Xueqi Cheng

Xueqi Cheng, Qinwen Ge, Hamid Karimi et al.

Adverse social interactions (ASIs) can shape how online communities evolve over the time. However, structural-based ASIs and content-based ASIs are often studied separately and at a single analytical scale. In this study, we propose a multi-level framework to examine how adverse social interactions appear locally, spread through neighborhoods, and disrupt cohesive subgroups. Using large-scale datasets from X and Bluesky, we analyze friend and foe patterns at the micro level, peer influence through matched triadic designs at the meso level, and subgroup disruption against random and recommendation-based references at the macro level. Our results show that structural disconnection and toxic communication provide complementary signals: structural negativity more persistently marks subgroup disruption, while toxic communication captures broader conflict both within and across communities. These findings suggest that adverse social interactions are multi-scale processes that influence how online communities form, fracture, and evolve. Our source code is publicly available at https://github.com/XueqiC/Adverse-Social-Interactions.

Weicheng Ren, Zixuan Li, Long Bai et al.

Event ontology expansion aims to discover emerging event types from data and extend them to appropriate positions in the existing event ontology.. Existing methods typically cluster contextualized trigger representations and attach induced clusters to the ontology based on instance-level similarity. However, ontology expansion requires concept-level semantics that characterize event types, whereas contextualized trigger representations often conflate these semantics with surface contextual variation, leading to unstable clustering and unreliable hierarchy expansion. To address this issue, we propose ConceptE, a conceptualization-enhanced framework for event ontology expansion. ConceptE first derives concept-level semantics by prompting an LLM with the sentence and event trigger, producing a concise concept name and a natural-language description. It then jointly encodes these semantics with trigger information to build concept-enhanced representations aligned with ontology-level reasoning. This representation design supports more coherent event clustering, more reliable hierarchy expansion, and ontology-consistent type naming. Experiments on ACE, ERE, and MAVEN demonstrate that ConceptE consistently outperforms state-of-the-art approaches across all subtasks of event ontology expansion. In particular, it achieves improvements of up to 12.37\% in BCubed-F1 for event clustering and 6.48\% in Taxo\_F1 for hierarchy expansion, demonstrating the effectiveness of the proposed ConceptE method.