Skip to main content
SpringerLink
Log in

Enhancing the fairness of offensive memes detection models by mitigating unintended political bias

  • Research
  • Published:
Journal of Intelligent Information Systems Aims and scope Submit manuscript

Abstract

This paper tackles the critical challenge of detecting and mitigating unintended political bias in offensive meme detection. Political memes are a powerful tool that can be used to influence public opinion and disrupt voters’ mindsets. However, current visual-linguistic models for offensive meme detection exhibit unintended bias and struggle to accurately classify non-offensive and offensive memes. This can harm the fairness of the democratic process either by targeting minority groups or promoting harmful political ideologies. With Hindi being the fifth most spoken language globally and having a significant number of native speakers, it is essential to detect and remove Hindi-based offensive memes to foster a fair and equitable democratic process. To address these concerns, we propose three debiasing techniques to mitigate the overrepresentation of majority group perspectives while addressing the suppression of minority opinions in political discourse. To support our approach, we curate a comprehensive dataset called Pol_Off_Meme, designed especially for the Hindi language. Empirical analysis of this dataset demonstrates the efficacy of our proposed debiasing techniques in reducing political bias in internet memes, promoting a fair and equitable democratic environment. Our debiased model, named \(DRTIM^{Adv}_{Att}\), exhibited superior performance compared to the CLIP-based baseline model. It achieved a significant improvement of +9.72% in the F1-score while reducing the False Positive Rate Difference (FPRD) by -16% and the False Negative Rate Difference (FNRD) by -14.01%. Our efforts strive to cultivate a more informed and inclusive political discourse, ensuring that all opinions, irrespective of their majority or minority status, receive adequate attention and representation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Availability of data

The dataset generated during and analyzed during the current study are available in the journal1_memes-A48B repository at the link: https://github.com/Gitanjali1801/journal_jiis.git.

Code Availability

The code of the current study is available at the link: https://github.com/Gitanjali1801/journal_jiis.git.

Notes

  1. https://www.pytorchlightning.ai/

References

  • Aksenov, D., Bourgonje, P., Zaczynska, K., et al. (2021). Fine-grained classification of political bias in German news: A data set and initial experiments. In: Proceedings of the 5th workshop on online abuse and Harms (WOAH 2021). Association for Computational Linguistics, Online, (pp. 121–131). https://doi.org/10.18653/v1/2021.woah-1.13

  • Arango, A., Perez-Martin, J., & Labrada, A. (2022). HateU at SemEval-2022 task 5: Multimedia automatic misogyny identification. In: Proceedings of the 16th international workshop on semantic evaluation (SemEval-2022). Association for Computational Linguistics, Seattle, United States, (pp. 581–584). https://doi.org/10.18653/v1/2022.semeval-1.80

  • Badjatiya, P., Gupta, M., & Varma, V. (2020). Stereotypical bias removal for hate speech detection task using knowledge-based generalizations. arXiv:2001.05495

  • Baldini, I., Wei, D., Natesan Ramamurthy, K., et al. (2022). Your fairness may vary: Pretrained language model fairness in toxic text classification. In: Findings of the association for computational linguistics: ACL 2022. Association for Computational Linguistics, Dublin, Ireland, (pp. 2245–2262). https://doi.org/10.18653/v1/2022.findings-acl.176

  • Bonchi, F., Castillo, C., & Ienco, D. (2013). Meme ranking to maximize posts virality in microblogging platforms. Journal of Intelligent Information Systems (pp. 120–136). https://doi.org/10.1007/s10844-011-0181-4

  • Borkan, D., Dixon, L., Sorensen, J., et al. (2019). Nuanced metrics for measuring unintended bias with real data for text classification. CoRR abs/1903.04561. arXiv:1903.04561

  • Casavantes, M., Aragon, M., Gonzalez Gurrola, LC., et al. (2023). Leveraging posts’ and authors’ metadata to spot several forms of abusive comments in twitter. Journal of Intelligent Information Systems (pp. 1–21). https://doi.org/10.1007/s10844-023-00779-z

  • Chatzakou, D., Leontiadis, I., Blackburn, J., et al. (2019). Detecting cyberbullying and cyberaggression in social media. ACM Trans Web 13(3). https://doi.org/10.1145/3343484

