My research uses empirical modeling on large datasets to understand firms' strategies for social media activism as well as their purpose-driven online branding strategies promoting positive social change. Using natural language processing, machine learning and econometric models on unstructured data at both firm and individual levels, my research converts large-scale Twitter texts into marketing insights with the purpose of strengthening firms' strategies on societal issues.

 

Research Interest:

Substantive: Social Media and Digital Marketing, Online Branding Strategies, Digital Platforms,
User-Generated Content (Text and Images).
Methodological: Text Mining and Natural Language Processing, Applied Econometrics,
Causal Inference, Machine Learning

Selected Research:

Guha, Mithila and Daniel Korschun, “Peer Effects on Brand Activism: Evidence from Firm-Generated Content And Online Chatter”, Revise & Resubmit (Minor Revision at the First Round)


Guha, Mithila, Daniel Korschun and Trina Andras, “Callouts and Shout-outs: Are Online Mentions On Social Issues Loud Enough To Drive Brand Performance?”

Selected Conference Presentation:
"Peer Effects on Brand Activism: Evidence from Firm-Generated Content And Online Chatter"

    • AMA Summer Academic Conference, 2022.
   -Best Track Paper Award Winner: Digital and Social Media Marketing Track.
   • ISMS Marketing Science Conference, 2022.
   • Drexel Emerging Graduate Scholars (DEGS) Conference, 2022.