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Register a new userRacist or Sexist Meme? Classifying Memes beyond Hateful
عنصر عنصري أو جنسي؟تصنيف الميمات وراء البغيض
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الميمات هي مجموعات من النص والصور التي غالبا ما تكون روح الدعابة في الطبيعة.ولكن، قد لا يكون هذا هو الحال دائما، وقد تصور مجموعات معينة من النصوص والصور الكراهية، يشار إليها باسم الميمات البغيضة.يقدم هذا العمل خط أنابيب متعدد الوسائط يأخذ كل من الميزات المرئية والنصية من الميمات إلى (1) تحديد الفئة المحمية (على سبيل المثال، الجنس، الجنس وما إلى ذلك) التي هاجمت؛و (2) اكتشاف نوع الهجوم (E.G. ازدراء، Slurs وما إلى ذلك).يستخدم خط أنابيبنا تمثيلا مرئيا ومرئيا تدريبا مسبقا مسبقا، متبوعا بتصنيف الانحدار اللوجستي البسيط.نحن نوظف خط أنابيبنا على مجموعة بيانات تحدي الميمات البغيضة مع ملصقات إضافية تم إنشاؤها حديثا عن الفئة المحمية ونوع الهجوم.يحقق أفضل نموذج لدينا AUROC من 0.96 لتحديد الفئة المحمية، و 0.97 للكشف عن نوع الهجوم.نطلق سرد علاماتنا في https://github.com/harisbinzia/hatefulmemes
Memes are the combinations of text and images that are often humorous in nature. But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes. This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g. race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g. contempt, slurs etc.). Our pipeline uses state-of-the-art pre-trained visual and textual representations, followed by a simple logistic regression classifier. We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack. Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack. We release our code at https://github.com/harisbinzia/HatefulMemes
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The Shared Task on Hateful Memes is a challenge that aims at the detection of hateful content in memes by inviting the implementation of systems that understand memes, potentially by combining image and textual information. The challenge consists of
three detection tasks: hate, protected category and attack type. The first is a binary classification task, while the other two are multi-label classification tasks. Our participation included a text-based BERT baseline (TxtBERT), the same but adding information from the image (ImgBERT), and neural retrieval approaches. We also experimented with retrieval augmented classification models. We found that an ensemble of TxtBERT and ImgBERT achieves the best performance in terms of ROC AUC score in two out of the three tasks on our development set.
Hateful memes pose a unique challenge for current machine learning systems because their message is derived from both text- and visual-modalities. To this effect, Facebook released the Hateful Memes Challenge, a dataset of memes with pre-extracted te
xt captions, but it is unclear whether these synthetic examples generalize to memes in the wild'. In this paper, we collect hateful and non-hateful memes from Pinterest to evaluate out-of-sample performance on models pre-trained on the Facebook dataset. We find that memes in the wild' differ in two key aspects: 1) Captions must be extracted via OCR, injecting noise and diminishing performance of multimodal models, and 2) Memes are more diverse than traditional memes', including screenshots of conversations or text on a plain background. This paper thus serves as a reality-check for the current benchmark of hateful meme detection and its applicability for detecting real world hate.
This paper describes our submission (winning solution for Task A) to the Shared Task on Hateful Meme Detection at WOAH 2021. We build our system on top of a state-of-the-art system for binary hateful meme classification that already uses image tags s
uch as race, gender, and web entities. We add further metadata such as emotions and experiment with data augmentation techniques, as hateful instances are underrepresented in the data set.
We present the results and main findings of the shared task at WOAH 5 on hateful memes detection. The task include two subtasks relating to distinct challenges in the fine-grained detection of hateful memes: (1) the protected category attacked by the
meme and (2) the attack type. 3 teams submitted system description papers. This shared task builds on the hateful memes detection task created by Facebook AI Research in 2020.
An abundance of methodological work aims to detect hateful and racist language in text. However, these tools are hampered by problems like low annotator agreement and remain largely disconnected from theoretical work on race and racism in the social
sciences. Using annotations of 5188 tweets from 291 annotators, we investigate how annotator perceptions of racism in tweets vary by annotator racial identity and two text features of the tweets: relevant keywords and latent topics identified through structural topic modeling. We provide a descriptive summary of our data and estimate a series of generalized linear models to determine if annotator racial identity and our 12 latent topics, alone or in combination, explain the way racial sentiment was annotated, net of relevant annotator characteristics and tweet features. Our results show that White and non-White annotators exhibit significant differences in ratings when reading tweets with high prevalence of particular, racially-charged topics. We conclude by suggesting how future methodological work can draw on our results and further incorporate social science theory into analyses.
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