  • Chauhan, DS., S R, D., Ekbal, A., et al. (2020). Sentiment and emotion help sarcasm? a multi-task learning framework for multi-modal sarcasm, sentiment and emotion analysis. In: Proceedings of the 58th Annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online, (pp. 4351–4360). https://doi.org/10.18653/v1/2020.acl-main.401

  • Cheng, L., Mosallanezhad, A., Silva, Y., et al. (2021). Mitigating bias in session-based cyberbullying detection: A non-compromising approach. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol. 1: Long Papers). Association for Computational Linguistics, Online, (pp. 2158–2168). https://doi.org/10.18653/v1/2021.acl-long.168

  • Conneau, A., Khandelwal, K., Goyal, N., et al. (2020). Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online, (pp 8440–8451). https://doi.org/10.18653/v1/2020.acl-main.747

  • Dadvar, M., Trieschnigg, D., Ordelman, R., et al. (2013). Improving cyberbullying detection with user context. In: Proceedings of the 35th European conference on advances in information retrieval. Springer-Verlag, Berlin, Heidelberg, ECIR’13, (pp. 693–696). https://doi.org/10.1007/978-3-642-36973-5_62

  • Das, A., Wahi, JS., & Li, S. (2020). Detecting hate speech in multi-modal memes. arXiv:2012.14891

  • Davidson, T., Bhattacharya, D., Weber, I. (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, (pp. 25–35). https://doi.org/10.18653/v1/W19-3504

  • Dev, S., Sheng, E., Zhao, J., et al. (2022). On measures of biases and harms in NLP. In: Findings of the association for computational linguistics: AACL-IJCNLP 2022. Association for Computational Linguistics, Online only, (pp. 246–267). https://aclanthology.org/2022.findings-aacl.24

  • Dixon, L., Li, J., Sorensen, J., et al. (2018). Measuring and mitigating unintended bias in text classification. In: Proceedings of the 2018 AAAI/ACM conference on AI, ethics, and society. Association for Computing Machinery, New York, USA, AIES ’18, (pp. 67–73). https://doi.org/10.1145/3278721.3278729

  • Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al. (2021). An image is worth 16x16 words: Transformers for image recognition at scale. In: 9th International conference on learning representations, ICLR 2021, Virtual Event, Austria, Accessed 3–7 May 2021 . OpenReview.net. https://dblp.org/rec/conf/iclr/DosovitskiyB0WZ21.bib

  • Godoy, D., & Tommasel, A. (2021). Is my model biased? exploring unintended bias in misogyny detection tasks. In: AIofAI 2021: 1st workshop on adverse impacts and collateral effects of artificial intelligence technologies, Montreal, Canada, (pp. 97–11)

  • Hada, R., Sudhir, S., Mishra, P., et al. (2021). Ruddit: Norms of offensiveness for English Reddit comments. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol. 1: Long Papers). Association for Computational Linguistics, Online, (pp. 2700–2717). https://doi.org/10.18653/v1/2021.acl-long.210

  • Hardt, M., Price, E., & Srebro, N. (2016). Equality of opportunity in supervised learning. In: Proceedings of the 30th international conference on neural information processing systems. Curran Associates Inc., Red Hook, NY, USA, NIPS’16, (pp. 3323–3331)

  • He, S., Zheng, X., Wang, J., et al. (2016). Meme extraction and tracing in crisis events. In: 2016 IEEE Conference on intelligence and security informatics (ISI). IEEE Press, (pp. 61–66). https://doi.org/10.1109/ISI.2016.7745444

  • Hee, MS., Lee, R. K. W., & Chong, W. H. (2022). On explaining multimodal hateful meme detection models. https://doi.org/10.48550/ARXIV.2204.01734

  • Hildebrandt, M. (2019). Privacy as protection of the incomputable self: From agnostic to agonistic machine learning. Theoretical Inquiries in Law (pp. 83–121). https://doi.org/10.1515/til-2019-0004

  • Hossain, E., Sharif, O., Hoque, M. M. (2022a). MUTE: A multimodal dataset for detecting hateful memes. In: Proceedings of the 2nd conference of the asia-pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing: student research workshop. Association for Computational Linguistics, Online, (pp. 32–39). https://aclanthology.org/2022.aacl-srw.5

  • Hossain, E., Sharif, O., Hoque, M. M., et al. (2022b) Identification of multilingual offense and troll from social media memes using weighted ensemble of multimodal features. Journal of King Saud University - Computer and Information Sciences, (pp. 6605–6623). https://doi.org/10.1016/j.jksuci.2022.06.010

