Human-centered neural reasoning for subjective content processing: Hate speech, emotions, and humor
Open accessAbstract
Some tasks in content processing, e.g., natural language processing (NLP), like hate or offensive speech and emotional or funny text detection, are subjective by nature. Each human may perceive some content individually. The existing reasoning methods commonly rely on agreed output values, the same for all recipients. We propose fundamentally different – personalized solutions applicable to any subjective NLP task. Our five new deep learning models take into account not only the textual content but also the opinions and beliefs of a given person. They differ in their approaches to learning Human Bias (HuBi) and fusion with content (text) representation. The experiments were carried out on 14 tasks related to offensive, emotional, and humorous texts. Our personalized HuBi methods radically outperformed the generalized ones for all NLP problems. Personalization also has a greater impact on reasoning quality than commonly explored pre-trained and fine-tuned language models. We discovered a high correlation between human bias calculated using our dedicated formula and that learned by the model. Multi-task solutions achieved better outcomes than single-task architectures. Human and word embeddings also provided additional insights.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
It is difficult to identify universal ground truth in tasks such as predicting emotions or offensiveness of text, especially in personalized context.
“At the same time, it is difficult to identify universal ground truth in tasks such as predicting emotions or offensiveness of text, especially in the personalized context [20, 21, 22].”
From ChatGPT: Jack of all trades, master of none (2023) · cites this paper as [22]
Cites
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Enriching Word Vectors with Subword Information
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00051 · Cited by 9,645 · see citing works
Measuring nominal scale agreement among many raters.
Fleiss, J. L. (1971) · Psychological Bulletin · doi:10.1037/h0031619 · Cited by 8,733 · see citing works
Neural Collaborative Filtering
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052569 · Cited by 7,066 · see citing works
A Multiple Comparison Procedure for Comparing Several Treatments with a Control
Dunnett, C. W. (1955) · Journal of the American Statistical Association · doi:10.1080/01621459.1955.10501294 · Cited by 5,930 · see citing works
Proceedings of the 19th International Joint Conference on Artificial Intelligence
Pujol, J. M., Delgado, J., Ramon Sangüesa, A., & Flache, A. (2005) · International Joint Conference on Artificial Intelligence · Cited by 5,771 · see citing works
Unsupervised Cross-lingual Representation Learning at Scale
Conneau, A., Khandelwal, K., Goyal, N., Chaudhary, V., Wenzek, G., Guzmán, F., Grave, E., Ott, M., Zettlemoyer, L., & Stoyanov, V. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.747 · Cited by 5,637 · see citing works
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222 · see citing works
A GENERAL PSYCHOEVOLUTIONARY THEORY OF EMOTION
Plutchik, R. (1980) · Elsevier eBooks · doi:10.1016/b978-0-12-558701-3.50007-7 · Cited by 2,240 · see citing works
2011 International Joint Conference on Neural Networks
(2011) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2010.2102870 · Cited by 1,772 · see citing works
Text Classification Algorithms: A Survey
Kowsari, K., Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L. E., & Brown, D. E. (2019) · Inf. · doi:10.3390/info10040150 · Cited by 1,605 · see citing works
A Survey on Automatic Detection of Hate Speech in Text
Fortuna, P., & Nunes, S. (2019) · ACM Computing Surveys · doi:10.1145/3232676 · Cited by 1,241 · see citing works
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
He, P., Liu, X., Gao, J., & Chen, W. (2021) · International Conference on Learning Representations · Cited by 1,116 · see citing works
A comprehensive survey on sentiment analysis: Approaches, challenges and trends
Birjali, M., Kasri, M., & Beni‐Hssane, A. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107134 · Cited by 992 · see citing works
Computing Krippendorff's Alpha-Reliability
Krippendorff, K. (2011) · ScholarlyCommons (University of Pennsylvania) · Cited by 809 · see citing works
Language-agnostic BERT Sentence Embedding
Feng, F., Yang, Y., Cer, D., Arivazhagan, N., & Wang, W. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2022.acl-long.62 · Cited by 758 · see citing works
MPNet: Masked and Permuted Pre-training for Language UnderstandingPreprint
Song, K., Tan, X., Qin, T., Lu, J., & Liu, T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.09297 · Cited by 713 · see citing works
Transformer models for text-based emotion detection: a review of BERT-based approaches
Acheampong, F. A., Nunoo‐Mensah, H., & Chen, W. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-09958-2 · Cited by 686 · see citing works
Perceptual image quality assessment: a survey
Zhai, G., & Min, X. (2020) · Science China Information Sciences · doi:10.1007/s11432-019-2757-1 · Cited by 662 · see citing works
Proceedings of the 25th International Conference on World Wide Web
Jacqueline Bourdeau, B., Hendler, J., Nkambou, R., Horrocks, I. R., & Zhao, B. Y. (2016) · Cited by 635 · see citing works
Proceedings of the 25th ACM international conference on Multimedia
(2017) · doi:10.1145/3123266 · Cited by 619 · see citing works
Learning Semantic Representations of Users and Products for Document Level Sentiment Classification
Tang, D., Qin, B., & Liu, T. (2015) · doi:10.3115/v1/p15-1098 · Cited by 378 · see citing works
Introduction to multivariate regression analysis.
