{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:45:45Z","timestamp":1769906745851,"version":"3.49.0"},"reference-count":77,"publisher":"Association for Computing Machinery (ACM)","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2024,8,31]]},"abstract":"<jats:p>Multimodal hateful social media meme detection is an important and challenging problem in the vision-language domain. Recent studies show high accuracy for such multimodal tasks due to datasets that provide better joint multimodal embedding to narrow the semantic gap. Religiously hateful meme detection is not extensively explored among published datasets. While there is a need for higher accuracy on religiously hateful memes, deep learning\u2013based models often suffer from inductive bias. This issue is addressed in this work with the following contributions. First, a religiously hateful memes dataset is created and published publicly to advance hateful religious memes detection research. Over 2000 meme images are collected with their corresponding text. The proposed approach compares and fine-tunes VisualBERT pre-trained on the Conceptual Caption (CC) dataset for the downstream classification task. We also extend the dataset with the Facebook hateful memes dataset. We extract visual features using ResNeXT-152 Aggregated Residual Transformations\u2013based Masked Regions with Convolutional Neural Networks (R-CNN) and Bidirectional Encoder Representations from Transformers (BERT) uncased for textual encoding for the early fusion model. We use the primary evaluation metric of an Area Under the Operator Characters Curve (AUROC) to measure model separability. Results show that the proposed approach has a higher AUROC score of 78%, proving the model\u2019s higher separability performance and an accuracy of 70%. It shows comparatively superior performance considering dataset size and against ensemble-based machine learning approaches.<\/jats:p>","DOI":"10.1145\/3623396","type":"journal-article","created":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T10:55:00Z","timestamp":1694861700000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Multimodal Religiously Hateful Social Media Memes Classification Based on Textual and Image Data"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5541-2625","authenticated-orcid":false,"given":"Ameer","family":"Hamza","sequence":"first","affiliation":[{"name":"Air University, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0045-2934","authenticated-orcid":false,"given":"Abdul Rehman","family":"Javed","sequence":"additional","affiliation":[{"name":"Lebanese American University, Byblos, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9081-3598","authenticated-orcid":false,"given":"Farkhund","family":"Iqbal","sequence":"additional","affiliation":[{"name":"Zayed University, Abu Dhabi, UAE"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0202-9061","authenticated-orcid":false,"given":"Amanullah","family":"Yasin","sequence":"additional","affiliation":[{"name":"Air University, Islamabad, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9851-4103","authenticated-orcid":false,"given":"Gautam","family":"Srivastava","sequence":"additional","affiliation":[{"name":"Brandon University, Brandon, Canada, China Medical University, Taichung, Taiwan, and Lebanese American University, Beirut, Lebanon"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1972-5979","authenticated-orcid":false,"given":"Dawid","family":"Po\u0142ap","sequence":"additional","affiliation":[{"name":"Silesian University of Technology, Gliwice, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-801X","authenticated-orcid":false,"given":"Thippa Reddy","family":"Gadekallu","sequence":"additional","affiliation":[{"name":"Lebanese American University, Byblos, Lebanon, Jiaxing University, Jiaxing, China, and Lovely Professional University, Phagwara, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2531-2564","authenticated-orcid":false,"given":"Zunera","family":"Jalil","sequence":"additional","affiliation":[{"name":"National University of Computer and Emerging Sciences, Islamabad, Pakistan"}]}],"member":"320","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1007\/978-3-030-66840-2_109","volume-title":"Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications","author":"Afridi Tariq Habib","year":"2021","unstructured":"Tariq Habib Afridi, Aftab Alam, Muhammad Numan Khan, Jawad Khan, and Young-Koo Lee. 2021. A multimodal memes classification: A survey and open research issues. In Innovations in Smart Cities Applications Volume 4: The Proceedings of the 5th International Conference on Smart City Applications. Springer, 1451\u20131466."},{"key":"e_1_3_2_3_2","article-title":"Two-way feature extraction using sequential and multimodal approach for hateful meme classification","volume":"2021","author":"Aggarwal Apeksha","year":"2021","unstructured":"Apeksha Aggarwal, Vibhav Sharma, Anshul Trivedi, Mayank Yadav, Chirag Agrawal, Dilbag Singh, Vipul Mishra, and Hass\u00e8ne Gritli. 