BibTeX
@InProceedings{falk-etal-2021-predicting,
title = "Predicting Moderation of Deliberative Arguments: Is Argument
Quality the Key?",
author = "Falk, Neele and
Jundi, Iman and
Vecchi, Eva Maria and
Lapesa, Gabriella",
booktitle = "Proceedings of the 8th Workshop on Argument Mining",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.argmining-1.13",
pages = "133-141",
}
@inproceedings{falk-etal-2024-moderation,
title = "Moderation in the Wild: Investigating User-Driven Moderation in Online Discussions",
author = "Falk, Neele and
Vecchi, Eva and
Jundi, Iman and
Lapesa, Gabriella",
editor = "Graham, Yvette and
Purver, Matthew",
booktitle = "Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = mar,
year = "2024",
address = "St. Julian{'}s, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.eacl-long.60/",
pages = "992-1013",
abstract = "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. { textquotedblleft }Ordinary{ textquotedblright } 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 whichusers 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 releaseof UMOD is complemented by two analyses which focus on the constitutive features of constructiveness in user moderation and on thesources of annotator disagreements, given the high subjectivity of the task."
}
@inproceedings{jundi-etal-2023-node,
title = "Node Placement in Argument Maps: Modeling Unidirectional Relations in High {&} Low-Resource Scenarios",
author = "Jundi, Iman and
Falk, Neele and
Vecchi, Eva Maria and
Lapesa, Gabriella",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.322/",
doi = "10.18653/v1/2023.acl-long.322",
pages = "5854-5876",
abstract = "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."
}
@inproceedings{jundi-etal-2025-negative,
title = "It Is Not Only the Negative that Deserves Attention! Understanding, Generation {&} Evaluation of (Positive) Moderation",
author = "Jundi, Iman and
Vecchi, Eva Maria and
Quensel, Carlotta and
Falk, Neele and
Lapesa, Gabriella",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "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)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-long.567/",
pages = "11360-11395",
ISBN = "979-8-89176-189-6",
abstract = "Moderation is essential for maintaining and improving the quality of online discussions. This involves: (1) countering negativity, e.g. hate speech and toxicity, and (2) promoting positive discourse, e.g. broadening the discussion to involve other users and perspectives. While significant efforts have focused on addressing negativity, driven by an urgency to address such issues, this left moderation promoting positive discourse (henceforth PositiveModeration) under-studied. With the recent advancements in LLMs, Positive Moderation can potentially be scaled to vast conversations, fostering more thoughtful discussions and bridging the increasing divide in online interactions.We advance the understanding of Positive Moderation by annotating a dataset on 13 moderation properties, e.g. neutrality, clarity and curiosity. We extract instructions from professional moderation guidelines and use them to prompt LLaMA to generate such moderation. This is followed by extensive evaluation showing that (1) annotators rate generated higher than professional moderation, but still slightly prefer professional moderation in pairwise comparison, and (2) LLMs can be used to estimate human evaluation as an efficient alternative."
}
@InProceedings{vecchi-etal-2021-towards,
title = "Towards Argument Mining for Social Good: A Survey",
author = "Vecchi, Eva Maria and
Falk, Neele and
Jundi, Iman and
Lapesa, Gabriella",
booktitle = "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)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.107",
doi = "10.18653/v1/2021.acl-long.107",
pages = "1338-1352",
}