Alexopoulos, E. C. (2010) · Europe PMC (PubMed Central) · Cited by 366 · see citing works
Hate speech review in the context of online social networks
Chetty, N., & Alathur, S. (2018) · Aggression and Violent Behavior · doi:10.1016/j.avb.2018.05.003 · Cited by 357 · see citing works
Neural Sentiment Classification with User and Product Attention
Chen, H., Sun, M., Tu, C., Lin, Y., & Liu, Z. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1171 · Cited by 349 · see citing works
Study of Subjective and Objective Quality Assessment of Audio-Visual Signals
Min, X., Zhai, G., Zhou, J., Farias, M. C. Q., & Bovik, A. C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2988148 · Cited by 328 · see citing works
Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., Korthikanti, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2201.11990 · Cited by 286 · see citing works
Modelling Context with User Embeddings for Sarcasm Detection in Social Media
Amir, S., Wallace, B., Lyu, H., Carvalho, P., & Silva, M. J. (2016) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1017 · Cited by 280 · see citing works
A survey of state-of-the-art approaches for emotion recognition in text
Alswaidan, N., & Menaï, M. E. B. (2020) · Knowledge and Information Systems · doi:10.1007/s10115-020-01449-0 · Cited by 279 · see citing works
Ex Machina: Personal Attacks Seen at ScalePreprint
Wulczyn, E., Thain, N., & Dixon, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.08914 · Cited by 260 · see citing works
Characterizing context-aware recommender systems: A systematic literature review
Villegas, N. M., Sánchez, C. J. M., Díaz-Cely, J., & Tamura, G. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.11.003 · Cited by 217 · see citing works
Language-agnostic BERT Sentence EmbeddingPreprint
Feng, F., Yang, Y., Cer, D. M., et al. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.01852 · Cited by 194 · see citing works
Who is the "Human" in Human-Centered Machine Learning
Chancellor, S., Baumer, E., & de Choudhury, M. (2019) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3359249 · Cited by 187 · see citing works
A survey on group recommender systems
Dara, S., Chowdary, C. R., & Kumar, C. (2019) · Journal of Intelligent Information Systems · doi:10.1007/s10844-018-0542-3 · Cited by 159 · see citing works
Text classification: A least square support vector machine approach
Mitra, V., Wang, C.-J., & Banerjee, S. (2006) · Applied Soft Computing · doi:10.1016/j.asoc.2006.04.002 · Cited by 142 · see citing works
A Review of Recent Deep Learning Approaches in Human-Centered Machine Learning
Kaluarachchi, T. I., Reis, A., & Nanayakkara, S. C. (2021) · Sensors · doi:10.3390/s21072514 · Cited by 132 · see citing works
Learning User and Product Distributed Representations Using a Sequence Model for Sentiment Analysis
Chen, T., Xu, R., He, Y., Xia, Y., & Wang, X. (2016) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2016.2572539 · Cited by 122 · see citing works
Challenges in Automated Debiasing for Toxic Language Detection
Zhou, X., Sap, M., Swayamdipta, S., Choi, Y., & Smith, N. A. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.274 · Cited by 116 · see citing works
Predicting Personalized Emotion Perceptions of Social Images
Zhao, S., Yao, H., Gao, Y., Ji, R., Xie, W., Jiang, X., & Chua, T. (2016) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/2964284.2964289 · Cited by 114 · see citing works
Offensive, aggressive, and hate speech analysis: From data-centric to human-centered approach
Kocoń, J., Figas, A., Gruza, M., Puchalska, D., Kajdanowicz, T., & Kazienko, P. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102643 · Cited by 103 · see citing works
Self-citationTwo Contrasting Data Annotation Paradigms for Subjective NLP Tasks
Röttger, P., Vidgen, B., Hovy, D., & Pierrehumbert, J. B. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.13 · Cited by 97 · see citing works
Personalized Image Aesthetics Assessment with Rich Attributes