  • Hu, A., & Flaxman, S. (2018). Multimodal sentiment analysis to explore the structure of emotions. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery &; data mining. Association for Computing Machinery, New York, USA, KDD ’18, (pp. 350–358). https://doi.org/10.1145/3219819.3219853

  • Hukkelås, H., Mester, R., & Lindseth, F. (2019). Deepprivacy: A generative adversarial network for face anonymization. arXiv:1909.04538

  • Kiela, D., Firooz, H., Mohan, A., et al. (2020). The hateful memes challenge: Detecting hate speech in multimodal memes. In: H. Larochelle, M. Ranzato, R. Hadsell, et al. (eds.), Advances in neural information processing systems, vol. 33. Curran Associates, Inc., (pp. 2611–2624). https://proceedings.neurips.cc/paper/2020/file/1b84c4cee2b8b3d823b30e2d604b1878-Paper.pdf

  • Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. arXiv:1412.6980

  • Kiritchenko, S., & Mohammad, S. M. (2018). Examining gender and race bias in two hundred sentiment analysis systems. arXiv:1805.04508

  • Kumar, R., Ojha, A. K., Malmasi, S., et al. (2018). Benchmarking aggression identification in social media. In: Proceedings of the first workshop on trolling, aggression and cyberbullying (TRAC-2018). Association for Computational Linguistics, Santa Fe, New Mexico, USA, (pp. 1–11). https://aclanthology.org/W18-4401

  • Kumar, S., Wintner, S., Smith, N. A., et al. (2019). Topics to avoid: Demoting latent confounds in text classification. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, (pp. 4153–4163). https://doi.org/10.18653/v1/D19-1425

  • Kumari, G., Bandyopadhyay, D., & Ekbal, A. (2023). Emoffmeme: Identifying offensive memes by leveraging underlying emotions. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-023-14807-1

    Article  Google Scholar 

  • Li, L. H., Yatskar, M., Yin, D., et al. (2020). What does BERT with vision look at? In: Proceedings of the 58th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Online, (pp. 5265–5275). https://doi.org/10.18653/v1/2020.acl-main.469

  • Liu, C., Geigle, G., Krebs, R., et al. (2022). FigMemes: A dataset for figurative language identification in politically-opinionated memes. In: Proceedings of the 2022 conference on empirical methods in natural language processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, (pp. 7069–7086). https://aclanthology.org/2022.emnlp-main.476

  • Malmasi, S., & Zampieri, M. (2018). Challenges in discriminating profanity from hate speech. Journal of Experimental & Theoretical Artificial Intelligence, (pp. 187–202). https://doi.org/10.1080/0952813X.2017.1409284

  • Nee, R. C., & Maio, M. D. (2019) A “presidential look’? an analysis of gender framing in 2016 persuasive memes of hillary clinton. Journal of Broadcasting & Electronic Media, (pp. 304–321). https://doi.org/10.1080/08838151.2019.1620561

  • Nelatoori, K., & Kommanti, H. (2022). Multi-task learning for toxic comment classification and rationale extraction. Journal of Intelligent Information Systems, (pp. 495–519). https://doi.org/10.1007/s10844-022-00726-4

  • Nobata, C., Tetreault, J., Thomas, A. et al. (2016). Abusive language detection in online user content. In: Proceedings of the 25th international conference on World Wide Web. International World Wide Web conferences steering committee, Republic and Canton of Geneva, CHE, WWW ’16, (pp. 145–153). https://doi.org/10.1145/2872427.2883062

  • Nozza, D. (2021). Exposing the limits of zero-shot cross-lingual hate speech detection. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (vol. 2: Short Papers). Association for Computational Linguistics, Online, (pp. 907–914). https://doi.org/10.18653/v1/2021.acl-short.114

  • Nozza, D., Volpetti, C., & Fersini, E. (2019). Unintended bias in misogyny detection. In: IEEE/WIC/ACM International conference on Web intelligence. Association for Computing Machinery, New York, USA, WI ’19, (pp. 149–155). https://doi.org/10.1145/3350546.3352512

  • Pandey, R., & Singh, J. P. (2022). Bert-lstm model for sarcasm detection in code-mixed social media post. Journal Intelligence Information Systems (pp. 235–254). https://doi.org/10.1007/s10844-022-00755-z