2021. Two-way feature extraction using sequential and multimodal approach for hateful meme classification. Complexity 2021 (2021).","journal-title":"Complexity"},{"key":"e_1_3_2_4_2","article-title":"Optimization of students\u2019 performance prediction through an iterative model of frustration severity","volume":"2022","author":"Ahmad Sadique","year":"2022","unstructured":"Sadique Ahmad, Najib Ben Aoun, Mohammed A El Affendi, M Shahid Anwar, Sidra Abbas, and Ahmed A Latif. 2022. Optimization of students\u2019 performance prediction through an iterative model of frustration severity. Computational Intelligence and Neuroscience 2022 (2022).","journal-title":"Computational Intelligence and Neuroscience"},{"key":"e_1_3_2_5_2","first-page":"69","volume-title":"Informatics","author":"Aldjanabi Wassen","year":"2021","unstructured":"Wassen Aldjanabi, Abdelghani Dahou, Mohammed AA Al-qaness, Mohamed Abd Elaziz, Ahmed Mohamed Helmi, and Robertas Dama\u0161evi\u010dius. 2021. Arabic offensive and hate speech detection using a cross-corpora multi-task learning model. In Informatics, Vol. 8. Multidisciplinary Digital Publishing Institute, 69."},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","unstructured":"Abdullah Alqahtani Habib Ullah Khan Shtwai Alsubai Mohemmed Sha Ahmad Almadhor Tayyab Iqbal and Sidra Abbas. 2022. An efficient approach for textual data classification using deep learning. (2022).","DOI":"10.3389\/fncom.2022.992296"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/app10238614"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00636"},{"issue":"6","key":"e_1_3_2_9_2","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1108\/OIR-08-2020-0333","article-title":"An empirical approach to understanding users\u2019 fake news identification on social media","volume":"45","author":"Barakat Karine Aoun","year":"2021","unstructured":"Karine Aoun Barakat, Amal Dabbous, and Abbas Tarhini. 2021. An empirical approach to understanding users\u2019 fake news identification on social media. Online Information Review 45, 6 (2021), 1080\u20131096.","journal-title":"Online Information Review"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.09.030"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1002\/spe.2853"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3130902"},{"key":"e_1_3_2_13_2","volume-title":"Deep Learning","author":"Bengio Yoshua","year":"2017","unstructured":"Yoshua Bengio, Ian Goodfellow, and Aaron Courville. 2017. Deep Learning. Vol. 1. MIT Press, Cambridge, Massachusetts, USA."},{"key":"e_1_3_2_14_2","article-title":"Feature and subfeature selection for classification using correlation coefficient and fuzzy model","author":"Bhuyan Hemanta Kumar","year":"2021","unstructured":"Hemanta Kumar Bhuyan, Chinmay Chakraborty, Subhendu Kumar Pani, and Vinayakumar Ravi. 2021. Feature and subfeature selection for classification using correlation coefficient and fuzzy model. IEEE Transactions on Engineering Management (2021).","journal-title":"IEEE Transactions on Engineering Management"},{"key":"e_1_3_2_15_2","article-title":"Analysis of subfeature for classification in data mining","author":"Bhuyan Hemanta Kumar","year":"2021","unstructured":"Hemanta Kumar Bhuyan and Vinayakumar Ravi. 2021. Analysis of subfeature for classification in data mining. IEEE Transactions on Engineering Management (2021).","journal-title":"IEEE Transactions on Engineering Management"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","unstructured":"Joanna Bitton and Zoe Papakipos. 2021. AugLy: A data augmentations library for audio image text and video. https:\/\/github.com\/facebookresearch\/AugLy. 10.5281\/zenodo.5014032","DOI":"10.5281\/zenodo.5014032"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00051"},{"key":"e_1_3_2_18_2","article-title":"Improving language models by retrieving from trillions of tokens","author":"Borgeaud Sebastian","year":"2021","unstructured":"Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George van den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, et\u00a0al. 2021. Improving language models by retrieving from trillions of tokens. arXiv preprint arXiv:2112.04426 (2021).","journal-title":"arXiv preprint arXiv:2112.04426"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/mti6040028"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3091478.3091487"},{"key":"e_1_3_2_22_2","article-title":"Microsoft COCO captions: Data collection and evaluation server","author":"Chen Xinlei","year":"2015","unstructured":"Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Doll\u00e1r, and C. Lawrence Zitnick. 