Yang, Y., Xu, L., Li, L., Qie, N., Li, Y., Zhang, P., & Guo, Y. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01924 · Cited by 97 · see citing works
Lascarides, A., Gardent, C., & Nivre, J. (2009) · Cited by 96 · see citing works
Wu, Z., Dai, X., Yin, C., Huang, S., & Chen, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.12054 · Cited by 96 · see citing works
Estimating the Uncertainty of Average F1 Scores
Zhang, D., Wang, Y.-X., & Zhao, X. (2015) · ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR) · doi:10.1145/2808194.2809488 · Cited by 89 · see citing works
Multi-Modal Sarcasm Detection and Humor Classification in Code-Mixed Conversations
Bedi, M., Kumar, S., Akhtar, M. S., & Chakraborty, T. (2021) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2021.3083522 · Cited by 89 · see citing works
Identifying and Measuring Annotator Bias Based on Annotators’ Demographic Characteristics
Kuwatly, H. A., Wich, M., & Groh, G. (2020) · Workshop on Online Abuse and Harms (WOAH) · doi:10.18653/v1/2020.alw-1.21 · Cited by 88 · see citing works
Personalized Classifier for Food Image Recognition
Horiguchi, S., Amano, S., Ogawa, M., & Aizawa, K. (2018) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2018.2814339 · Cited by 86 · see citing works
We Need to Consider Disagreement in Evaluation
Basile, V., Fell, M., Fornaciari, T., Hovy, D., Paun, S., Plank, B., Poesio, M., & Uma, A. (2021) · doi:10.18653/v1/2021.bppf-1.3 · Cited by 80 · see citing works
Green, J. R., MacDonald, R. L., Jiang, P.-P., Cattiau, J., Heywood, R., Cave, R., Seaver, K., Ladewig, M. A., Tobin, J., Brenner, M. P., et al. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-1384 · Cited by 74 · see citing works
Chou, H.-C., & Lee, C.-C. (2019) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2019.8682170 · Cited by 68 · see citing works
A Survey of Figurative Language and Its Computational Detection in Online Social Networks
Abulaish, M., Kamal, A., & Zaki, M. J. (2020) · ACM Transactions on the Web · doi:10.1145/3375547 · Cited by 63 · see citing works
Cost-Effective Active Learning from Diverse Labelers
Huang, S.-J., Chen, J.-L., Mu, X., & Zhou, Z. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/261 · Cited by 62 · see citing works
Radun, J. E., Leisti, T., Häkkinen, J. P., Ojanen, H. J., Olives, J.-L., Vuori, T., & Nyman, G. (2008) · ACM Transactions on Applied Perception · doi:10.1145/1278760.1278762 · Cited by 55 · see citing works
Personalization of end-to-end speech recognition on mobile devices for named entities
Sim, K. C., Beaufays, F., Benard, A., Guliani, D., Kabel, A., Khare, N., Lucassen, T., Zadrazil, P., Zhang, H., Johnson, L., et al. (2019) · 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · doi:10.1109/asru46091.2019.9003775 · Cited by 54 · see citing works
Humor norms for 4,997 English words
Engelthaler, T., & Hills, T. T. (2017) · Behavior Research Methods · doi:10.3758/s13428-017-0930-6 · Cited by 53 · see citing works
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Bansal, M., & Villavicencio, A. (2019) · doi:10.18653/v1/k19-1 · Cited by 51 · see citing works
Human Centered NLP with User-Factor Adaptation
Lynn, V., Son, Y., Kulkarni, V., Balasubramanian, N., & Schwartz, H. A. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1119 · Cited by 47 · see citing works
Proceedings of the Fourth Workshop on Online Abuse and Harms
(2020) · doi:10.18653/v1/2020.alw-1 · Cited by 38 · see citing works
Kocoń, J., Gruza, M., Bielaniewicz, J., Grimling, D., Kanclerz, K., Miłkowski, P., & Kazienko, P. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00140 · Cited by 38 · see citing works
Self-citationZiems, C., Vigfússon, Ý., & Morstatter, F. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7345 · Cited by 37 · see citing works
Personal Bias in Prediction of Emotions Elicited by Textual Opinions
Miłkowski, P., Gruza, M., Kanclerz, K., Kazienko, P., Grimling, D., & Kocoń, J. (2021) · doi:10.18653/v1/2021.acl-srw.26 · Cited by 34 · see citing works
