research-article

Anatomy of Hate Speech Datasets: Composition Analysis and Cross-dataset Classification

Authors:

Samuel Guimarães,

Gabriel Kakizaki,

Fabricio Murai,

Julio C. S. Reis,

Fabrício BenevenutoAuthors Info & Claims

HT '23: Proceedings of the 34th ACM Conference on Hypertext and Social Media

Article No.: 33, Pages 1 - 11

https://doi.org/10.1145/3603163.3609158

Published: 05 September 2023 Publication History

Abstract

Manifestations of hate speech in different scenarios are increasingly frequent on social platforms. In this context, there is a large number of works that propose solutions for identifying this type of content in these environments. Most efforts to automatically detect hate speech follow the same process of supervised learning, using annotators to label a predefined set of messages, which are, in turn, used to train classifiers. However, annotators can create labels for different classification tasks, with divergent definitions of hate speech, binary or multi-label schemes, and various methodologies for collecting data. In this context, we examine the principal publicly available datasets for hate speech research. We investigate the types of hate speech (e.g., ethnicity, religion, sexual orientation) present in their composition, explore their content beyond the labels, and use cross-dataset classification to examine the use of the labeled data beyond its original work. Our results reveal interesting insights toward a better understanding of the hate speech phenomenon and improving its detection on social platforms.

Warning. This paper contains offensive words and tweet examples.

References

[1]

CJ Adams and Lucas Dixon. 2017. Better discussions with imperfect models -- The False Positive -- Medium. https://medium.com/the-false-positive/better-discussions-with-imperfect-models-91558235d442. (Accessed on 05/23/2018).

[2]

Fatimah Alkomah and Xiaogang Ma. 2022. A Literature Review of Textual Hate Speech Detection Methods and Datasets. Information 13, 6 (2022), 273.

[3]

Oscar Araque and Carlos A Iglesias. 2020. An approach for radicalization detection based on emotion signals and semantic similarity. IEEE Access 8 (2020), 17877--17891.

[4]

Pinkesh Badjatiya, Shashank Gupta, Manish Gupta, and Vasudeva Varma. 2017. Deep learning for hate speech detection in tweets. In Proceedings of the 26th International Conference on World Wide Web Companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 759--760. https://doi.org/10.1145/3041021.3054223

Digital Library

[5]

Paul M Barrett. 2020. Who moderates the social media giants. Technical Report. NYU Stern Center for Business and Human Rights.

[6]

Valerio Basile, Cristina Bosco, Elisabetta Fersini, Debora Nozza, Viviana Patti, Francisco Manuel Rangel Pardo, Paolo Rosso, and Manuela Sanguinetti. 2019. Semeval-2019 task 5: Multilingual detection of hate speech against immigrants and women in twitter. In Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, Minnesota, USA, 54--63. https://doi.org/10.18653/v1/S19-2007

[7]

Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency. Association for Computing Machinery, New York, NY, USA, 610--623. https://doi.org/10.1145/3442188.3445922

Digital Library

[8]

Tommaso Caselli, Valerio Basile, Jelena Mitrović, and Michael Granitzer. 2021. HateBERT: Retraining BERT for Abusive Language Detection in English. In Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021). Association for Computational Linguistics, Online, 17--25. https://doi.org/10.18653/v1/2021.woah-1.3

[9]

Bharathi Raja Chakravarthi, Ruba Priyadharshini, Thenmozhi Durairaj, John Philip McCrae, Paul Buitelaar, Prasanna Kumaresan, and Rahul Ponnusamy. 2022. Overview of the shared task on homophobia and transphobia detection in social media comments. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion. Association for Computational Linguistics, Dublin, Ireland, 369--377. https://doi.org/10.18653/v1/2022.ltedi-1.57

[10]

Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Emiliano De Cristofaro, Gianluca Stringhini, and Athena Vakali. 2017. Measuring# gamergate: a tale of hate, sexism, and bullying. In Proceedings of the 26th international conference on world wide web companion. International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1285--1290. https://doi.org/10.1145/3041021.3053890

Digital Library

[11]

Lu Cheng, Ahmadreza Mosallanezhad, Yasin N. Silva, Deborah L. Hall, and Huan Liu. 2022. Bias Mitigation for Toxicity Detection via Sequential Decisions. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR '22). Association for Computing Machinery, New York, NY, USA, 1750--1760. https://doi.org/10.1145/3477495.3531945

Digital Library

[12]

