[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
10.1145/3038912.3052591acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
research-article

Ex Machina: Personal Attacks Seen at Scale

Published: 03 April 2017 Publication History

Abstract

The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users.

References

[1]
J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. J. Mach. Learn. Res., 13:281--305, Feb. 2012.
[2]
M. Buhrmester, T. Kwang, and S. D. Gosling. Amazon's mechanical turk a new source of inexpensive, yet high-quality, data? Perspectives on psychological science, 6(1):3--5, 2011.
[3]
J. Cheng, C. Danescu-Niculescu-Mizil, and J. Leskovec. Antisocial behavior in online discussion communities. In ICWSM, 2015.
[4]
K. Dinakar, R. Reichart, and H. Lieberman. Modeling the detection of textual cyberbullying. The Social Mobile Web, 11:02, 2011.
[5]
M. Duggan. Online harassment. Pew Research Center, 2014.
[6]
Fundacion Barcelona Media (FBM). Caw 2.0 training datasets, 2009. http://caw2.barcelonamedia.org/.
[7]
I. Gagliardone, D. Gal, T. Alves, and G. Martinez. Countering online hate speech. UNESCO Publishing, 2015.
[8]
A. Halfaker. mwdiffs. https://github.com/mediawiki-utilities/python-mwdiffs.
[9]
A. F. Hayes and K. Krippendorff. Answering the call for a standard reliability measure for coding data. Communication methods and measures, 1(1):77--89, 2007.
[10]
Impermium. Detecting insults in social commentary dataset, 2012. https://www.kaggle.com/c/detecting-insults-in-social-commentary.
[11]
K. Krippendorff. Content analysis: An introduction to its methodology. Sage, 2004.
[12]
K. Krippendorff. Reliability in content analysis. Human communication research, 30(3):411--433, 2004.
[13]
I. Kwok and Y. Wang. Locate the hate: Detecting tweets against blacks. In AAAI, 2013.
[14]
M. J. Moore, T. Nakano, A. Enomoto, and T. Suda. Anonymity and roles associated with aggressive posts in an online forum. Computers in Human Behavior, 28(3):861--867, 2012.
[15]
C. Nobata, J. Tetreault, A. Thomas, Y. Mehdad, and Y. Chang. Abusive language detection in online user content. In WWW, 2016.
[16]
B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and trends in information retrieval, 2(1--2):1--135, 2008.
[17]
S. Pieschl, C. Kuhlmann, and T. Porsch. Beware of publicity! perceived distress of negative cyber incidents and implications for defining cyberbullying. Journal of School Violence, 14(1):111--132, 2015.
[18]
B. Plank, D. Hovy, and A. Søgaard. Learning part-of-speech taggers with inter-annotator agreement loss. In EACL, pages 742--751, 2014.
[19]
H. M. Saleem, K. P. Dillon, S. Benesch, and D. Ruths. A web of hate: Tackling hateful speech in online social spaces. In TA-COS, 2016.
[20]
A. Schrock and D. Boyd. Problematic youth interaction online: Solicitation, harassment, and cyberbullying. Computer-Mediated Communication in Personal Relationships, pages 368--398, 2011.
[21]
S. O. Sood, E. F. Churchill, and J. Antin. Automatic identification of personal insults on social news sites. Journal of the American Society for Information Science and Technology, 63(2):270--285, 2012.
[22]
N. Spirin and J. Han. Survey on web spam detection: principles and algorithms. ACM SIGKDD Explorations Newsletter, 13(2):50--64, 2012.
[23]
Support and Safety Team. Harassment Survey. Wikimedia Foundation, 2015. https://upload.wikimedia.org/wikipedia/commons/5/52/Harassment_Survey_2015_-_Results_Report.pdf.
[24]
J. R. Tetreault, E. Filatova, and M. Chodorow. Rethinking grammatical error annotation and evaluation with the amazon mechanical turk. In NAACL-HLT, 2010.
[25]
R. S. Tokunaga. Following you home from school: A critical review and synthesis of research on cyberbullying victimization. Computers in human behavior, 26(3):277--287, 2010.
[26]
M. A. Walker, J. E. F. Tree, P. Anand, R. Abbott, and J. King. A corpus for research on deliberation and debate. In LREC, pages 812--817, 2012.
[27]
W. Warner and J. Hirschberg. Detecting hate speech on the world wide web. In LSM, 2012.
[28]
Z. Waseem and D. Hovy. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of NAACL-HLT, pages 88--93, 2016.
[29]
D. Wiener. Negligent publication of statements posted on electronic bulletin boards: Is there any liability left after zeran. Santa Clara L. Rev., 39:905, 1998.
[30]
Wikimedia. Harassment consultation 2015. https://meta.wikimedia.org/wiki/Harassment_consultation_2015.
[31]
Wikimedia. Machine-learning tool to reduce toxic talk page interactions. https://meta.wikimedia.org/wiki/2015_Community_Wishlist_Survey/Bots_and_gadgets#Machine-learning_tool_to_reduce_toxic_talk_page_interactions.
[32]
Wikipedia. Help:Talk pages. https://www.mediawiki.org/wiki/Help:Talk_pages.
[33]
Wikipedia. Wikipedia:No personal attacks. https://en.wikipedia.org/wiki/Wikipedia:No_personal_attacks.
[34]
Wikipedia. Wikipedia:Revision_deletion. https://en.wikipedia.org/wiki/Wikipedia:Revision_deletion.
[35]
N. E. Willard. Cyberbullying and cyberthreats: Responding to the challenge of online social aggression, threats, and distress. Research Press, 2007.
[36]
E. Wulczyn, N. Thain, and L. Dixon. https://figshare.com/articles/Wikipedia_Detox_Data/4054689.
[37]
G. Xiang, B. Fan, L. Wang, J. Hong, and C. Rose. Detecting offensive tweets via topical feature discovery over a large scale twitter corpus. In CIKM, 2012.
[38]
J.-M. Xu, B. Burchfiel, X. Zhu, and A. Bellmore. An examination of regret in bullying tweets. In HLT-NAACL, pages 697--702, 2013.
[39]
M. L. Ybarra and K. J. Mitchell. Youth engaging in online harassment: Associations with caregiver--child relationships, internet use, and personal characteristics. Journal of adolescence, 27(3):319--336, 2004.
[40]
D. Yin, Z. Xue, L. Hong, B. D. Davison, A. Kontostathis, and L. Edwards. Detection of harassment on web 2.0. In WWW, 2009.

Cited By

View all
  • (2024)Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive ToolsSustainability10.3390/su1602078116:2(781)Online publication date: 16-Jan-2024
  • (2024)Development of an Automated Moderator for Deliberative EventsElectronics10.3390/electronics1303054413:3(544)Online publication date: 29-Jan-2024
  • (2024)The Unseen Targets of Hate: A Systematic Review of Hateful Communication DatasetsSocial Science Computer Review10.1177/08944393241258771Online publication date: 13-Jun-2024
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences
WWW '17: Proceedings of the 26th International Conference on World Wide Web
April 2017
1678 pages
ISBN:9781450349130

Sponsors

  • IW3C2: International World Wide Web Conference Committee

In-Cooperation

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 03 April 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. online discussions
  2. online harassment
  3. wikipedia

Qualifiers

  • Research-article

Conference

WWW '17
Sponsor:
  • IW3C2

Acceptance Rates

WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)183
  • Downloads (Last 6 weeks)15
Reflects downloads up to 19 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2024)Artificial Intelligence Alone Will Not Democratise Education: On Educational Inequality, Techno-Solutionism and Inclusive ToolsSustainability10.3390/su1602078116:2(781)Online publication date: 16-Jan-2024
  • (2024)Development of an Automated Moderator for Deliberative EventsElectronics10.3390/electronics1303054413:3(544)Online publication date: 29-Jan-2024
  • (2024)The Unseen Targets of Hate: A Systematic Review of Hateful Communication DatasetsSocial Science Computer Review10.1177/08944393241258771Online publication date: 13-Jun-2024
  • (2024)A Generative AI Powered Approach to Cyberbullying DetectionProceedings of the 2024 8th International Conference on Information System and Data Mining10.1145/3686397.3686407(57-63)Online publication date: 24-Jun-2024
  • (2024)“HOT” ChatGPT: The Promise of ChatGPT in Detecting and Discriminating Hateful, Offensive, and Toxic Comments on Social MediaACM Transactions on the Web10.1145/364382918:2(1-36)Online publication date: 2-Feb-2024
  • (2024)Algorithmic Arbitrariness in Content ModerationProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659036(2234-2253)Online publication date: 3-Jun-2024
  • (2024)Disentangling Perceptions of Offensiveness: Cultural and Moral CorrelatesProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3659021(2007-2021)Online publication date: 3-Jun-2024
  • (2024)Evaluation of Different Machine Learning and Deep Learning Techniques for Hate Speech DetectionProceedings of the 2024 ACM Southeast Conference10.1145/3603287.3651218(253-258)Online publication date: 18-Apr-2024
  • (2024)A Topology-Based Approach for Predicting Toxic Outcomes on Twitter and YouTubeIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339821911:5(4875-4885)Online publication date: Sep-2024
  • (2024)Cyberbullying Detection Using Bidirectional Encoder Representations from Transformers (BERT)2024 IEEE International Mediterranean Conference on Communications and Networking (MeditCom)10.1109/MeditCom61057.2024.10621093(257-262)Online publication date: 8-Jul-2024
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media