Self-citationControversy and Conformity: from Generalized to Personalized Aggressiveness Detection
Kanclerz, K., Figas, A., Gruza, M., Kajdanowicz, T. J., Kocoń, J., Puchalska, D., & Kazienko, P. (2021) · doi:10.18653/v1/2021.acl-long.460 · Cited by 32 · see citing works
Self-citationGraph Domain Adversarial Transfer Network for Cross-Domain Sentiment Classification
Tang, H., Mi, Y., Xue, F., & Cao, Y. (2021) · IEEE Access · doi:10.1109/access.2021.3061139 · Cited by 29 · see citing works
Computational Science – ICCS 2021
Paszyński, M. R., Kranzlmüller, D. A., Krzhizhanovskaya, V. V., Dongarra, J. J., & Sloot, P. M. A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77964-1 · Cited by 29 · see citing works
A Knowledge-Based Deep Learning Architecture for Aspect-Based Sentiment Analysis
Alexandridis, G., Aliprantis, J., Michalakis, K., Korovesis, K., Tsantilas, P., & Caridakis, G. (2021) · International Journal of Neural Systems · doi:10.1142/s0129065721500465 · Cited by 27 · see citing works
Neuro-Symbolic Models for Sentiment Analysis
Kocoń, J., Baran, J., Gruza, M., Janz, A., Kajstura, M., Kazienko, P., Korczyński, W., Miłkowski, P., Piasecki, M., & Szołomicka, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08754-7_69 · Cited by 25 · see citing works
Self-citationIt’s the End of the Gold Standard as We Know It
Basile, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77091-4_26 · Cited by 22 · see citing works
Modeling Social Norms Evolution for Personalized Sentiment Classification
Lin, G., Boni, M. A., & Wang, H. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1081 · Cited by 21 · see citing works
Multitask Personalized Recognition of Emotions Evoked by Textual Content
Miłkowski, P., Saganowski, S., Gruza, M., Kazienko, P., Piasecki, M., & Kocoń, J. (2022) · IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomworkshops53856.2022.9767502 · Cited by 20 · see citing works
Self-citationMireshghallah, F., Shrivastava, V., Shokouhi, M., Berg-Kirkpatrick, T., Sim, R., & Dimitriadis, D. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.252 · Cited by 20 · see citing works
Shahabinejad, M., Wang, Y., Yu, Y., Tang, J., & Li, J. (2021) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg52635.2021.9666982 · Cited by 19 · see citing works
Debiasing Methods in Natural Language Understanding Make Bias More Accessible
Mendelson, M., & Belinkov, Y. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.116 · Cited by 16 · see citing works
Clustered Model Adaption for Personalized Sentiment Analysis
Lin, G., Haines, B., & Wang, H. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052693 · Cited by 16 · see citing works
Human-Centered Concept Explanations for Neural NetworksPreprint
Yeh, C., Kim, B., & Ravikumar, P. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2202.12451 · Cited by 12 · see citing works
Humor in Word Embeddings: Cockamamie Gobbledegook for NincompoopsPreprint
Gultchin, L., Patterson, G., Baym, N. K., Swinger, N., & Kalai, A. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02783 · Cited by 9 · see citing works
Personalized Frame-Level Facial Expression Recognition in Video
Savchenko, A. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-09037-0_37 · Cited by 7 · see citing works
PU-GEN: Enhancing generative commonsense reasoning for language models with human-centered knowledge
Seo, J., Oh, D., Eo, S., Park, C., Yang, K., Moon, H., Park, K., & Lim, H. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.109861 · Cited by 6 · see citing works
What Do You Mean I'm Funny? Personalizing the Joke Skill of a Voice-Controlled Virtual AssistantPreprint
Mottini, A., & Chowdhury, A. R. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03234 · Cited by 5 · see citing works
A Personalized Sentiment Model with Textual and Contextual Information
Guo, S., Höhn, S., & Schommer, C. (2019) · Conference on Computational Natural Language Learning · doi:10.18653/v1/k19-1093 · Cited by 4 · see citing works
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?