Thomas Davidson, Debasmita Bhattacharya, and Ingmar Weber. 2019. Racial Bias in Hate Speech and Abusive Language Detection Datasets. In Proceedings of the Third Workshop on Abusive Language Online. Association for Computational Linguistics, Florence, Italy, 25--35. https://doi.org/10.18653/v1/W19-3504

[13]

Thomas Davidson, Dana Warmsley, Michael Macy, and Ingmar Weber. 2017. Automated hate speech detection and the problem of offensive language. Proceedings of the international AAAI conference on web and social media 11, 1 (2017), 512--515. https://doi.org/10.1609/icwsm.v11i1.14955

[14]

Fabio Del Vigna, Andrea Cimino, Felice Dell'Orletta, Marinella Petrocchi, and Maurizio Tesconi. 2017. Hate me, hate me not: Hate speech detection on facebook. In Proceedings of the first Italian conference on cybersecurity (ITASEC17). CEUR-WS.org, Venice, Italy, 86--95.

[15]

Mai ElSherief, Shirin Nilizadeh, Dana Nguyen, Giovanni Vigna, and Elizabeth Belding. 2018. Peer to peer hate: Hate speech instigators and their targets. Proceedings of the Twelfth International AAAI Conference on Web and Social Media 12, 1 (2018), 52--61. https://doi.org/10.1609/icwsm.v12i1.15038

[16]

Mai ElSherief, Caleb Ziems, David Muchlinski, Vaishnavi Anupindi, Jordyn Seybolt, Munmun De Choudhury, and Diyi Yang. 2021. Latent Hatred: A Benchmark for Understanding Implicit Hate Speech. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 345--363. https://doi.org/10.18653/v1/2021.emnlp-main.29

[17]

Karen B Enes, Matheus Nunes, Fabricio Murai, and Gisele L Pappa. 2023. Evolving Node Embeddings for Dynamic Exploration of Network Topologies. In Advances in Artificial Intelligence--IBERAMIA 2022: 17th Ibero-American Conference on AI, Cartagena de Indias, Colombia, November 23-25, 2022, Proceedings. Springer International Publishing, Cham, 147--159. https://doi.org/10.1007/978-3-031-22419-5_13

Digital Library

[18]

Klint Finley. 2015. A brief history of the end of the comments. https://www.wired.com/2015/10/brief-history-of-the-demise-of-the-comments-timeline/.

[19]

Paula Fortuna and Sérgio Nunes. 2018. A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR) 51, 4 (2018), 85.

Digital Library

[20]

Paula Fortuna, Juan Soler, and Leo Wanner. 2020. Toxic, hateful, offensive or abusive? what are we really classifying? an empirical analysis of hate speech datasets. In Proceedings of the 12th language resources and evaluation conference. European Language Resources Association, Marseille, France, 6786--6794. https://aclanthology.org/2020.lrec-1.838

[21]

Antigoni Founta, Constantinos Djouvas, Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn, Gianluca Stringhini, Athena Vakali, Michael Sirivianos, and Nicolas Kourtellis. 2018. Large scale crowdsourcing and characterization of twitter abusive behavior. Proceedings of the international AAAI conference on web and social media 12, 1 (2018), 491--500. https://doi.org/10.1609/icwsm.v12i1.14991

[22]

Simona Frenda, Bilal Ghanem, Manuel Montes-y Gómez, and Paolo Rosso. 2019. Online hate speech against women: Automatic identification of misogyny and sexism on twitter. Journal of Intelligent & Fuzzy Systems 36, 5 (2019), 4743--4752.

[23]

Iginio Gagliardone, Danit Gal, Thiago Alves, and Gabriela Martinez. 2015. Countering online hate speech. Unesco Publishing, Online.

[24]

Iginio Gagliardone, Danit Gal, Thiago Alves, and Gabriela Martinez. 2015. Countering online Hate Speech. UNESCO, Online.

[25]

Mayur Gaikwad, Swati Ahirrao, Ketan Kotecha, and Ajith Abraham. 2022. Multi-Ideology Multi-Class Extremism Classification Using Deep Learning Techniques. IEEE Access 10 (2022), 104829--104843.

[26]

Björn Gambäck and Utpal Kumar Sikdar. 2017. Using convolutional neural networks to classify hate-speech. In Proceedings of the First Workshop on Abusive Language Online. Association for Computational Linguistics, Vancouver, BC, Canada, 85--90. https://doi.org/10.18653/v1/W17-3013

[27]

SK Gargee, Pranav Bhargav Gopinath, Shridhar Reddy SR Kancharla, CR Anand, and Anoop S Babu. 2022. Analyzing and Addressing the Difference in Toxicity Prediction Between Different Comments with Same Semantic Meaning in Google's Perspective API. In ICT Systems and Sustainability: Proceedings of ICT4SD 2022. Springer, Singapore, 455--464.

