| 1 |
Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
|
|
|
|
In: ISSN: 2561-326X ; JMIR Formative Research ; https://hal.archives-ouvertes.fr/hal-03614832 ; JMIR Formative Research, JMIR Publications 2022, 6 (2), pp.e18539. ⟨10.2196/18539⟩ ; https://formative.jmir.org/2022/2/e18539 (2022)
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Psychiatry on Twitter: Content Analysis of the Use of Psychiatric Terms in French
|
|
|
|
In: JMIR Form Res (2022)
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
|
|
|
|
In: ISSN: 1866-9956 ; EISSN: 1866-9964 ; Cognitive Computation ; https://hal.archives-ouvertes.fr/hal-03275549 ; Cognitive Computation, Springer, 2021, 13 (4), ⟨10.1007/s12559-021-09862-5⟩ ; https://link.springer.com/article/10.1007%2Fs12559-021-09862-5 (2021)
|
|
BASE
|
|
Show details
|
|
|
| 4 |
“Be nice to your wife! The restaurants are closed”: Can Gender Stereotype Detection Improve Sexism Classification?
|
|
|
|
In: Findings of the Association for Computational Linguistics: EMNLP 2021 ; Conference on Findings of the Association for Computational Linguistics (EMNLP 2021) ; https://hal.archives-ouvertes.fr/hal-03468351 ; Conference on Findings of the Association for Computational Linguistics (EMNLP 2021), ACL: Association for Computational Linguistics, Nov 2021, Punta Cana, Dominican Republic. pp.2833-2844 ; https://aclanthology.org/2021.findings-emnlp.242/ (2021)
|
|
BASE
|
|
Show details
|
|
|
| 5 |
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
|
|
|
|
In: Cognit Comput (2021)
|
|
BASE
|
|
Show details
|
|
|
| 6 |
Emotionally Informed Hate Speech Detection: A Multi-target Perspective
|
|
|
|
Abstract:
Hate Speech and harassment are widespread in online communication, due to users’ freedom and anonymity and the lack of regulation provided by social media platforms. Hate speech is topically focused (misogyny, sexism, racism, xenophobia, homophobia, etc.), and each specific manifestation of hate speech targets different vulnerable groups based on characteristics such as gender (misogyny, sexism), ethnicity, race, religion (xenophobia, racism, Islamophobia), sexual orientation (homophobia), and so on. Most automatic hate speech detection approaches cast the problem into a binary classification task without addressing either the topical focus or the target-oriented nature of hate speech. In this paper, we propose to tackle, for the first time, hate speech detection from a multi-target perspective. We leverage manually annotated datasets, to investigate the problem of transferring knowledge from different datasets with different topical focuses and targets. Our contribution is threefold: (1) we explore the ability of hate speech detection models to capture common properties from topic-generic datasets and transfer this knowledge to recognize specific manifestations of hate speech; (2) we experiment with the development of models to detect both topics (racism, xenophobia, sexism, misogyny) and hate speech targets, going beyond standard binary classification, to investigate how to detect hate speech at a finer level of granularity and how to transfer knowledge across different topics and targets; and (3) we study the impact of affective knowledge encoded in sentic computing resources (SenticNet, EmoSenticNet) and in semantically structured hate lexicons (HurtLex) in determining specific manifestations of hate speech. We experimented with different neural models including multitask approaches. Our study shows that: (1) training a model on a combination of several (training sets from several) topic-specific datasets is more effective than training a model on a topic-generic dataset; (2) the multi-task approach outperforms a single-task model when detecting both the hatefulness of a tweet and its topical focus in the context of a multi-label classification approach; and (3) the models incorporating EmoSenticNet emotions, the first level emotions of SenticNet, a blend of SenticNet and EmoSenticNet emotions or affective features based on Hurtlex, obtained the best results. Our results demonstrate that multitarget hate speech detection from existing datasets is feasible, which is a first step towards hate speech detection for a specific topic/target when dedicated annotated data are missing. Moreover, we prove that domain-independent affective knowledge, injected into our models, helps finer-grained hate speech detection.