Fornaciari, T., Bianchi, F., Nozza, D., & Hovy, D. (2021) · Archivio istituzionale della Ricerca - Bocconi · Cited by 4 · see citing works
Hate Speech Detection on Social Media Using Graph Convolutional Networks
Nagar, S., Gupta, S., Bahushruth, C. S., Barbhuiya, F. A., & Dey, K. (2022) · Studies in computational intelligence · doi:10.1007/978-3-030-93413-2_1 · Cited by 4 · see citing works
Like-tasted user groups to predict ratings in recommender systems
Jaffali, S., Jamoussi, S., Smaı̈li, K., & Hamadou, A. B. (2020) · Social Network Analysis and Mining · doi:10.1007/s13278-020-00643-w · Cited by 3 · see citing works
Towards Personalized Aesthetic Image Caption
Xiong, K., Jiang, L., Dang, X., Wang, G., Ye, W., & Qin, Z. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206953 · Cited by 3 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
ChatGPT: Jack of all trades, master of none
Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101861 · Cited by 758 · see citing works
Self-citationHate speech detection in social media: Techniques, recent trends, and future challenges
Rawat, A., Kumar, S., & Samant, S. S. (2024) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1648 · Cited by 68 · see citing works
Min, C., Lin, H., Li, X., Zhao, H., Lu, J., Yang, L., & Xu, B. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.03.015 · Cited by 41 · see citing works
Personalized Large Language Models
Woźniak, S., Koptyra, B., Janz, A., Kazienko, P., & Kocoń, J. (2024) · 2024 IEEE International Conference on Data Mining Workshops (ICDMW) · doi:10.1109/icdmw65004.2024.00071 · Cited by 28 · see citing works
Self-citationEvading text based emotion detection mechanism via adversarial attacks
Bajaj, A., & Vishwakarma, D. K. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.126787 · Cited by 25 · see citing works
Woźniak, S., & Kocoń, J. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00108 · Cited by 20 · see citing works
Self-citationImproving Training Dataset Balance with ChatGPT Prompt Engineering
Kochanek, M., Cichecki, I., Kaszyca, O., Szydło, D., Madej, M., Jędrzejewski, D., Kazienko, P., & Kocoń, J. (2024) · Electronics · doi:10.3390/electronics13122255 · Cited by 19 · see citing works
Self-citationNgo, A., & Kocoń, J. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.102966 · Cited by 13 · see citing works
Self-citationAddressing Challenges in Hate Speech Detection Using BERT-Based Models: A Review
Aljawazeri, J. A., & Jasim, M. N. (2024) · Iraqi Journal for Computer Science and Mathematics · doi:10.52866/ijcsm.2024.05.02.001 · Cited by 11 · see citing works
SeeNet: A Soft Emotion Expert and Data Augmentation Method to Enhance Speech Emotion Recognition
Li, Q., Gao, Y., Wen, Y., Zhao, Z., Li, Y., & Schuller, B. W. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3555406 · Cited by 11 · see citing works
Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flows
Miłkowski, P., Karanowski, K., Wielopolski, P., Kocoń, J., Kazienko, P., & Zięba, M. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00103 · Cited by 10 · see citing works
Self-citationPALS: Personalized Active Learning for Subjective Tasks in NLP
Kanclerz, K., Karanowski, K., Bielaniewicz, J., Gruza, M., Miłkowski, P., Kocoń, J., & Kazienko, P. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.823 · Cited by 9 · see citing works
Self-citationDeep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNets
Kocoń, J. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00101 · Cited by 10 · see citing works
Self-citationInto the Unknown: Self-Learning Large Language Models
Ferdinan, T., Kocoń, J., & Kazienko, P. (2024) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw65004.2024.00060 · Cited by 8 · see citing works
Self-citationHierarchical Attention-enhanced Contextual CapsuleNet for Multilingual Hope Speech Detection
Rehman, M. Z. U., Raghuvanshi, D., Pachar, H., Raghaw, C. S., & Kumar, N. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.126285 · Cited by 7 · see citing works
Fortifying NLP models against poisoning attacks: The power of personalized prediction architectures
Ferdinan, T., & Kocoń, J. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102692 · Cited by 7 · see citing works
Self-citationTowards Model-Based Data Acquisition for Subjective Multi-Task NLP Problems
Kanclerz, K., Bielaniewicz, J., Gruza, M., Kocoń, J., Woźniak, S., & Kazienko, P. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00099 · Cited by 5 · see citing works
Self-citationImproving LLM-Based Recommender Systems with User-Controllable Profiles