  • Park, J. H., Shin, J., & Fung, P. (2018) Reducing gender bias in abusive language detection. In: Proceedings of the 2018 conference on empirical methods in natural language processing. Association for Computational Linguistics, Brussels, Belgium, (pp. 2799–2804). https://doi.org/10.18653/v1/D18-1302

  • Pramanick, S., Dimitrov, D., Mukherjee, R., et al. (2021a) Detecting harmful memes and their targets. In: Findings of the association for computational linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, Online, (pp. 2783–2796). https://doi.org/10.18653/v1/2021.findings-acl.246

  • Pramanick S, Sharma S, Dimitrov D, et al. (2021b) MOMENTA: A multimodal framework for detecting harmful memes and their targets. In: Findings of the association for computational linguistics: EMNLP 2021. Association for Computational Linguistics, Punta Cana, Dominican Republic, (pp. 4439–4455). https://doi.org/10.18653/v1/2021.findings-emnlp.379

  • Premjith, B., Chakravarthi, B. R., Subramanian, M., et al. (2022) Findings of the shared task on multimodal sentiment analysis and troll meme classification in Dravidian languages. In: Proceedings of the second workshop on speech and language technologies for Dravidian languages. Association for Computational Linguistics, Dublin, Ireland, (pp. 254–260). https://doi.org/10.18653/v1/2022.dravidianlangtech-1.39

  • Radford, A., Kim, J. W., Hallacy, C., et al. (2021). Learning transferable visual models from natural language supervision. In: M. Meila, T. Zhang (eds.), Proceedings of the 38th international conference on machine learning, proceedings of machine learning research, vol. 139. PMLR, (pp. 8748–8763). https://proceedings.mlr.press/v139/radford21a.html

  • Ranasinghe, T., & Zampieri, M. (2020). Multilingual offensive language identification with cross-lingual embeddings. In: Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, Online, (pp. 5838–5844). https://doi.org/10.18653/v1/2020.emnlp-main.470

  • Ranasinghe, T., Zampieri, M. (2021). Multilingual offensive language identification for low-resource languages. ACM Transactions Asian Low-Resour Language Information Processes 21(1). https://doi.org/10.1145/3457610

  • Ranjan, R., Pandey, D., Rai, A. K., et al. (2023). A manifold-level hybrid deep learning approach for sentiment classification using an autoregressive model. Applied Sciences. https://doi.org/10.3390/app13053091

    Article  Google Scholar 

  • Ribeiro, M.T., Singh, S., Guestrin, C. (2016). Why should I trust you?: Explaining the predictions of any classifier. arXiv:1602.04938

  • Rijhwani, S., Sequiera, R., Choudhury, M., et al. (2017). Estimating code-switching on Twitter with a novel generalized word-level language detection technique. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol. 1: Long Papers). Association for Computational Linguistics, Vancouver, Canada, (pp. 1971–1982). https://doi.org/10.18653/v1/P17-1180

  • Sharma, S., Akhtar, M. S., Nakov, P., et al. (2022a). DISARM: Detecting the victims targeted by harmful memes. In: Findings of the association for computational linguistics: NAACL 2022. Association for Computational Linguistics, Seattle, United States, (pp. 1572–1588). https://doi.org/10.18653/v1/2022.findings-naacl.118

  • Sharma, V., Bendapudi, A., Trehan, T., et al. (2020c). Analysing political bias in social media. In: 2020 Research, Innovation, knowledge management and technology application for business sustainability (INBUSH), (pp. 241–246), https://doi.org/10.1109/INBUSH46973.2020.9392153

  • Sharma, C., Bhageria, D., Scott, W., et al. (2020a). SemEval-2020 task 8: Memotion analysis- the visuo-lingual metaphor! In: Proceedings of the fourteenth workshop on semantic evaluation. International Committee for Computational Linguistics, Barcelona (online), (pp. 759–773). https://doi.org/10.18653/v1/2020.semeval-1.99

  • Sharma, M., Kandasamy, I., Vasantha, W. (2020b). Memebusters at SemEval-2020 task 8: Feature fusion model for sentiment analysis on memes using transfer learning. In: A. Herbelot, X. Zhu, A. Palmer, et al. (eds.), Proceedings of the fourteenth workshop on semantic evaluation. International Committee for Computational Linguistics, Barcelona (online), (pp. 1163–1171). https://doi.org/10.18653/v1/2020.semeval-1.154

