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Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text

Main Authors: Shardul Suryawanshi, Bharathi Raja Chakravarthi, Mihael Arcan, Paul Buitelaar
Format: Proceeding Journal
Bahasa: eng
Terbitan: , 2020
Subjects:
multimodal data
classification
memes
offensive content
opinion mining
Online Access: https://zenodo.org/record/3899868
ctrlnum 3899868
fullrecord <?xml version="1.0"?> <dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><creator>Shardul Suryawanshi</creator><creator>Bharathi Raja Chakravarthi</creator><creator>Mihael Arcan</creator><creator>Paul Buitelaar</creator><date>2020-05-11</date><description>A 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-Text</description><identifier>https://zenodo.org/record/3899868</identifier><identifier>10.5281/zenodo.3899868</identifier><identifier>oai:zenodo.org:3899868</identifier><language>eng</language><relation>doi:10.5281/zenodo.3899867</relation><rights>info:eu-repo/semantics/openAccess</rights><rights>https://creativecommons.org/licenses/by/4.0/legalcode</rights><subject>multimodal data, classification, memes, offensive content, opinion mining</subject><title>Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text</title><type>Journal:Proceeding</type><type>Journal:Proceeding</type><recordID>3899868</recordID></dc>
language eng
format Journal:Proceeding
Journal
Journal:Journal
author Shardul Suryawanshi
Bharathi Raja Chakravarthi
Mihael Arcan
Paul Buitelaar
title Multimodal Meme Dataset (MultiOFF) for Identifying Offensive Content in Image and Text
publishDate 2020
topic multimodal data
classification
memes
offensive content
opinion mining
url https://zenodo.org/record/3899868
contents A 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-Text
id IOS16997.3899868
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library Cognizance Journal of Multidisciplinary Studies
library_id 5267
collection Cognizance Journal of Multidisciplinary Studies
repository_id 16997
subject_area Multidisciplinary
city Stockholm
province INTERNASIONAL
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first_indexed 2022-06-06T05:29:11Z
last_indexed 2022-06-06T05:29:11Z
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