[28]

Yotam Gil, Yoav Chai, Or Gorodissky, and Jonathan Berant. 2019. White-to-Black: Efficient Distillation of Black-Box Adversarial Attacks. In Proceedings of NAACL-HLT. Association for Computational Linguistics, Minneapolis, Minnesota, 1373--1379. https://doi.org/10.18653/v1/N19-1139

[29]

Bing He, Caleb Ziems, Sandeep Soni, Naren Ramakrishnan, Diyi Yang, and Srijan Kumar. 2021. Racism is a virus: Anti-Asian hate and counterspeech in social media during the COVID-19 crisis. In Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (Virtual Event, Netherlands). Association for Computing Machinery, New York, NY, USA, 90--94. https://doi.org/10.1145/3487351.3488324

Digital Library

[30]

Edwin Jain, Stephan Brown, Jeffery Chen, Erin Neaton, Mohammad Baidas, Ziqian Dong, Huanying Gu, and Nabi Sertac Artan. 2018. Adversarial text generation for google's perspective api. In 2018 international conference on computational science and computational intelligence (CSCI). IEEE Computer Society, Los Alamitos, CA, USA, 1136--1141. https://doi.org/10.1109/CSCI46756.2018.00220

[31]

Ben Knight. 2018. Germany implements new internet hate speech crackdown. https://www.dw.com/en/germany-implements-new-internet-hate-speech-crackdown/a-41991590. (Accessed on 02/20/2023).

[32]

Denise Law. 2017. Help us shape the future of comments on economist.com | The Economist. https://medium.economist.com/help-us-shape-the-future-of-comments-on-economist-com-fa86eeafb0ce. (Accessed on 03/20/2023).

[33]

Ioanna K Lekea and Panagiotis Karampelas. 2018. Detecting Hate Speech Within the Terrorist Argument: A Greek Case. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Computer Society, Los Alamitos, CA, USA, 1084--1091. https://doi.org/10.1109/ASONAM.2018.8508270

[34]

Lucas Lima, Julio CS Reis, Philipe Melo, Fabricio Murai, Leandro Araujo, Pantelis Vikatos, and Fabricio Benevenuto. 2018. Inside the right-leaning echo chambers: Characterizing gab, an unmoderated social system. In 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Computer Society, Los Alamitos, CA, USA, 515--522. https://doi.org/10.1109/ASONAM.2018.8508809

[35]

Lucas Lima, Julio C. S. Reis, Philipe Melo, Fabrício Murai, and Fabrício Benevenuto. 2020. Characterizing (Un)moderated Textual Data in Social Systems. In 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE Computer Society, Los Alamitos, CA, USA, 430--434. https://doi.org/10.1109/ASONAM49781.2020.9381327

Digital Library

[36]

Thomas Mandl, Sandip Modha, Anand Kumar M, and Bharathi Raja Chakravarthi. 2020. Overview of the hasoc track at fire 2020: Hate speech and offensive language identification in tamil, malayalam, hindi, english and german. In Forum for information retrieval evaluation. Association for Computing Machinery, New York, NY, USA, 29--32. https://doi.org/10.1145/3441501.3441517

Digital Library

[37]

Thomas Mandl, Sandip Modha, Prasenjit Majumder, Daksh Patel, Mohana Dave, Chintak Mandlia, and Aditya Patel. 2019. Overview of the hasoc track at fire 2019: Hate speech and offensive content identification in indo-european languages. In Proceedings of the 11th forum for information retrieval evaluation. Association for Computing Machinery, New York, NY, USA, 14--17. https://doi.org/10.1145/3368567.3368584

Digital Library

[38]

Binny Mathew, Ritam Dutt, Pawan Goyal, and Animesh Mukherjee. 2019. Spread of hate speech in online social media. In Proceedings of the 10th ACM conference on web science. Association for Computing Machinery, New York, NY, USA, 173--182. https://doi.org/10.1145/3292522.3326034

Digital Library

[39]

Mainack Mondal, Leandro Araújo Silva, and Fabrício Benevenuto. 2017. A measurement study of hate speech in social media. In Proceedings of the 28th ACM Conference on Hypertext and Social Media. ACM, Association for Computing Machinery, New York, NY, USA, 85--94. https://doi.org/10.1145/3078714.3078723

Digital Library

[40]

Mainack Mondal, Leandro Araújo Silva, Denzil Correa, and Fabrício Benevenuto. 2018. Characterizing usage of explicit hate expressions in social media. New Review of Hypermedia and Multimedia 24, 2 (2018), 110--130.

