  Identifying the targets of hate speech is a crucial step in grasping the
nature of such speech and, ultimately, in improving the detection of offensive
posts on online forums. Much harmful content on online platforms uses implicit
language especially when targeting vulnerable and protected groups such as
using stereotypical characteristics instead of explicit target names, making it
harder to detect and mitigate the language. In this study, we focus on
identifying implied targets of hate speech, essential for recognizing subtler
hate speech and enhancing the detection of harmful content on digital
platforms. We define a new task aimed at identifying the targets even when they
are not explicitly stated. To address that task, we collect and annotate target
spans in three prominent implicit hate speech datasets: SBIC, DynaHate, and
IHC. We call the resulting merged collection Implicit-Target-Span. The
collection is achieved using an innovative pooling method with matching scores
based on human annotations and Large Language Models (LLMs). Our experiments
indicate that Implicit-Target-Span provides a challenging test bed for target
span detection methods.
