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dc.contributor.authorSuryawanshi, Shardul
dc.contributor.authorChakravarthi, Bharathi Raja
dc.contributor.authorArcan, Mihael
dc.contributor.authorBuitelaar, Paul
dc.date.accessioned2020-07-08T11:05:56Z
dc.date.available2020-07-08T11:05:56Z
dc.date.issued2020-05-11
dc.identifier.citationSuryawanshi, Shardul, Chakravarthi, Bharathi Raja, Arcan, Mihael, & Buitelaar, Paul. (2020). Multimodal meme dataset (MultiOFF) for identifying offensive content in image and text. Paper presented at the Language Resources and Evaluation Conference (LREC 2020) Second Workshop on Trolling, Aggression and Cyberbullying, Marseille, France, 11-16 May.en_IE
dc.identifier.urihttp://hdl.handle.net/10379/16060
dc.description.abstractA meme is a form of media that spreads an idea or emotion across the internet. As posting meme has become a new form of communication of the web, due to the multimodal nature of memes, postings of hateful memes or related events like trolling, cyberbullying are increasing day by day. Hate speech, offensive content and aggression content detection have been extensively explored in a single modality such as text or image. However, combining two modalities to detect offensive content is still a developing area. Memes make it even more challenging since they express humour and sarcasm in an implicit way, because of which the meme may not be offensive if we only consider the text or the image. Therefore, it is necessary to combine both modalities to identify whether a given meme is offensive or not. Since there was no publicly available dataset for multimodal offensive meme content detection, we leveraged the memes related to the 2016 U.S. presidential election and created the MultiOFF multimodal meme dataset for offensive content detection dataset. We subsequently developed a classifier for this task using the MultiOFF dataset. We use an early fusion technique to combine the image and text modality and compare it with a text- and an image-only baseline to investigate its effectiveness. Our results show improvements in terms of Precision, Recall, and F-Score. The code and dataset for this paper is published in https://github.com/bharathichezhiyan/ Multimodal-Meme-Classification-Identifying-Offensive-Content-in-Image-and-Texten_IE
dc.description.sponsorshipThis publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 P2, co-funded by the European Regional Development Fund, as well as by the H2020 project Pret- ˆ a-LLOD under Grant Agreement ` number 825182.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherEuropean Language Resources Association (ELRA)en_IE
dc.relation.ispartofProceedings of the Second Workshop on Trolling, Aggression and Cyberbullyingen
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Ireland
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/
dc.subjectmultimodal dataen_IE
dc.subjectclassificationen_IE
dc.subjectmemesen_IE
dc.subjectoffensive contenten_IE
dc.subjectopinion miningen_IE
dc.titleMultimodal meme dataset (MultiOFF) for identifying offensive content in image and texten_IE
dc.typeWorkshop paperen_IE
dc.date.updated2020-07-01T13:20:11Z
dc.local.publishedsourcehttps://www.aclweb.org/anthology/2020.trac-1.0/en_IE
dc.description.peer-reviewednon-peer-reviewed
dc.contributor.funderScience Foundation Irelanden_IE
dc.contributor.funderEuropean Regional Development Funden_IE
dc.contributor.funderHorizon 2020en_IE
dc.internal.rssid21013122
dc.local.contactShardul Suryawanshi, Ida Business Park, Lower Dangan, Galway. Email: shardul.suryawanshi@insight-centre.org
dc.local.copyrightcheckedYes
dc.local.versionPUBLISHED
dcterms.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en_IE
dcterms.projectinfo:eu-repo/grantAgreement/EC/H2020::RIA/825182/EU/Ready-to-use Multilingual Linked Language Data for Knowledge Services across Sectors/Pret-a-LLODen_IE
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