2015. Microsoft COCO captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325 (2015).","journal-title":"arXiv preprint arXiv:1504.00325"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0274300"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_7"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/3462244.3479949"},{"key":"e_1_3_2_27_2","article-title":"BERT: Pre-training of deep bidirectional Transformers for language understanding","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. BERT: Pre-training of deep bidirectional Transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).","journal-title":"arXiv preprint arXiv:1810.04805"},{"key":"e_1_3_2_28_2","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et\u00a0al. 2020. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020).","journal-title":"arXiv preprint arXiv:2010.11929"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10111332"},{"key":"e_1_3_2_30_2","doi-asserted-by":"crossref","unstructured":"Matt Gardner Yoav Artzi Victoria Basmova Jonathan Berant Ben Bogin Sihao Chen Pradeep Dasigi Dheeru Dua Yanai Elazar Ananth Gottumukkala Nitish Gupta Hanna Hajishirzi Gabriel Ilharco Daniel Khashabi Kevin Lin Jiangming Liu Nelson F. Liu Phoebe Mulcaire Qiang Ning Sameer Singh Noah A. Smith Sanjay Subramanian Reut Tsarfaty Eric Wallace Ally Zhang and Ben Zhou. 2020. Evaluating Models\u2019 Local Decision Boundaries via Contrast Sets. arxiv:cs.CL\/2004.02709","DOI":"10.18653\/v1\/2020.findings-emnlp.117"},{"key":"e_1_3_2_31_2","first-page":"0","volume-title":"Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","author":"Gomez Raul","year":"2020","unstructured":"Raul Gomez, Jaume Gibert, Lluis Gomez, and Dimosthenis Karatzas. 2020. Exploring hate speech detection in multimodal publications. In Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV). 0\u20130."},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093414"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.670"},{"key":"e_1_3_2_34_2","article-title":"Hybrid intelligence-driven medical image recognition for remote patient diagnosis in Internet of Medical Things","author":"Guo Zhiwei","year":"2021","unstructured":"Zhiwei Guo, Yu Shen, Shaohua Wan, Wenlong Shang, and Keping Yu. 2021. Hybrid intelligence-driven medical image recognition for remote patient diagnosis in Internet of Medical Things. IEEE Journal of Biomedical and Health Informatics (2021).","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/SWC50871.2021.00093"},{"key":"e_1_3_2_37_2","article-title":"Detection of cyberbullying incidents on the Instagram social network","author":"Hosseinmardi Homa","year":"2015","unstructured":"Homa Hosseinmardi, Sabrina Arredondo Mattson, Rahat Ibn Rafiq, Richard Han, Qin Lv, and Shivakant Mishra. 2015. Detection of cyberbullying incidents on the Instagram social network. arXiv preprint arXiv:1503.03909 (2015).","journal-title":"arXiv preprint arXiv:1503.03909"},{"issue":"2","key":"e_1_3_2_38_2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.14742\/ajet.6749","article-title":"Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis","volume":"37","author":"Huang Changqin","year":"2021","unstructured":"Changqin Huang, Zhongmei Han, Ming Li, Xizhe Wang, and Wenzhu Zhao. 2021. Sentiment evolution with interaction levels in blended learning environments: Using learning analytics and epistemic network analysis. Australasian Journal of Educational Technology 37, 2 (2021), 81\u201395.","journal-title":"Australasian Journal of Educational Technology"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12041020"},{"key":"e_1_3_2_40_2","article-title":"Learning the difference that makes a difference with counterfactually-augmented data","author":"Kaushik Divyansh","year":"2019","unstructured":"Divyansh Kaushik, Eduard Hovy, and Zachary C. Lipton. 2019. Learning the difference that makes a difference with counterfactually-augmented data. arXiv preprint arXiv:1909.12434 (2019).","journal-title":"arXiv preprint arXiv:1909.12434"},{"key":"e_1_3_2_41_2","unstructured":"Divyansh Kaushik Eduard Hovy and Zachary C. Lipton. 2020. Learning the Difference that Makes a Difference with Counterfactually-Augmented Data. arxiv:cs.CL\/1909.12434"},{"key":"e_1_3_2_42_2","article-title":"Transformers in vision: A survey","author":"Khan Salman","year":"2021","unstructured":"Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. 2021. Transformers in vision: A survey. arXiv preprint arXiv:2101.01169 (2021).","journal-title":"arXiv preprint arXiv:2101.01169"},{"key":"e_1_3_2_43_2","article-title":"The hateful memes challenge: Detecting hate speech in multimodal memes","author":"Kiela Douwe","year":"2020","unstructured":"Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, and Davide Testuggine. 