Don’t Mention It? Analyzing User-Generated Content Signals for Early Adverse Event Warnings

Published Online:https://doi.org/10.1287/isre.2019.0847

References

  • Abbasi A, Adjeroh D (2014) Social media analytics for smart health. IEEE Intelligence Systems 29(2):60–64.CrossrefGoogle Scholar
  • Abbasi A, Sarker S, Chiang RH (2016) Big data research in information systems: Toward an inclusive research agenda. J. Assoc. Inform. Systems 17(2):Article 3.Google Scholar
  • Abbasi A, Zhou Y, Deng S, Zhang P (2018) Text analytics to support sense-making in social media: A language-action perspective. Management Inform. Systems Quart. 42(2):427–464.CrossrefGoogle Scholar
  • Abrahams AS, Jiao J, Wang GA, Fan W (2012) Vehicle defect discovery from social media. Decision Support Systems 54(1):87–97.CrossrefGoogle Scholar
  • Abrahams AS, Fan W, Wang GA, Zhang ZJ, Jiao J (2015) An integrated text analytic framework for product defect discovery. Production Oper. Management 24(6):975–990.CrossrefGoogle Scholar
  • Abrahams AS, Jiao J, Fan W, Wang GA, Zhang Z (2013) What’s buzzing in the blizzard of buzz? Automotive component isolation in social media postings. Decision Support Systems 55(4):871–882.CrossrefGoogle Scholar
  • Adjeroh D, Beal R, Abbasi A, Zheng W, Abate M, Ross A (2014) Signal fusion for social media analysis of adverse drug events. IEEE Intelligence Systems 29(2):74–80.Google Scholar
  • Agarwal R, Dhar V (2014) Big data, data science, and analytics: The opportunity and challenge for IS research. Inform. Systems Res. 25(3):443–448.LinkGoogle Scholar
  • Agarwal R, Gao G, DesRoches C, Jha AK (2010) The digital transformation of healthcare: Current status and the road ahead. Inform. Systems Res. 21(4):796–809.LinkGoogle Scholar
  • Anderson CL, Agarwal R (2011) The digitization of healthcare: Boundary risks, emotion, and consumer willingness to disclose personal health information. Inform. Systems Res. 22(3):469–490.LinkGoogle Scholar
  • Aytug H, Koehler GJ (1996) Stopping criteria for finite length genetic algorithms. INFORMS J. Comput. 8(2):183–191.LinkGoogle Scholar
  • Bardhan I, Oh J, Zheng Z, Kirksey K (2015) Predictive analytics for readmission of patients with congestive heart failure: Analysis across multiple hospitals. Inform. Systems Res. 26(1):19–39.LinkGoogle Scholar
  • Blattberg RC, Byung-Do K, Neslin SA (2008) Database Marketing: Analyzing and Managing Customers (Springer, New York).CrossrefGoogle Scholar
  • Bollen J, Mao H, Zeng X (2011) Twitter mood predicts the stock market. J. Comput. Sci. 2(1):1–8.CrossrefGoogle Scholar
  • Boynton J (2013) How the voice of the people is driving corporate social responsibility. Harvard Bus. Rev. (July 17), https://hbr.org/2013/07/how-the-voice-of-the-people-is.Google Scholar
  • Broniatowski D, Paul MJ, Dredze M (2014) National influenza surveillance through Twitter. PLoS One 8(12):e83672.CrossrefGoogle Scholar
  • Browne J, Manning H, O’Connor C (2015) How to use text analytics in your VoC program. Report, Forrester Research, Cambridge, MA.Google Scholar
  • Brynjolfsson E, Geva T, Reichman S (2016) Crowd-squared: Amplifying the predictive power of search trend data. Management Inform. Systems Quart. 40(4):941–961.CrossrefGoogle Scholar
  • Cassino D (2016) The ‘wisdom of the crowd’ has a pretty bad track record at predicting jobs reports. Harvard Bus. Rev. (July 8), https://hbr.org/2016/07/the-wisdom-of-the-crowd-has-a-pretty-bad-track-record-at-predicting-jobs-reports.Google Scholar
  • Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: From big data to big impact. Management Inform. Systems Quart. 36(4):1165–1188.CrossrefGoogle Scholar
  • Chen Y, Ganesan S, Liu Y (2009) Does a firm’s product-recall strategy affect its financial value? An examination of strategic alternatives during product-harm crises. J. Marketing 73(6):214–226.CrossrefGoogle Scholar
  • Colella-Walsh S (2019) Pradaxa (Dabigatran) trials continue nationwide. Accessed February 10, 2019, https://www.natlawreview.com/article/pradaxa-dabigatran-trials-continue-nationwide.Google Scholar
  • Das SR, Chen MY (2007) Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Sci. 53(9):1375–1388.LinkGoogle Scholar
  • Davies J (2015) 15 voice-of-the-customer best practices linked to organizational maturity. Report, Gartner, Stamford, CT.Google Scholar
