Delivery Optimized Discovery in Behavioral User Segmentation under Budget Constrain

Users' behavioral footprints online enable firms to discover behavior-based user segments (or, segments) and deliver segment specific messages to users. Following the discovery of segments, delivery of messages to users through preferred media channels like Facebook and Google can be challenging, as only a portion of users in a behavior segment find match in a medium, and only a fraction of those matched actually see the message (exposure). Even high quality discovery becomes futile when delivery fails. Many sophisticated algorithms exist for discovering behavioral segments; however, these ignore the delivery component. The problem is compounded because (i) the discovery is performed on the behavior data space in firms' data (e.g., user clicks), while the delivery is predicated on the static data space (e.g., geo, age) as defined by media; and (ii) firms work under budget constraint. We introduce a stochastic optimization based algorithm for delivery optimized discovery of behavioral user segmentation and offer new metrics to address the joint optimization. We leverage optimization under a budget constraint for delivery combined with a learning-based component for discovery. Extensive experiments on a public dataset from Google and a proprietary dataset show the effectiveness of our approach by simultaneously improving delivery metrics, reducing budget spend and achieving strong predictive performance in discovery.

Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings

Social media plays a pivotal role in disseminating news across the globe and acts as a platform for people to express their opinions on a variety of topics. COVID-19 vaccination drives across the globe are accompanied by a wide variety of expressed opinions, often colored by emotions. We extracted a corpus of Twitter posts related to COVID-19 vaccination and created two classes of lexical categories - Emotions and Influencing factors. Using unsupervised word embeddings, we tracked the longitudinal change in the latent space of the lexical categories in five countries with strong vaccine roll-out programs, i.e. India, USA, Brazil, UK, and Australia. Nearly 600 thousand vaccine-related tweets from the United States and India were analyzed for an overall understanding of the situation around the world for the time period of 8 months from June 2020 to January 2021. Cosine distance between lexical categories was used to create similarity networks and modules using community detection algorithms. We demonstrate that negative emotions like hesitancy towards vaccines have a high correlation with health-related effects and misinformation. These associations formed a major module with the highest importance in the network formed for January 2021, when millions of vaccines were administered. The relationship between emotions and influencing factors were found to be variable across the countries. By extracting and visualizing these, we propose that such a framework may be helpful in guiding the design of effective vaccine campaigns and can be used by policymakers for modeling vaccine uptake.