(18):9912
    CrossRef
  32. Khademi Habibabadi S, Hallinan C, Bonomo Y, Conway M. Consumer-Generated Discourse on Cannabis as a Medicine: Scoping Review of Techniques. Journal of Medical Internet Research 2022;24(11):e35974
    CrossRef
  33. Tang JE, Arvind V, Dominy C, White CA, Cho SK, Kim JS. How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments. Global Spine Journal 2023;13(8):2107
    CrossRef
  34. He L, Yin T, Zheng K. They May Not Work! An evaluation of eleven sentiment analysis tools on seven social media datasets. Journal of Biomedical Informatics 2022;132:104142
    CrossRef
  35. Rahim AIA, Ibrahim MI, Musa KI, Chua S, Yaacob NM. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021;9(10):1369
    CrossRef
  36. Walker AL, LoParco C, Rossheim ME, Livingston MD. #Delta8: a retailer-driven increase in Delta-8 THC discussions on Twitter from 2020 to 2021. The American Journal of Drug and Alcohol Abuse 2023;49(4):491
    CrossRef
  37. . Reply to critique of the paper, ‘investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study’. International Journal of Disaster Risk Reduction 2024;100:104105
    CrossRef
  38. SV P, Gajjar P. Critique of the paper, ‘Investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study’. International Journal of Disaster Risk Reduction 2024;100:104104
    CrossRef
  39. Lossio-Ventura JA, Weger R, Lee AY, Guinee EP, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150
    CrossRef
  40. Luo J, Yoo JE, Park JE. ‘ From fail to prevail ’ : How a salesperson’s communication sentiment influences consumer forgiveness in service failures focusing on the role of consumer self-construal. Journal of Global Scholars of Marketing Science 2024;34(2):231
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.6327):

  1. Natsiavas P, Maglaveras N, Koutkias V. Knowledge Representation for Health Care. 2017. Chapter 4:51
    CrossRef
  2. Kursuncu U, Gaur M, Lokala U, Thirunarayan K, Sheth A, Arpinar IB. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. 2019. Chapter 4:67
    CrossRef
  3. Zhang H, Wheldon C, Tao C, Dunn AG, Guo Y, Huo J, Bian J. Social Web and Health Research. 2019. Chapter 11:207
    CrossRef
  4. Leightley D, Sharp M, Williamson V, Fear NT, Gribble R. Social Media and the Armed Forces. 2020. Chapter 9:145
    CrossRef
  5. Portelli B, Passabì D, Lenzi E, Serra G, Santus E, Chersoni E. AI for Disease Surveillance and Pandemic Intelligence. 2022. Chapter 8:87
    CrossRef
  6. Leightley D, Sharp M, Williamson V, Fear NT, Gribble R. Soziale Medien und die Streitkräfte. 2023. Chapter 9:183
    CrossRef