Woźniak, S., Duszenko, J., Kocoń, J., & Kazienko, P. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717734 · Cited by 5 · see citing works
Self-citationKnowledge Graphs: Semantics, Machine Learning, and Languages
Acosta Maribel, P. S. (2023) · Studies on the semantic web · doi:10.3233/ssw56 · Cited by 4 · see citing works
Role of Artificial Intelligence in Cyberbullying and Cyberhate Detection
Kaur, M., & Saini, M. (2023) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt56998.2023.10308090 · Cited by 4 · see citing works
Radliński, Ł., Guściora, M., & Kocoń, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97626-1_1 · Cited by 4 · see citing works
Self-citationSu, X., Zierau, N., Kim, S., Wang, A. Y., & Wambsganß, T. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3757472 · Cited by 5 · see citing works
Personalization of Affective State Recognition from Physiological Signals: A Review
Perz, B., & Kazienko, P. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-5881-7_12 · Cited by 2 · see citing works
Self-citationMirończuk, M. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104247 · Cited by 2 · see citing works
Pihulski, D., & Kocoń, J. (2025) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw69685.2025.00261 · Cited by 1 · see citing works
Self-citationKnowledge-Grounded Target Group Language Recognition in Hate Speech
Lobo, P. R., Daga, E., Alani, H., & Fernández, M. (2023) · Studies on the semantic web · doi:10.3233/ssw230002 · Cited by 1 · see citing works
Building and Analysing an Online Hate Speech Corpus: The NETLANG Experience and Beyond
Ermida, I. (2023) · Springer eBooks · doi:10.1007/978-3-031-38248-2_1 · Cited by 1 · see citing works
Bielaniewicz, J., & Kazienko, P. (2023) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw60847.2023.00098 · Cited by 1 · see citing works
Self-citationIs it possible for ChatGPT to mimic human annotator?Preprint
Kaszyca, O., Kazienko, P., Kocoń, J., Cichecki, I., Kochanek, M., & Szydło, D. (2023) · doi:10.36227/techrxiv.170259175.55207879/v1 · Cited by 1 · see citing works
Self-citationBeyond Human Review: Levereging ChatGPT for Label Noise DetectionPreprint
Cichecki, I., Kocoń, J., Kazienko, P., Kaszyca, O., Szydło, D., & Kochanek, M. (2023) · doi:10.36227/techrxiv.170326715.56351742/v1
Self-citationAdvanced Approaches for Hate Speech Detection: A Machine and Deep Learning Investigation
Reghunathan, A., Singh, S., Gunavathi, R., & Johnson, A. (2024) · International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT) · doi:10.1109/tqcebt59414.2024.10545188
Dongna, L. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251363147 · Cited by 1 · see citing works
Mohtaj, S. (2025) · T-labs series in telecommunication services · doi:10.1007/978-3-032-00850-3_1
The impact of annotator gender on Chinese toxic speech detection
Chen, J., Li, Y., Tang, C., Kang, W., Liang, L., & Wu, Y. C. (2026) · Pattern Recognition Letters · doi:10.1016/j.patrec.2026.04.025
Kirchmair, T., Koban, K., & Matthes, J. (2026) · Human Behavior and Emerging Technologies · doi:10.1155/hbe2/3332187
Ghosal, S., Jain, A., & Tayal, D. K. (2026) · IETE Technical Review · doi:10.1080/02564602.2026.2694495
Towards Personalized Language Models: Significant Advancements at the Cost of Emerging Risks
Gniewkowski, M., Walkowiak, P., Klonowski, M., Walkowiak, T., & Syga, P. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37676-3_5
Persona-Guided Sentiment Analysis of Medical Reviews with Encoder and Large Language Models
Nowacka, K., Koptyra, B., & Kocoń, J. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-37933-7_16
Self-citation
- Authors
- Kazienko, P., Bielaniewicz, J., Gruza, M., Kanclerz, K., Karanowski, K., Miłkowski, P., & Kocoń, J.
- Affiliations
- Wrocław University of Science and Technology (PL)
- Corresponding author
- Kazienko, Przemysław
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2023-06
- Volume
- 94
- Pages
- 43-65
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W4315864754
- Cited by
- 38
- References
- 95
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 8 · 2023: 15
- FWCI
- 3.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Information Fusion (publishedVersion, cc-by)
- Funders
- Horizon Europe (101086321)
- Narodowe Centrum Nauki (2021/41/B/ST6/04471)
- Ministerstwo Edukacji i Nauki
- Politechnika Wrocławska
- European Regional Development Fund (POIR.04.02.00-00C002/19)
- Department of Artificial Intelligence, Korea University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- human bias, hate speech detection, emotion recognition, humor detection, multi-task learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