  • Davies J (2016) How to start creating a voice-of-the-customer strategy. Report, Gartner, Stamford, CT.Google Scholar
  • DuMouchel W (1999) Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Amer. Statist. 53(3):177–190.Google Scholar
  • Esuli A, Sebastiani F (2006) Sentiwordnet: A publicly available lexical resource for opinion mining. Proc. Language Resources Evaluation Conf. (European Language Resources Association, Luxembourg), 417–422.Google Scholar
  • Fang X, Hu PJH, Li Z, Tsai W (2013) Predicting adoption probabilities in social networks. Inform. Systems Res. 24(1):128–145.LinkGoogle Scholar
  • Fenwick N, Leaver S, Kark K, Paderni LS, Blackburn L (2011) Social business strategy: An IT execution plan. Report, Forrester Research, Cambridge, MA.Google Scholar
  • Fichman RG, Kohli R, Krishnan R (2011) The role of information systems in healthcare: Current research and future trends. Inform. Systems Res. 22(3):419–428.LinkGoogle Scholar
  • Forster AJ, Jennings A, Chow C, Leeder C, van Walraven C (2012) A systematic review to evaluate the accuracy of electronic adverse drug event detection. J. Amer. Medical Informatics Assoc. 19(1):31–38.CrossrefGoogle Scholar
  • Fredericks B (2014) Toyota to pay $1.2B settlement in vehicle acceleration lawsuit. New York Post (March 19), https://nypost.com/2014/03/19/toyota-to-pay-1-2b-settlement-in-vehicle-acceleration-lawsuit/.Google Scholar
  • Gregor S, Hevner AR (2013) Positioning and presenting design science research for maximum impact. Management Inform. Systems Quart. 37(2):337–355.CrossrefGoogle Scholar
  • Hadzi-Puric J, Grmusa J (2012) Automatic drug adverse reaction discovery from parenting websites using disproportionality methods. IEEE Internat. Conf. Adv. Soc. Networks Anal. Mining (IEEE, Piscataway, NJ), 792–797.CrossrefGoogle Scholar
  • Hassan A, Abbasi A, Zeng D (2013) Twitter sentiment analysis: A bootstrap ensemble framework. IEEE Internat. Conf. Soc. Comput. (IEEE, Piscataway, NJ), 357–364.Google Scholar
  • Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. Management Inform. Systems Quart. 28(1):75–105.CrossrefGoogle Scholar
  • Hora M, Bapuji H, Roth AV (2011) Safety hazard and time to recall: The role of recall strategy, product defect type, and supply chain player. J. Oper. Management 29(7):766–777.CrossrefGoogle Scholar
  • Jin HW, Chen J, He H, Kelman C, McAullay D, O’Keefe CM (2010) Signaling potential adverse drug reactions from administrative health databases. IEEE Trans. Knowledge Data Engrg. 22(6):839–853.CrossrefGoogle Scholar
  • Karimi S, Wang C, Metke-Jimenez A, Gaire R, Paris C (2015) Text and data mining techniques in adverse drug reaction detection. ACM Comput. Surveys 47(4):Article 56.Google Scholar
  • Kitchens B, Dobolyi D, Li J, Abbasi A (2018) Advanced customer analytics: Strategic value through integration of relationship-oriented big data. J. Management Inform. Systems 35(2):540–574.CrossrefGoogle Scholar
  • Kwak H, Lee C, Park H, Moon S (2010) What is Twitter, a social network or a news media? Proc. 19th ACM Internat. Conf. World Wide Web (ACM, New York), 591–600.CrossrefGoogle Scholar
  • Lardon J, Abdellaoui R, Bellet F, Asfari H, Bousquet C (2015) Adverse drug reaction identification and extraction in social media: A scoping review. J. Medical Internet Res. 17(7):1–16.CrossrefGoogle Scholar
  • Lau R, Liao S, Wong KF, Dickson K (2012) Web 2.0 environmental scanning and adaptive decision support for business mergers and acquisitions. Management Inform. Systems Quart. 36(4):1239–1268.CrossrefGoogle Scholar
  • Lazer D, King G, Vespignani A (2014) The parable of Google Flu: Traps in big data analysis. Science 343(6176):1203–1205.CrossrefGoogle Scholar
  • Lindquist M (2008) VigiBase, the WHO Global ICSR Database System. Drug Inform. J. 42(5):409–419.CrossrefGoogle Scholar
  • Liu M, Hinz ERM, Xu H (2013) Comparative analysis of pharmacovigilance methods in the detection of adverse drug reactions using electronic medical records. J. Amer. Medical Inform. Assoc. 20(3):420–426.CrossrefGoogle Scholar
  • Liu X, Chen H (2013) AZDrugMiner: An information extraction system for mining patient-reported adverse drug events in online patient forums. Zeng D, Yang CC, Tseng VS, Xing C, Chen H, Wang F-Y, Zheng X, eds. Proc. Internat. Conf. Smart Health (Springer, Berlin), 134–150.CrossrefGoogle Scholar
