AIR-JPMC@SMM4H'22: Classifying Self-Reported Intimate Partner Violence in Tweets with Multiple BERT-based Models
This work addresses the problem of detecting intimate partner violence reports on social media for public health monitoring, but it is incremental as it builds on existing BERT-based methods.
The paper tackled the classification of self-reported intimate partner violence in English tweets, achieving a 13% improvement over the baseline and ranking as the best performing system in the SMM4H 2022 shared task.
This paper presents our submission for the SMM4H 2022-Shared Task on the classification of self-reported intimate partner violence on Twitter (in English). The goal of this task was to accurately determine if the contents of a given tweet demonstrated someone reporting their own experience with intimate partner violence. The submitted system is an ensemble of five RoBERTa models each weighted by their respective F1-scores on the validation data-set. This system performed 13% better than the baseline and was the best performing system overall for this shared task.