  • Sharma, S., Siddiqui, M. K., Akhtar, M. S., et al. (2022b). Domain-aware self-supervised pre-training for label-efficient meme analysis. In: Proceedings of the 2nd conference of the Asia-Pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing (vol. 1: Long Papers). Association for Computational Linguistics, Online only, (pp. 792–805). https://aclanthology.org/2022.aacl-main.60

  • Shrout, P., Spitzer, R., & Fleiss, J. (1987). Quantification of agreement in psychiatric diagnosis revisited. Archives of General Psychiatry (pp. 172–177). https://doi.org/10.1001/archpsyc.1987.01800140084013

  • Simonyan, K., & Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. Computational and Biological Learning Society, (pp. 1–14)

  • Singh, P., & Lefever, E. (2021). LT3 at SemEval-2021 task 6: Using multi-modal compact bilinear pooling to combine visual and textual understanding in memes. In: Proceedings of the 15th international workshop on semantic evaluation (SemEval-2021). Association for Computational Linguistics, Online, (pp. 1051–1055). https://doi.org/10.18653/v1/2021.semeval-1.145

  • Spinde, T., Rudnitckaia, L., Sinha, K., et al. (2021). MBIC - A media bias annotation dataset including annotator characteristics. arXiv:2105.11910

  • Sturm, B. L. (2013). Classification accuracy is not enough. Journal of Intelligent Information Systems (pp. 371–406). https://doi.org/10.1007/s10844-013-0250-y

  • Su, B., Peng, J. (2023). Sentiment analysis of comment texts on online courses based on hierarchical attention mechanism. Applied Sciences 13(7). https://doi.org/10.3390/app13074204

  • Subramanian, M., Ponnusamy, R., Benhur, S., et al. (2022). Offensive language detection in tamil youtube comments by adapters and cross-domain knowledge transfer. Computer Speech & Language (pp. 101404). https://doi.org/10.1016/j.csl.2022.101404

  • Sundararajan, M., Taly, A., & Yan, Q. (2017). Axiomatic attribution for deep networks. arXiv:1703.01365

  • Suryawanshi, S., Chakravarthi, B. R., Arcan, M., et al. (2020). Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In: Proceedings of the second workshop on trolling, aggression and cyberbullying. European Language Resources Association (ELRA), Marseille, France, (pp. 32–41). https://aclanthology.org/2020.trac-1.6

  • Tan, H., & Bansal, M. (2019). LXMERT: Learning cross-modality encoder representations from transformers. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP). Association for Computational Linguistics, Hong Kong, China, (pp. 5100–5111). https://doi.org/10.18653/v1/D19-1514

  • Tran, H. N., & Cambria, E. (2018). Ensemble application of ELM and GPU for real-time multimodal sentiment analysis. Memetic Comput (pp. 3–13). https://doi.org/10.1007/s12293-017-0228-3

  • Wich, M., Bauer, J., & Groh, G. (2020). Impact of politically biased data on hate speech classification. In: Proceedings of the fourth workshop on online abuse and harms. Association for Computational Linguistics, Online, (pp. 54–64). https://doi.org/10.18653/v1/2020.alw-1.7

  • Wiegand, M., Ruppenhofer, J., & Kleinbauer, T. (2019a). Detection of abusive language: The problem of biased datasets. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, vol. 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, (pp. 602–608). https://doi.org/10.18653/v1/N19-1060

  • Wiegand, M., Siegel, M., & Ruppenhofer, J. (2019b). Overview of the germeval 2018 shared task on the identification of offensive language. Proceedings of GermEval 2018, 14th conference on natural language processing (KONVENS 2018), Vienna, Austria, Austrian Academy of Sciences, Vienna, Austria, (pp. 1 – 10). Accessed 21 Sept 2018. https://nbn-resolving.org/urn:nbn:de:bsz:mh39-84935

  • Xia, M., Field, A., & Tsvetkov, Y. (2020). Demoting racial bias in hate speech detection. In: Proceedings of the eighth international workshop on natural language processing for social media. Association for Computational Linguistics, Online, (pp. 7–14). https://doi.org/10.18653/v1/2020.socialnlp-1.2

  • Xu, J. M., Jun, K. S., Zhu, X., et al. (2012). Learning from bullying traces in social media. In: Proceedings of the 2012 conference of the North American chapter of the association for computational linguistics: human language technologies. Association for Computational Linguistics, Montréal, Canada, (pp. 656–666). https://aclanthology.org/N12-1084