2020. The hateful memes challenge: Detecting hate speech in multimodal memes. arXiv preprint arXiv:2005.04790 (2020).","journal-title":"arXiv preprint arXiv:2005.04790"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106942"},{"key":"e_1_3_2_45_2","first-page":"110137","article-title":"Diversity-driven automated web API recommendation based on implicit requirements","author":"Kou Huaizhen","year":"2023","unstructured":"Huaizhen Kou, Jian Xu, and Lianyong Qi. 2023. Diversity-driven automated web API recommendation based on implicit requirements. Applied Soft Computing (2023), 110137.","journal-title":"Applied Soft Computing"},{"key":"e_1_3_2_46_2","unstructured":"Ranjay Krishna Yuke Zhu Oliver Groth Justin Johnson Kenji Hata Joshua Kravitz Stephanie Chen Yannis Kalantidis Li-Jia Li David A. Shamma Michael S. Bernstein and Fei-Fei Li. 2016. Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations. arxiv:cs.CV\/1602.07332"},{"key":"e_1_3_2_47_2","article-title":"Quantitatively interpreting residents happiness prediction by considering factor\u2013factor interactions","author":"Li Lin","year":"2023","unstructured":"Lin Li, Xiaohua Wu, Miao Kong, Jinhang Liu, and Jianwei Zhang. 2023. Quantitatively interpreting residents happiness prediction by considering factor\u2013factor interactions. IEEE Transactions on Computational Social Systems (2023).","journal-title":"IEEE Transactions on Computational Social Systems"},{"key":"e_1_3_2_48_2","unstructured":"Liunian Harold Li Mark Yatskar Da Yin Cho-Jui Hsieh and Kai-Wei Chang. 2019. VisualBERT: A Simple and Performant Baseline for Vision and Language. arxiv:cs.CV\/1908.03557"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58577-8_8"},{"key":"e_1_3_2_50_2","unstructured":"Phillip Lippe Nithin Holla Shantanu Chandra Santhosh Rajamanickam Georgios Antoniou Ekaterina Shutova and Helen Yannakoudakis. 2020. A Multimodal Framework for the Detection of Hateful Memes. arxiv:cs.CL\/2012.12871"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics12102320"},{"issue":"1","key":"e_1_3_2_52_2","first-page":"1","article-title":"Emotion classification for short texts: An improved multi-label method","volume":"10","author":"Liu Xuan","year":"2023","unstructured":"Xuan Liu, Tianyi Shi, Guohui Zhou, Mingzhe Liu, Zhengtong Yin, Lirong Yin, and Wenfeng Zheng. 2023. Emotion classification for short texts: An improved multi-label method. Humanities and Social Sciences Communications 10, 1 (2023), 1\u20139.","journal-title":"Humanities and Social Sciences Communications"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3147032"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1400"},{"key":"e_1_3_2_55_2","article-title":"Efficient estimation of word representations in vector space","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013).","journal-title":"arXiv preprint arXiv:1301.3781"},{"key":"e_1_3_2_56_2","article-title":"\u2018Hate speech in Encyclopedia of the American Constitution","author":"Nockleyby J.","year":"2000","unstructured":"J. Nockleyby. 2000. \u2018Hate speech in Encyclopedia of the American Constitution. Electronic Journal of Academic and Special Librarianship (2000).","journal-title":"Electronic Journal of Academic and Special Librarianship"},{"key":"e_1_3_2_57_2","first-page":"1501","volume-title":"2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC)","author":"Nsouli Ahmad","year":"2018","unstructured":"Ahmad Nsouli, Azzam Mourad, and Danielle Azar. 2018. Towards proactive social learning approach for traffic event detection based on Arabic tweets. In 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE, 1501\u20131506."},{"key":"e_1_3_2_58_2","first-page":"4055","volume-title":"International Conference on Machine Learning","author":"Parmar Niki","year":"2018","unstructured":"Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran. 2018. Image transformer. In International Conference on Machine Learning. PMLR, 4055\u20134064."},{"key":"e_1_3_2_59_2","first-page":"8026","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et\u00a0al. 2019. PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (2019), 8026\u20138037.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/D14-1162"},{"key":"e_1_3_2_61_2","article-title":"Social media in the Middle East: 2019 in review","author":"Radcliffe Damian","year":"2020","unstructured":"Damian Radcliffe and Hadil Abuhmaid. 2020. Social media in the Middle East: 2019 in review. Available at SSRN 3517916 (2020).","journal-title":"Available at SSRN 3517916"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1504\/ijwbc.2020.108626"},{"key":"e_1_3_2_63_2","unstructured":"Chhavi Sharma Deepesh Bhageria William Scott Srinivas PYKL Amitava Das Tanmoy Chakraborty Viswanath Pulabaigari and Bjorn Gamb\u00e4ck. 2020. Task report: Memotion analysis 1.0@ Semeval 2020: The visuo-lingual metaphor. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020) Barcelona Spain Sep. Association for Computational Linguistics."