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Association for computational linguistics, pp 1415\u20131420. https:\/\/doi.org\/10.18653\/v1\/N19-1144. https:\/\/aclanthology.org\/N19-1144","DOI":"10.18653\/v1\/N19-1144"},{"key":"14807_CR62","unstructured":"Zhang W, Liu G, Li Z, Zhu F (2020) Hateful memes detection via complementary visual and linguistic networks. arXiv:2012.04977"},{"key":"14807_CR63","doi-asserted-by":"crossref","unstructured":"Zhou Y, Chen Z (2020) Multimodal learning for hateful memes detection. arXiv:2011.12870","DOI":"10.1109\/ICMEW53276.2021.9455994"},{"key":"14807_CR64","unstructured":"Zhu R (2020) Enhance multimodal transformer with external label and in-domain pretrain: hateful meme challenge winning solution. arXiv:2012.08290"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14807-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-14807-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-14807-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T10:12:52Z","timestamp":1701166372000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-14807-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,26]]},"references-count":64,"journal-issue":{"issue":"29","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["14807"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-14807-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,26]]},"assertion":[{"value":"28 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 September 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 April 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interests about the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}},{"value":"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. Biases: Detecting and removing political and religious biases is an extensive research area. However, previous annotation studies show that we cannot correctly remove bias and subjectivity from the annotation process despite having some form of annotation scheme. However, any biases detected in our dataset are unintentional, and we have no intention of harming any individual or group. We ensure that our data collection is generated equally and comparably in order to answer any political and religious bias queries. Furthermore, we ensure that the topic includes various issues relevant in the Indian context over the last seven years by using a keyword-based data-gathering technique. Moreover, we made sure that the terms included were inclusive of all the conceivable politicians, political organizations, young politicians, extreme groups, and religions and were not prejudiced against any one group. Based on previous work done by to remove biases from the dataset during annotation, in our dataset, annotators were strictly instructed not to make decisions based on what they believe but on what the social media user wants to transmit through that meme.3. 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.4. Intended Use: Our dataset is presented to encourage research into studying humorous memes on the internet. We believe that it represents a valuable resource when used appropriately.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of Interests"}}]}}