Human moderators in online discussions face a heterogeneous range of tasks, which go beyond content moderation, or policing.They also support and improve discussion quality, which is challenging to model (and evaluate) in NLP due to its inherent subjectivity and the scarcity of annotated resources.We address this gap by introducing PerspectiveMod, a dataset of online comments annotated for the question: "Does this comment require moderation, and why?" Annotations were collected from both expert moderators and trained nonexperts.PerspectiveMod is unique in its intentional variation across (a) the level of moderation experience embedded in the source data (professional vs. non-professional moderation environments), (b) the annotator profiles (experts vs. trained crowdworkers), and (c) the richness of each moderation judgment, both in terms on fine-grained comment properties (drawn from argumentation and deliberative theory) and in the representation of the individuality of the annotator (socio-demographics and attitudes towards the task).We advance understanding of the task's complexity by providing interpretation layers that account for its subjectivity.Our statistical analysis highlights the value of collecting annotator perspectives, including their experiences, attitudes, and views on AI, as a foundation for developing more context-aware and interpretively robust moderation tools.

Iman Jundi, Eva Maria Vecchi, Carlotta Quensel, Neele Falk, Gabriella Lapesa. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Effective content moderation is imperative for fostering healthy and productive discussions in online domains.Despite the substantial efforts of moderators, the overwhelming nature of discussion flow can limit their effectiveness.However, it is not only trained moderators who intervene in online discussions to improve their quality."Ordinary" users also act as moderators, actively intervening to correct information of other users' posts, enhance arguments, and steer discussions back on course.This paper introduces the phenomenon of user moderation, documenting and releasing UMOD, the first dataset of comments in which users act as moderators.UMOD contains 1000 comment-reply pairs from the subreddit r/changemyview with crowdsourced annotations from a large annotator pool and with a fine-grained annotation schema targeting the functions of moderation, stylistic properties (aggressiveness, subjectivity, sentiment), constructiveness, as well as the individual perspectives of the annotators on the task.The release of UMOD is complemented by two analyses which focus on the constitutive features of constructiveness in user moderation and on the sources of annotator disagreements, given the high subjectivity of the task.

Argument maps structure discourse into nodes in a tree with each node being an argument that supports or opposes its parent argument.This format is more comprehensible and less redundant compared to an unstructured one.Exploring those maps and maintaining their structure by placing new arguments under suitable parents is more challenging for users with huge maps that are typical in online discussions.To support those users, we introduce the task of node placement: suggesting candidate nodes as parents for a new contribution.We establish an upper-bound of human performance, and conduct experiments with models of various sizes and training strategies.We experiment with a selection of maps from Kialo, drawn from a heterogeneous set of domains.Based on an annotation study, we highlight the ambiguity of the task that makes it challenging for both humans and models.We examine the unidirectional relation between tree nodes and show that encoding a node into different embeddings for each of the parent and child cases improves performance.We further show the few-shot effectiveness of our approach.

Eva Maria Vecchi, Neele Falk, Iman Jundi, Gabriella Lapesa. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.