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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041) Infodemiology and Infoveillance (1349) Pharmacovigilance (134)

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

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

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

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

Authors of this article:

Raminta Daniulaityte1, 2 Author Orcid Image ;   Lu Chen2 Author Orcid Image ;   Francois R Lamy1, 2 Author Orcid Image ;   Robert G Carlson1, 2 Author Orcid Image ;   Krishnaprasad Thirunarayan2 Author Orcid Image ;   Amit Sheth2 Author Orcid Image
Article Authors Cited by (50) Tweetations (11) Metrics

Journals

  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 View
  2. Guiñazú M, Cortés V, Ibáñez C, Velásquez J. 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 View
  3. Adams N, Artigiani E, Wish E. Choosing Your Platform for Social Media Drug Research and Improving Your Keyword Filter List. Journal of Drug Issues 2019;49(3):477 View
  4. Yao H, Rashidian S, Dong X, Duanmu H, Rosenthal R, Wang F. Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach. Journal of Medical Internet Research 2020;22(11):e15293 View
  5. Ashford R, Curtis B. 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 View
  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 View
  7. Metwally O, Blumberg S, Ladabaum U, Sinha S. 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 View
  8. Young S, Padwa H, Bonar E. Social Big Data as a Tool for Understanding and Predicting the Impact of Cannabis Legalization. Frontiers in Public Health 2019;7 View
  9. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  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 View
  11. Yin Z, Sulieman L, Malin B. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561 View
  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 View
  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 View
  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 View
  15. Lamy F, Daniulaityte R, Zatreh M, Nahhas R, Sheth A, Martins S, Boyer E, Carlson R. "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 View
  16. Kim S, Marsch L, Hancock J, Das A. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353 View
  17. Daniulaityte R, Lamy F, Barratt M, Nahhas R, Martins S, Boyer E, Sheth A, Carlson R. Characterizing marijuana concentrate users: A web-based survey. Drug and Alcohol Dependence 2017;178:399 View
  18. He L, Yin T, Hu Z, Chen Y, Hanauer D, 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 View
  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 View
  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 View
  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 View
  22. Tsai M, 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 View
  23. Obiedat R, Al-Qaisi L, Qaddoura R, Harfoushi O, Al-Zoubi A. An Intelligent Hybrid Sentiment Analyzer for Personal Protective Medical Equipments Based on Word Embedding Technique: The COVID-19 Era. Symmetry 2021;13(12):2287 View
  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) View
  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 View
  26. Black J, Margolin Z, Bau G, Olson R, Iwanicki J, Dart R. 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 View
  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 View
  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 View
  29. Rahim A, Ibrahim M, Chua S, Musa K. Hospital Facebook Reviews Analysis Using a Machine Learning Sentiment Analyzer and Quality Classifier. Healthcare 2021;9(12):1679 View
  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 View
  31. A. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. 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 View
  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 View
  33. Tang J, Arvind V, Dominy C, White C, Cho S, Kim J. How Are Patients Reviewing Spine Surgeons Online? A Sentiment Analysis of Physician Review Website Written Comments. Global Spine Journal 2023;13(8):2107 View
  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 View
  35. Rahim A, Ibrahim M, Musa K, Chua S, Yaacob N. Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook. Healthcare 2021;9(10):1369 View
  36. Walker A, LoParco C, Rossheim M, Livingston M. #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 View
  37. Paul S. 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 View
  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 View
  39. Lossio-Ventura J, Weger R, Lee A, Guinee E, 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 View
  40. Luo J, Yoo J, Park J. ‘ 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 View

Books/Policy Documents

  1. Natsiavas P, Maglaveras N, Koutkias V. Knowledge Representation for Health Care. View
  2. Kursuncu U, Gaur M, Lokala U, Thirunarayan K, Sheth A, Arpinar I. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. View
  3. Zhang H, Wheldon C, Tao C, Dunn A, Guo Y, Huo J, Bian J. Social Web and Health Research. View
  4. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Social Media and the Armed Forces. View
  5. Portelli B, Passabì D, Lenzi E, Serra G, Santus E, Chersoni E. AI for Disease Surveillance and Pandemic Intelligence. View
  6. Leightley D, Sharp M, Williamson V, Fear N, Gribble R. Soziale Medien und die Streitkräfte. View