  • Provost F, Fawcett T (2013) Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking (O'Reilly Media, Sebastopol, CA).Google Scholar
  • Provost F, Martens D, Murray A (2015) Finding similar mobile consumers with a privacy-friendly geosocial design. Inform. Systems Res. 26(2):243–265.LinkGoogle Scholar
  • Ritter JM (2008) Minimising harm: Human variation and ADRs. Brit. J. Clinical Pharmacol. 65(4):451–452.CrossrefGoogle Scholar
  • Sampathkumar H, Chen XW, Luo B (2014) Mining adverse drug reactions from online healthcare forums using hidden Markov model. BMC Medical Informatics Decision Making 14(1):Article 91.Google Scholar
  • Sarker A, Gonzalez G (2015) Portable automatic text classification for adverse drug reaction detection via multi-corpus training. J. Biomedical Inform. 53(February):196–207.CrossrefGoogle Scholar
  • Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, Gonzalez G (2015) Utilizing social media data for pharmacovigilance: A review. J. Biomedical Inform. 54(April):202–212.CrossrefGoogle Scholar
  • Schmidt-Subramanian M, Manning H, Czarnecki D (2014) The state of voice-of-the-customer programs: It’s time to act. Report, Forrester Research, Cambridge, MA.Google Scholar
  • Schweinsberg C (2012) Toyota agrees to $1.1 billion settlement in unintended acceleration lawsuit. Accessed May 1, 2017, https://www.wardsauto.com/industry/toyota-agrees-11-billion-settlement-unintended-acceleration-lawsuit.Google Scholar
  • Sharif H, Zaffar F, Abbasi A, Zimbra D (2014) Detecting adverse drug reactions using a sentiment classification framework. Proc. 6th ASE Internat. Conf. Soc. Comput. (IEEE, Piscataway, NJ), 231–240.Google Scholar
  • Shmueli G, Koppius OR (2011) Predictive analytics in information systems research. Management Inform. Systems Quart. 35(3):553–572.CrossrefGoogle Scholar
  • Song Y, Sahoo N, Srinivasan S, Dellarocas C (2014) Uncovering path-to-purchase segments in large consumer population. 24th Workshop on Information Technologies and Systems, Auckland, NZ.Google Scholar
  • Sunstein C (2006) When crowds aren’t wise. Harvard Bus. Rev. 9(September):20–21.Google Scholar
  • Surowiecki J (2005) The Wisdom of Crowds (Anchor Press, New York).Google Scholar
  • Thomas K (2014) $650 Million to settle blood thinner lawsuits. New York Times (May 28), https://www.nytimes.com/2014/05/29/business/international/german-drug-company-to-pay-650-million-to-settle-blood-thinner-lawsuits.html.Google Scholar
  • Turney PD, Littman ML (2003) Measuring praise and criticism: Inference of semantic orientation from association. ACM Trans. Inform. Systems 21(4):315–346.CrossrefGoogle Scholar
  • Wasko MM, Faraj S (2005) Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. Management Inform. Systems Quart. 29(1):35–57.CrossrefGoogle Scholar
  • White RW, Tatonetti N, Shah N, Altman RB, Horvitz E (2013) Web-scale pharmacovigilance: Listening to signals from the crowd. J. Amer. Med. Inform. Assoc. 20(3):404–408.CrossrefGoogle Scholar
  • Xu R, Wang Q (2014) Large-scale combining signals from both biomedical literature and FAERS to improve post-marketing drug safety signal detection. BMC Bioinformatics 15(1):Article 17.CrossrefGoogle Scholar
  • Yang CC, Yang H, Jiang L (2014) Postmarketing drug safety surveillance using publicly available health-consumer-contributed content in socia media. ACM Trans. Management Inform. Systems 5(1):Article 2.Google Scholar
  • Yang M, Wang X, Kiang MY (2013) Identification of consumer adverse drug reaction messages on social media. Proc. Pacific-Asia Conf. Inform. Systems (Association for Information Systems, Atlanta), Paper 193.Google Scholar
  • Zabin J, Nail J, Wilder SK (2011) Gleansight social intelligence. Report, Gleanster Research, Pleasanton, CA.Google Scholar
  • Zeng D, Chen H, Li S (2010) Social media analytics and intelligence. IEEE Intelligence Systems 25(6):13–16.CrossrefGoogle Scholar
  • Zimbra D, Abbasi A, Zeng D, Chen H (2018) The state-of-the-art in Twitter sentiment analysis: A review and benchmark evaluation. ACM Trans. Management Inform. Systems 9(2):Article 5.Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.