The X types: Mapping the semantics of the Twitter sphere

While existing knowledge bases maintain factual world knowledge, we view the social network of Twitter (now, X) as a complementary source of social world knowledge. Treating popular Twitter accounts as entities of general interest, our aim is to elicit useful semantic information about these entities that may be used by downstream applications. We first address the task of entity typing, such as determining whether an account belongs to a politician or a musical artist. To obtain labeled data, we align a subset of social entities with DBpedia and Wikidata, yielding an aligned dataset of over 20K Twitter entities annotated with 136 fine-grained semantic types. In learning models of type prediction, we process network and content-based evidence as entity embeddings, where we finetune the textual encoder on the semantic typing task. Our best classifier yields weighted F1 performance of 0.68 on set-aside labeled examples; manual inspection indicates that many apparent mismatches correspond to semantically plausible alternative types. Applying classification at large-scale, we obtain semantic embeddings and types for as many as 200K social entities, many of which are not covered by existing factual knowledge bases. Our study offers insights into the distribution of entity types across the ‘Twitter sphere’. Additionally, we show that content-based embeddings encode fine-grained entity semantics, as demonstrated on the key task of entity similarity assessment. We believe that the inferred semantic entity types and embeddings can serve various applications, such as social conversation and recommendation tasks using LLM agents, which involve social entities. We make all artifacts of this work available to the research community.