  • Yu, Z., Yu, J., Fan, J., et al. (2017). Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In: 2017 IEEE International conference on computer vision (ICCV). IEEE Computer Society, Los Alamitos, CA, USA, (pp. 1839–1848). https://doi.org/10.1109/ICCV.2017.202

  • Zampieri, M., Malmasi, S., Nakov, P., et al. (2019a) Predicting the type and target of offensive posts in social media. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: Human Language Technologies, vol. 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, (pp. 1415–1420). https://doi.org/10.18653/v1/N19-1144

  • Zampieri, M., Malmasi, S., Nakov, P., et al. (2019b) SemEval-2019 task 6: Identifying and categorizing offensive language in social media (OffensEval). In: Proceedings of the 13th international workshop on semantic evaluation. Association for Computational Linguistics, Minneapolis, Minnesota, USA, (pp. 75–86). https://doi.org/10.18653/v1/S19-2010

  • Zampieri, M., Ranasinghe, T., Sarkar, D., et al. (2023). Offensive language identification with multi-task learning. Journal Intelligence Information System (pp. 613–630). https://doi.org/10.1007/s10844-023-00787-z

  • Zhou, Y., Chen, Z., & Yang, H. (2021) Multimodal learning for hateful memes detection. In: 2021 IEEE International conference on multimedia & expo workshops (ICMEW), (pp. 1–6). https://doi.org/10.1109/ICMEW53276.2021.9455994

  • Zhou, K., Lai, E., & Jiang, J. (2022). VLStereoSet: A study of stereotypical bias in pre-trained vision-language models. In: Proceedings of the 2nd conference of the Asia-Pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing (vol. 1: Long Papers). Association for Computational Linguistics, Online only, (pp. 527–538). https://aclanthology.org/2022.aacl-main.40

  • Zhou, L., Palangi, H., Zhang, L., et al. (2020). Unified vision-language pre-training for image captioning and vqa. Proceedings of the AAAI Conference on Artificial Intelligence,34(07), 13041–13049.

Download references

Funding

The authors gratefully acknowledge the project “HELIOS - Hate, Hyperpartisan, and Hyperpluralism Elicitation and Observer System“, sponsored by Wipro AI.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Patna, 801103, Bihar, India

    Gitanjali Kumari, Anubhav Sinha & Asif Ekbal

  2. Wipro AI, Banglore, 560100, Karnataka, India

    Arindam Chatterjee & Vinutha B N

Authors
  1. Gitanjali Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anubhav Sinha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Asif Ekbal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arindam Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vinutha B N
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Gitanjali Kumari: Corpus creation, Algorithm design, Implementation, Experiments, Analysis, Writing - original draft. Anubhav Sinha: Analysis, Writing - original draft. Asif Ekbal: Supervision, Algorithm conceptualization, Arindam Chatterjee: Supervision, Vinitha B N: Supervision

Corresponding author

Correspondence to Gitanjali Kumari.

Ethics declarations

Competing interests

The authors declare no competing interests.

Conflicts of interest

The authors declare no conflict of interest about the work reported in this paper.

Ethics approval

1. Individual Privacy To maintain the anonymity of any individual, we replaced the actual name with Person-XYZ throughout the paper. In addition, we also tried to anonymize the known faces presented in the visual part of the meme by masking them. We have masked these faces only to maintain the anonymity issues in the paper. During the implementation, we used the original image. 2. Misuse Potential: We suggest that researchers be aware that our dataset might be abused to filter the memes based on prejudices that may or may not be connected to demographics or other textual information. To prevent this from happening, human intervention with moderation would be essential. 3. Intended Use: Our dataset is presented to encourage research into studying political memes on the internet. We believe that it represents a valuable resource when used appropriately. 4. Institutional Review Board Statement: The resources we created for this study were from publicly available memes. We strictly adhered to the restrictions on data usage to avoid any infringement of copyright laws. Furthermore, our Institutional Review Board (IRB) evaluated and approved our study. We plan to make our code and data accessible for research purposes, subject to appropriate data agreement procedures, upon acceptance of our study. 5. Availability of data: The dataset generated during and analyzed during the current study is available from the corresponding author upon request.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumari, G., Sinha, A., Ekbal, A. et al. Enhancing the fairness of offensive memes detection models by mitigating unintended political bias. J Intell Inf Syst (2024). https://doi.org/10.1007/s10844-023-00834-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10844-023-00834-9

Keywords

Search

Navigation