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Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation

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Universitat Politècnica de Catalunya
Universitat Politècnica de Catalunya

This work addresses the challenge of hate speech detection in Internet memes, and attempts using visual information to automatically detect hate speech, unlike any previous work of our knowledge. Memes are pixel-based multimedia documents that contain photos or illustrations together with phrases which, when combined, usually adopt a funny meaning. However, hate memes are also used to spread hate through social networks, so their automatic detection would help reduce their harmful societal impact. Our results indicate that the model can learn to detect some of the memes, but that the task is far from being solved with this simple architecture. While previous work focuses on linguistic hate speech, our experiments indicate how the visual modality can be much more informative for hate speech detection than the linguistic one in memes. In our experiments, we built a dataset of 5,020 memes to train and evaluate a multi-layer perceptron over the visual and language representations, whether independently or fused. https://github.com/imatge-upc/hate-speech-detection

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@DocXavi Hate Speech in Pixels Detection of Offensive Memes towards Automatic Moderation Xavier Giro Benet Oriol Cristian Canton
@DocXavi 2 Photo of Torre Glòries by Barcelona Turisme
@DocXavi 3
@DocXavi 4
@DocXavi 5 Hate Memes (racist) Classic style meme Modern style meme
@DocXavi 6 UN Sustainable Development Goals
@DocXavi 7 Motivation Our system X
@DocXavi 8 Hate Speech in Language Most (all ?) previous efforts focused on language only. Paula Fortuna, Sergio Nunes, “A Survey on Automatic Detection of Hate Speech in Text”. ACM Comput. Surv 2018.
@DocXavi 9 Our system focuses on a deep learning approach. Paula Fortuna, Sergio Nunes, “A Survey on Automatic Detection of Hate Speech in Text”. ACM Comput. Surv 2018. Hate Speech in Language
@DocXavi 10 Some language patterns clearly correlate with hate speech: Silva, Leandro, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. "Analyzing the targets of hate in online social media." In Tenth International AAAI Conference on Web and Social Media. 2016. Hate Speech in Language
@DocXavi 11 Hate Speech in Memes Language is not enough to understand memes. Our system X
@DocXavi 12 Hate Speech in Memes Language is not enough to understand memes. Our system X
@DocXavi 13 Context: Pixel Moderation Computer vision tools are already deployed for content moderation: ● Nudity ● Pornography ● Violence ● ... João Macedo,, Filipe Costa, Jefersson A. dos Santos, “A Benchmark Methodology for Child Pornography Detection” SINGRAPI 2018.
@DocXavi 14 Context: Study case Our experiments focus on racist memes. [1] Silva, Leandro, Mainack Mondal, Denzil Correa, Fabrício Benevenuto, and Ingmar Weber. "Analyzing the targets of hate in online social media." In Tenth International AAAI Conference on Web and Social Media. 2016. [2] Paula Fortuna, Sergio Nunes, “A Survey on Automatic Detection of Hate Speech in Text”. ACM Comput. Surv 2018.
@DocXavi 15 Related Work #Rosetta Fedor Borisyuk, Albert Gordo, Viswanath Sivakumar, “Rosetta: Large scale system for text detection and recognition in images”. KDD 2018. [blog]
@DocXavi 16 Related Work (concurrent) #MMHS150K Raul Gomez, Jaume Gibert, Lluis Gomez, Dimosthenis Karatzas, “Exploring Hate Speech Detection in Multimodal Publications”. WACV2020 (to be presented)
@DocXavi 17 Deep Neural Architecture MLP Concat X
@DocXavi 18 Deep Neural Architecture OCR Extraction Pytessteract (Tesseract 4.0.0) Text Feature Extraction BERT: bert-base-multilingual-cased. This version has 12 layers, 768 hidden dimensions, 12 attention heads with a total of 110M parameters and is trained on 104 languages. Frozen weights. Feature size = 768. Visual Feature Extraction VGG-16. Pretrained on ImageNet for input images of 224x224 Frozen weights. Feature size = 4096. Hate Speech Detector Multi Layer Perceptron. 2 Hidden Layers of 100 dims. ReLU Activation. 1 output neuron to predict hate speech
@DocXavi 19 Dataset Collection Source Queries to Google images: ● “racist meme” ● “jew meme” ● “muslim meme” Reddit Memes dataset [*] Assumption: No hate speech memes in it. X [*] https://www.kaggle.com/sayangoswami/reddit-memes-dataset
@DocXavi 20 Dataset Partition Unbalanced dataset towards Train (85%) 1441 2825 Val (15%) 254 500 Total 1695 3325 5020 X
@DocXavi 21 MSE Loss Accuracy (All No Hate = 0.66) Language vs Vision vs Multimodal Model optimized with ADAM
@DocXavi 22 Results Accuracy on the validation dataset, not enough data to build a third test partition.
@DocXavi 23 Results All three configurations perform better than the 0.66 accuracy of a naive “All no hate” predictions.
@DocXavi 24 Results ● Visual modality is more important than language.
@DocXavi 25 Results ● Visual modality is more important than language. Hypothesis #1: Dataset bias in terms of visual style: hate speech memes follow a more classical style.
@DocXavi 26 Results ● Visual modality is more important than language. Hypothesis #2: Memes often present visual effects and sitortions that damage the OCR performance.
@DocXavi 27 Results ● Visual modality is more important than language. ● Small improvement when combining vision and language.
@DocXavi 28 Results ● Visual modality is more important than language. ● Small improvement when combining vision and language. Hypothesis #3: Because of the larger dimensionality of the representations: 4096 (vision) vs 784 (language). MLP Concat X
@DocXavi 29 Conclusions ● Hate speech detection with naive ImageNet-Bert-like feature fusion: ○ Can capture some valuable patterns… ■ 83% accuracy improves over the 66% baseline. ○ ...but does not fully solve the challenge. ■ Gain of 17%, but still another 17% before 100%.
@DocXavi 30 Conclusions ● Challenging & attractive scientific task with high societal impact. ● Next steps: ○ Larger & cleaner dataset (MMHS150K ?). ○ Inspiration from other multimodal understanding tasks (eg. visual Q&A, visual dialog…) ○ Knowledge bases (history, breaking news, demographics…)
31http://deeplearning.barcelona Cristian Canton Benet Oriol Raul Gomez LLuis Gómez
32http://deeplearning.barcelona Oriol Vinyals Àgata Lapedriza Marta R. Costa-jussà Aleix Martínez
@DocXavi 33 bit.ly/hatepixels Travel grant by:
@DocXavi 34 bit.ly/hatepixels

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