[41]

Marzieh Mozafari, Reza Farahbakhsh, and Noel Crespi. 2020. A BERT-based transfer learning approach for hate speech detection in online social media. In Complex Networks and Their Applications VIII: Volume 1 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019 8. Springer, Springer International Publishing, Cham, 928--940. https://doi.org/10.1007/978-3-030-36687-2_77

[42]

UN Office on Genocide Prevention and the Responsibility to Protect and UNESCO. 2021. Addressing Hate Speech on Social Media: Contemporary Challenges. UNESCO, Online.

[43]

John Pavlopoulos, Jeffrey Sorensen, Lucas Dixon, Nithum Thain, and Ion Androutsopoulos. 2020. Toxicity Detection: Does Context Really Matter?. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Online, 4296--4305. https://doi.org/10.18653/v1/2020.acl-main.396

[44]

Fabio Poletto, Valerio Basile, Manuela Sanguinetti, Cristina Bosco, and Viviana Patti. 2021. Resources and benchmark corpora for hate speech detection: a systematic review. Language Resources and Evaluation 55 (2021), 477--523.

Digital Library

[45]

Manoel Ribeiro, Pedro Calais, Yuri Santos, Virgílio Almeida, and Wagner Meira Jr. 2018. Characterizing and detecting hateful users on twitter. Proceedings of the International AAAI Conference on Web and Social Media 12, 1 (2018), 676--679. https://doi.org/10.1609/icwsm.v12i1.15057

[46]

Bernhard Rieder and Yarden Skop. 2021. The fabrics of machine moderation: Studying the technical, normative, and organizational structure of Perspective API. Big Data & Society 8, 2 (2021), 20539517211046181.

[47]

Björn Ross, Michael Rist, Guillermo Carbonell, Ben Cabrera, Nils Kurowsky, and Michael Wojatzki. 2016. Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis. In NLP4CMC. Ruhr-Universitat Bochum, Germany, 6--9.

[48]

Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-gyo Jung, Jisun An, Haewoon Kwak, and Bernard J Jansen. 2018. Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media. Proceedings of the International AAAI Conference on Web and Social Media 12, 1 (2018), 330--339. https://doi.org/10.1609/icwsm.v12i1.15028

[49]

Maarten Sap, Swabha Swayamdipta, Laura Vianna, Xuhui Zhou, Yejin Choi, and Noah A Smith. 2022. Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Seattle, United States, 5884--5906. https://doi.org/10.18653/v1/2022.naacl-main.431

[50]

Leandro Araújo Silva, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. 2016. Analyzing the Targets of Hate in Online Social Media. Proceedings of the International AAAI Conference on Web and Social Media 10 (2016), 687--690. https://doi.org/10.1609/icwsm.v10i1.14811

[51]

The New York Times. 2016. The Times is Partnering with Jigsaw to Expand Comment Capabilities. https://www.nytco.com/press/the-times-is-partnering-with-jigsaw-to-expand-comment-capabilities/. (Accessed on 03/20/2023).

[52]

Francielle Vargas, Fabiana Rodrigues de Góes, Isabelle Carvalho, Fabrício Benevenuto, and Thiago Pardo. 2021. Contextual-lexicon approach for abusive language detection. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021). INCOMA Ltd., Online, 1438--1447. https://aclanthology.org/2021.ranlp-1.161

[53]

Bertie Vidgen and Leon Derczynski. 2020. Directions in abusive language training data, a systematic review: Garbage in, garbage out. Plos one 15, 12 (2020), e0243300.

[54]

Nishant Vishwamitra, Ruijia Roger Hu, Feng Luo, Long Cheng, Matthew Costello, and Yin Yang. 2020. On analyzing covid-19-related hate speech using bert attention. In Proceedings of the 2020 19th IEEE International Conference on Machine Learning and Applications. IEEE Computer Society, Los Alamitos, CA, USA, 669--676.

