1 code implementation • 9 May 2022 • Shivam Sharma, Firoj Alam, Md. Shad Akhtar, Dimitar Dimitrov, Giovanni Da San Martino, Hamed Firooz, Alon Halevy, Fabrizio Silvestri, Preslav Nakov, Tanmoy Chakraborty
One interesting finding is that many types of harmful memes are not really studied, e. g., such featuring self-harm and extremism, partly due to the lack of suitable datasets.
In this work, we propose two novel problem formulations: detecting harmful memes and the social entities that these harmful memes target.
We focus on two tasks: (i)detecting harmful memes, and (ii)identifying the social entities they target.
We further create and release a new corpus of 950 memes, carefully annotated with 22 propaganda techniques, which can appear in the text, in the image, or in both.
We describe SemEval-2021 task 6 on Detection of Persuasion Techniques in Texts and Images: the data, the annotation guidelines, the evaluation setup, the results, and the participating systems.
Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning and natural language processing methods.
Semantic Web technologies offer the prospect of significantly reducing the amount of effort required to integrate existing enterprise functionality in support of new composite processes; whether within a given organization or across multiple ones.
We present a novel approach for training neural abstract architectures which in- corporates (partial) supervision over the machine’s interpretable components.