},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1238"},{"key":"e_1_3_2_65_2","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/B978-1-59749-276-8.00002-9","volume-title":"Scene of the Cybercrime (Second Edition)","author":"Shinder Littlejohn","year":"2008","unstructured":"Littlejohn Shinder and Michael Cross. 2008. Chapter 2 \u2014the evolution of cybercrime. In Scene of the Cybercrime (Second Edition), Littlejohn Shinder and Michael Cross (Eds.). Syngress, Burlington, 41\u201375."},{"key":"e_1_3_2_66_2","article-title":"Are we pretraining it right? Digging deeper into visio-linguistic pretraining","author":"Singh Amanpreet","year":"2020","unstructured":"Amanpreet Singh, Vedanuj Goswami, and Devi Parikh. 2020. Are we pretraining it right? Digging deeper into visio-linguistic pretraining. arXiv preprint arXiv:2004.08744 (2020).","journal-title":"arXiv preprint arXiv:2004.08744"},{"key":"e_1_3_2_67_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01625"},{"key":"e_1_3_2_68_2","article-title":"Vl-BERT: Pre-training of generic visual-linguistic representations","author":"Su Weijie","year":"2019","unstructured":"Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. 2019. Vl-BERT: Pre-training of generic visual-linguistic representations. arXiv preprint arXiv:1908.08530 (2019).","journal-title":"arXiv preprint arXiv:1908.08530"},{"key":"e_1_3_2_69_2","first-page":"32","volume-title":"Proceedings of the 2nd Workshop on Trolling, Aggression and Cyberbullying","author":"Suryawanshi Shardul","year":"2020","unstructured":"Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, and Paul Buitelaar. 2020. Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. In Proceedings of the 2nd Workshop on Trolling, Aggression and Cyberbullying. 32\u201341."},{"key":"e_1_3_2_70_2","article-title":"LXMERT: Learning cross-modality encoder representations from Transformers","author":"Tan Hao","year":"2019","unstructured":"Hao Tan and Mohit Bansal. 2019. LXMERT: Learning cross-modality encoder representations from Transformers. arXiv preprint arXiv:1908.07490 (2019).","journal-title":"arXiv preprint arXiv:1908.07490"},{"key":"e_1_3_2_71_2","first-page":"5998","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998\u20136008."},{"key":"e_1_3_2_72_2","unstructured":"Riza Velioglu and Jewgeni Rose. 2020. Detecting Hate Speech in Memes Using Multimodal Deep Learning Approaches: Prize-winning solution to Hateful Memes Challenge. arxiv:cs.AI\/2012.12975"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W16-5618"},{"key":"e_1_3_2_74_2","article-title":"Huggingface\u2019s Transformers: State-of-the-art natural language processing","author":"Wolf Thomas","year":"2019","unstructured":"Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R\u00e9mi Louf, Morgan Funtowicz, et\u00a0al. 2019. Huggingface\u2019s Transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019).","journal-title":"arXiv preprint arXiv:1910.03771"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3182426"},{"key":"e_1_3_2_76_2","first-page":"1","article-title":"Social media use impacts body image and eating behavior in pregnant women","author":"Zeeni N.","year":"2021","unstructured":"N. Zeeni, J. Abi Kharma, and L. Mattar. 2021. Social media use impacts body image and eating behavior in pregnant women. Current Psychology (2021), 1\u20138.","journal-title":"Current Psychology"},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00688"},{"key":"e_1_3_2_78_2","article-title":"The effect of image enhancement on influencer\u2019s product recommendation effectiveness: The roles of perceived influencer authenticity and post type","author":"Zhang Yajun","year":"2023","unstructured":"Yajun Zhang, Zhuoyan Shao, Jin Zhang, Banggang Wu, and Liying Zhou. 2023. The effect of image enhancement on influencer\u2019s product recommendation effectiveness: The roles of perceived influencer authenticity and post type. Journal of Research in Interactive Marketing (2023).","journal-title":"Journal of Research in Interactive Marketing"}],"container-title":["ACM Transactions on Asian and Low-Resource Language Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3623396","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3623396","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:26Z","timestamp":1750178186000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3623396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":77,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,8,31]]}},"alternative-id":["10.1145\/3623396"],"URL":"https:\/\/doi.org\/10.1145\/3623396","relation":{},"ISSN":["2375-4699","2375-4702"],"issn-type":[{"value":"2375-4699","type":"print"},{"value":"2375-4702","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"2023-05-03","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-08-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-08-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}