[55]

Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017. Understanding Abuse: A Typology of Abusive Language Detection Subtasks. In Proceedings of the First Workshop on Abusive Language Online. Association for Computational Linguistics, Vancouver, BC, Canada, 78--84. https://doi.org/10.18653/v1/W17-3012

[56]

Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL student research workshop. Association for Computational Linguistics, San Diego, California, 88--93. https://doi.org/10.18653/v1/N16-2013

[57]

Michael Wiegand, Melanie Siegel, and Josef Ruppenhofer. 2018. Overview of the GermEval 2018 Shared Task on the Identification of Offensive Language. In 14th Conference on Natural Language Processing KONVENS 2018. Verlag der Österreichischen Akademie der Wissenschaften, Wien, 1--10. https://epub.oeaw.ac.at/?arp=0x003a10d2

[58]

Savvas Zannettou, Barry Bradlyn, Emiliano De Cristofaro, Haewoon Kwak, Michael Sirivianos, Gianluca Stringini, and Jeremy Blackburn. 2018. What is Gab: A Bastion of Free Speech or an Alt-Right Echo Chamber. In Companion Proceedings of the The Web Conference 2018 (Lyon, France) (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1007--1014. https://doi.org/10.1145/3184558.3191531

Digital Library

[59]

Savvas Zannettou, Joel Finkelstein, Barry Bradlyn, and Jeremy Blackburn. 2020. A quantitative approach to understanding online antisemitism. Proceedings of the International AAAI conference on Web and Social Media 14 (2020), 786--797. https://doi.org/10.1609/icwsm.v14i1.7343

[60]

Xuhui Zhou, Maarten Sap, Swabha Swayamdipta, Yejin Choi, and Noah Smith. 2021. Challenges in Automated Debiasing for Toxic Language Detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume. Association for Computational Linguistics, Online, 3143--3155. https://doi.org/10.18653/v1/2021.eacl-main.274

Cited By

Yuan LRizoiu M(2025)Generalizing Hate Speech Detection Using Multi-Task LearningComputer Speech and Language10.1016/j.csl.2024.10169089:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.csl.2024.101690
Tiderman LMercedes JRomanoschi FMurai F(2023)Towards Detecting Cascades of Biased Medical Claims on Twitter2023 IEEE MIT Undergraduate Research Technology Conference (URTC)10.1109/URTC60662.2023.10534948(1-5)Online publication date: 6-Oct-2023
https://doi.org/10.1109/URTC60662.2023.10534948

Index Terms

Anatomy of Hate Speech Datasets: Composition Analysis and Cross-dataset Classification
1. Applied computing
  1. Law, social and behavioral sciences
    1. Sociology
2. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis

Recommendations

Spread of Hate Speech in Online Social Media
WebSci '19: Proceedings of the 10th ACM Conference on Web Science

Hate speech is considered to be one of the major issues currently plaguing the online social media. With online hate speech culminating in gruesome scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the ...
A Measurement Study of Hate Speech in Social Media
HT '17: Proceedings of the 28th ACM Conference on Hypertext and Social Media

Social media platforms provide an inexpensive communication medium that allows anyone to quickly reach millions of users. Consequently, in these platforms anyone can publish content and anyone interested in the content can obtain it, representing a ...
Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures
Abstract
Automatic identification of hateful and abusive content is vital in combating the spread of harmful online content and its damaging effects. Most existing works evaluate models by examining the generalization error on train–test splits on hate ...
Highlights
- Using multi-task learning to improve hate speech detection generalization in new datasets.
- New Twitter hate speech dataset of American political public figures.
- Case study on targets and topic of hate and abuse in speech of online ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

HT '23: Proceedings of the 34th ACM Conference on Hypertext and Social Media

September 2023

334 pages

ISBN:9798400702327

DOI:10.1145/3603163

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Funding Sources

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Fundação de Amparo à Pesquisa do Estado de Minas Gerais
Fundação de Amparo à Pesquisa do Estado de São Paulo

Conference

HT '23

Sponsor:

SIGWEB

HT '23: 34th ACM Conference on Hypertext and Social Media

September 4 - 8, 2023

Rome, Italy

Acceptance Rates

Overall Acceptance Rate 378 of 1,158 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
200
Total Downloads

Downloads (Last 12 months)99
Downloads (Last 6 weeks)10

Reflects downloads up to 23 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yuan LRizoiu M(2025)Generalizing Hate Speech Detection Using Multi-Task LearningComputer Speech and Language10.1016/j.csl.2024.10169089:COnline publication date: 1-Jan-2025
https://dl.acm.org/doi/10.1016/j.csl.2024.101690
Tiderman LMercedes JRomanoschi FMurai F(2023)Towards Detecting Cascades of Biased Medical Claims on Twitter2023 IEEE MIT Undergraduate Research Technology Conference (URTC)10.1109/URTC60662.2023.10534948(1-5)Online publication date: 6-Oct-2023
https://doi.org/10.1109/URTC60662.2023.10534948

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten