research-article

Topic enhanced word embedding for toxic content detection in Q&A sites

Authors:

Roy Ka-Wei LeeAuthors Info & Claims

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Pages 1064 - 1071

https://doi.org/10.1145/3341161.3345332

Published: 15 January 2020 Publication History

Abstract

Increasingly, users are adopting community question-and-answer (Q&A) sites to exchange information. Detecting and eliminating toxic and divisive content in these Q&A sites are paramount tasks to ensure a safe and constructive environment for the users. Insincere question, which is founded upon false premises, is one type of toxic content in Q&A sites. In this paper, we proposed a novel deep learning framework enhanced pre-trained word embeddings with topical information for insincere question classification. We evaluated our proposed framework on a large real-world dataset from Quora Q&A site and showed that the topically enhanced word embedding is able to achieve better results in toxic content classification. An empirical study was also conducted to analyze the topics of the insincere questions on Quora, and we found that topics on "religion", "gender" and "politics" has a higher proportion of insincere questions.

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Cited By

Joshi DDere GDesai PElkiwar SFuse PDongardive A(2024)VITVerse: Enabling Knowledge Networks for Solving Academic Queries2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714710(543-548)Online publication date: 3-Oct-2024
https://doi.org/10.1109/I-SMAC61858.2024.10714710
Zuo WRaman AMondragÓN RTyson G(2023)A First Look at User-Controlled Moderation on Web3 Social Media: The Case of Memo.cashProceedings of the 3rd International Workshop on Open Challenges in Online Social Networks10.1145/3599696.3612901(29-37)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3599696.3612901
Museng FJessica AWijaya NAnderies AIswanto I(2022)Systematic Literature Review: Toxic Comment Classification2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA)10.1109/ICITDA55840.2022.9971338(1-7)Online publication date: 4-Nov-2022
https://doi.org/10.1109/ICITDA55840.2022.9971338
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cover image ACM Conferences

ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

August 2019

1228 pages

ISBN:9781450368681

DOI:10.1145/3341161

Editors:
Francesca Spezzano
Boise State University
,
Wei Chen
Microsoft Research, China
,
Xiaokui Xiao
National University of Singapore, Singapore

Copyright © 2019 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 ACM 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]

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Published: 15 January 2020

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ASONAM '19: International Conference on Advances in Social Networks Analysis and Mining

August 27 - 30, 2019

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;

Overall Acceptance Rate 116 of 549 submissions, 21%

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Cited By

Joshi DDere GDesai PElkiwar SFuse PDongardive A(2024)VITVerse: Enabling Knowledge Networks for Solving Academic Queries2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)10.1109/I-SMAC61858.2024.10714710(543-548)Online publication date: 3-Oct-2024
https://doi.org/10.1109/I-SMAC61858.2024.10714710
Zuo WRaman AMondragÓN RTyson G(2023)A First Look at User-Controlled Moderation on Web3 Social Media: The Case of Memo.cashProceedings of the 3rd International Workshop on Open Challenges in Online Social Networks10.1145/3599696.3612901(29-37)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1145/3599696.3612901
Museng FJessica AWijaya NAnderies AIswanto I(2022)Systematic Literature Review: Toxic Comment Classification2022 IEEE 7th International Conference on Information Technology and Digital Applications (ICITDA)10.1109/ICITDA55840.2022.9971338(1-7)Online publication date: 4-Nov-2022
https://doi.org/10.1109/ICITDA55840.2022.9971338
Gontumukkala SGodavarthi YGonugunta BGupta DPalaniswamy S(2022)Quora Question Pairs Identification and Insincere Questions Classification2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT)10.1109/ICCCNT54827.2022.9984492(1-6)Online publication date: 3-Oct-2022
https://doi.org/10.1109/ICCCNT54827.2022.9984492
Singh IGoyal GChandel A(2022)AlexNet architecture based convolutional neural network for toxic comments classificationJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.06.00734:9(7547-7558)Online publication date: Oct-2022
https://doi.org/10.1016/j.jksuci.2022.06.007
Ghosh KBanerjee ABhattacharjee MChatterjee S(2021)Improved Twitter Sarcasm Detection by Addressing Imbalanced Class ProblemAdvances in Smart Communication Technology and Information Processing10.1007/978-981-15-9433-5_14(135-145)Online publication date: 16-Feb-2021
https://doi.org/10.1007/978-981-15-9433-5_14

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