|
|
Keyword:
Affective resources; Hate speech detection; Hate speech targets; Multi-task learning; Social media
|
|
URL: https://doi.org/10.1007/s12559-021-09862-5
http://hdl.handle.net/2318/1792620
http://link.springer.com/article/10.1007/s12559-021-09862-5
|
|
BASE
|
|
Hide details
|
|
|
| 7 |
Irony Detection in a Multilingual Context
|
|
|
|
In: ECIR ; https://hal.archives-ouvertes.fr/hal-02889008 ; ECIR, Apr 2020, online, Portugal (2020)
|
|
BASE
|
|
Show details
|
|
|
| 8 |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist
|
|
|
|
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ; 58th Annual Meeting of the Association for Computational Linguistics 2020 ; https://jeannicod.ccsd.cnrs.fr/ijn_03046501 ; 58th Annual Meeting of the Association for Computational Linguistics 2020, ACL: Association for Computational Linguistics, Jul 2020, Online, France. pp.4055-4066, ⟨10.18653/v1/2020.acl-main.373⟩ ; https://www.aclweb.org/anthology/2020.acl-main.373/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 9 |
He said “who’s gonna take care of your children when you are at ACL?”: Reported Sexist Acts are Not Sexist
|
|
|
|
In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03046097 ; Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 2020, Online, United States. pp.4055-4066, ⟨10.18653/v1/2020.acl-main.373⟩ ; https://acl2020.org/ (2020)
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Multilingual and Multitarget Hate Speech Detection in Tweets
|
|
|
|
In: Actes de la Conférence sur le Traitement Automatique des Langues Naturelles (TALN) PFIA 2019. Volume II : Articles courts ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019) ; https://hal.archives-ouvertes.fr/hal-02567777 ; Conférence sur le Traitement Automatique des Langues Naturelles (TALN - PFIA 2019), Jul 2019, Toulouse, France. pp.351-360 ; https://www.aclweb.org/anthology/2019.jeptalnrecital-court.21/ (2019)
|
|
BASE
|
|
Show details
|
|
|
| 13 |
IDAT@FIRE2019: Overview of the Track on Irony Detection in Arabic Tweets
|
|
|
|
BASE
|
|
Show details
|
|
|
| 14 |
Can we Predict Locations in Tweets? A Machine Learning Approach
|
|
|
|
In: ISSN: 0976-0962 ; International Journal of Computational Linguistics and Applications ; https://hal.archives-ouvertes.fr/hal-02901421 ; International Journal of Computational Linguistics and Applications, Alexander Gelbukh, 2018, 9, pp.0 (2018)
|
|
BASE
|
|
Show details
|
|
|
| 15 |
IRIT at e-Risk 2018
|
|
|
|
In: CLEF 2018 Working Notes ; 9th Conference and Labs of the Evaluation Forum, Living Labs (CLEF 2018) ; https://hal.archives-ouvertes.fr/hal-02290007 ; 9th Conference and Labs of the Evaluation Forum, Living Labs (CLEF 2018), Sep 2018, Avignon, France. pp.1-12 (2018)
|
|
BASE
|
|
Show details
|
|
|
| 16 |
IRIT at e-Risk
|
|
|
|
In: CLEF 2017 - CLEF 2017 Working Notes ; International Conference of the CLEF Association, CLEF 2017 Labs Working Notes (CLEF 2017) ; https://hal.archives-ouvertes.fr/hal-01912779 ; International Conference of the CLEF Association, CLEF 2017 Labs Working Notes (CLEF 2017), Sep 2017, Dublin, Ireland. pp. 1-7 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Predicting Locations in Tweets
|
|
|
|
In: CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02624131 ; CINCLing 2017 : 18th International Conference on Intelligent Text Processing and Computational Linguistics, Apr 2017, Budapest, Hungary (2017)
|
|
BASE
|
|
Show details
|
|
|
| 18 |
Exploring the Impact of Pragmatic Phenomena on Irony Detection in Tweets: A Multilingual Corpus Study
|
|
|
|
In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1 ; 15th Conference of the European Chapter of the Association for Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686475 ; 15th Conference of the European Chapter of the Association for Computational Linguistics, Apr 2017, Valencia, Spain. pp.262 - 272 (2017)
|
|
BASE
|
|
Show details
|
|
|
| 19 |
SOUKHRIA: Towards an Irony Detection System for Arabic in Social Media
|
|
|
|
In: 3rd International Conference on Arabic Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-01686504 ; 3rd International Conference on Arabic Computational Linguistics, Nov 2017, Dubaï, United Arab Emirates. pp.161 - 168, ⟨10.1016/j.procs.2017.10.105⟩ (2017)
|
|
BASE
|
|
Show details
|
|
|
| 20 |
INEX Tweet Contextualization Task: Evaluation, Results and Lesson Learned
|
|
|
|
In: ISSN: 0306-4573 ; Information Processing and Management ; https://hal-amu.archives-ouvertes.fr/hal-01479297 ; Information Processing and Management, Elsevier, 2016, 52 (5), pp.801-819. ⟨10.1016/j.ipm.2016.03.002⟩ (2016)
|
|
BASE
|
|
Show details
|
|
|
|
|
