Zobrazeno 1 - 10
of 35
pro vyhledávání: '"Jeffrey Sorensen"'
Autor:
Alexandros Xenos, John Pavlopoulos, Ion Androutsopoulos, Lucas Dixon, Jeffrey Sorensen, Léo Laugier
Publikováno v:
First Monday; Volume 27, Number 9-5 September 2022
User posts whose perceived toxicity depends on conversational context are rare in current toxicity detection datasets. Hence, toxicity detectors trained on existing datasets will also tend to disregard context, making the detection of context-sensiti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf883808211e26af72877cae5d7a2994
https://firstmonday.org/ojs/index.php/fm/article/view/12285
https://firstmonday.org/ojs/index.php/fm/article/view/12285
Autor:
Alyssa Lees, Vinh Q. Tran, Yi Tay, Jeffrey Sorensen, Jai Gupta, Donald Metzler, Lucy Vasserman
On the world wide web, toxic content detectors are a crucial line of defense against potentially hateful and offensive messages. As such, building highly effective classifiers that enable a safer internet is an important research area. Moreover, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::296282522a24425881a98c4051ae19cc
http://arxiv.org/abs/2202.11176
http://arxiv.org/abs/2202.11176
Publikováno v:
Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6d99895c4afe98b9eb45faff27b5fddb
https://doi.org/10.18653/v1/2022.woah-1.9
https://doi.org/10.18653/v1/2022.woah-1.9
Publikováno v:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d13a5c7e8877348605aef4da322bdecb
https://doi.org/10.18653/v1/2022.acl-long.259
https://doi.org/10.18653/v1/2022.acl-long.259
Autor:
Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, Aurora Saibene, Berta Chulvi, Paolo Rosso, Alyssa Lees, Jeffrey Sorensen
The paper describes the SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification (MAMI),which explores the detection of misogynous memes on the web by taking advantage of available texts and images. The task has been organised in two related
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb3b75534fcfabe5e1d49beb0b8f2c8c
http://hdl.handle.net/10281/390528
http://hdl.handle.net/10281/390528
Autor:
Prashanth Ramakrishna, Rachel Greenstadt, Damon McCoy, Tobias Lauinger, Anna Turner, Kejsi Take, Max Aliapoulios, Beth Goldberg, Daniel Borkan, Jeffrey Sorensen
Publikováno v:
Internet Measurement Conference
Attack strategies used by online harassers have evolved over time to inflict increasing harm to their targets. In addition to scaling harassment through incitement and coordination, online communities that commonly engage in harassment are likely a s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9060a670509615f632112a4558faa820
https://doi.org/10.1145/3487552.3487852
https://doi.org/10.1145/3487552.3487852
Publikováno v:
SemEval@ACL/IJCNLP
The Toxic Spans Detection task of SemEval-2021 required participants to predict the spans of toxic posts that were responsible for the toxic label of the posts. The task could be addressed as supervised sequence labeling, using training data with gol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3051fc5c8fc95543a34ae7216e8d642e
https://doi.org/10.18653/v1/2021.semeval-1.6
https://doi.org/10.18653/v1/2021.semeval-1.6
Publikováno v:
EACL
Platforms that support online commentary, from social networks to news sites, are increasingly leveraging machine learning to assist their moderation efforts. But this process does not typically provide feedback to the author that would help them con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::97ed65e97c49718a1265c88166bda791
Publikováno v:
ACL
Moderation is crucial to promoting healthy on-line discussions. Although several `toxicity' detection datasets and models have been published, most of them ignore the context of the posts, implicitly assuming that comments maybe judged independently.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2c28edabcf319902035bb62b13a6842f
Autor:
Guillaume Sylvain, Maayan Roichman, Jordan Gifford-Moore, Jory Flemming, Ilan Price, Jeffrey Sorensen, Saul Musker, Nithum Thain, Lucas Dixon
Publikováno v:
WOAH
We present a new dataset of approximately 44000 comments labeled by crowdworkers. Each comment is labelled as either ‘healthy’ or ‘unhealthy’, in addition to binary labels for the presence of six potentially ‘unhealthy’ sub-attributes: (1
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f3ad33ddc767baa5dc7d8a1e76271e68
https://doi.org/10.18653/v1/2020.alw-1.15
https://doi.org/10.18653/v1/2020.alw-1.15