Email: karami At uab Dot edu
Associate Professor of Quantitative Methods/Business Analytics, UAB Collat School of Business
Scientist, Center for Clinical and Translational Science (CCST), UAB School of Medicine
Scientist, Center for Outcomes and Effectiveness Research and Education (COERE), UAB School of Medicine
Faculty Associate, South Carolina SmartState Center for Healthcare Quality (CHQ) at the Arnold School of Public Health
Faculty Associate, UofSC College of Information and Communications
Advisory Committee, Big Data Health Science Center (BDHSC)
Research Interests: business analytics, social media analytics, AI, misinformation, health informatics, text mining, and computational social science.
Publications: Google Scholar/ResearchGate/NCBI
Data and Tools: GitHub
News
False Information about AI: Did Pope Francis Really Endorse AI as a Divine Channel? was published in Information Matters.
Deciphering Latent Health Information in Social Media Using a Mixed-Methods Design was published in Healthcare.
Received the ASIS&T SIG Social Media Annual Workshop Best Paper Award Honorable Mention for "Analysis of Geotagging Behavior: Do Geotagged Users Represent the Twitter Population?".
Joined UAB Collat School of Business as an Associate Professor of Quantitative Methods/Business Analytics in the Department of Management, Information Systems & Quantitative Methods.
Appoitned as the Associate Dean for Research at the UofSC College of Information and Communications for three years.
2020 U.S. presidential election in swing states: Gender differences in Twitter conversations was published in International Journal of Information Management Data Insights.
Social media and COVID-19: Characterizing anti-quarantine comments on Twitter was among the top 10 most downloaded papers in Wiley.
Received the Healthcare 2021 Young Investigator (Under 40) Award.
I am the guest editor for the Open-Access Special Issue "Social Media for Health Information Management" for Healthcare (IF: 3.160 & Indexed in PubMed).
The leading neighborhood-level predictors of drug overdose: A mixed machine learning and spatial approach was published in Drug and Alcohol Dependence.
