A Multi-Modal Method for Satire Detection using Textual and Visual Cues

Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions

“…Satire is defined in the Cambridge Dictionary as "a way of criticizing people or ideas in a humorous way". 4 It is a liter-ary device that writers use to mock or ridicule a person, group, or ideology by judging them for various issues, particularly in the context of contemporary politics and other topical issues [109]. Such devices include humor, irony, sarcasm, exaggerations, parody, or caricature [110].…”

“…These are typically applied to news and social media posts and the purpose is not to cause harm but to ridicule, or expose behavior that is shameful, corrupt, or otherwise "bad" [111,112,113]. Even the intention is not to mislead, however, the content can be mistaken by the reader as legitimate news, which can lead to the spread of misinformation [109,114] and can be harmful [115]. Hence, it became essential to automatically identify them at a large scale and the typical classification task was to differentiate between real, fake, or satire news [112,109].…”

“…Even the intention is not to mislead, however, the content can be mistaken by the reader as legitimate news, which can lead to the spread of misinformation [109,114] and can be harmful [115]. Hence, it became essential to automatically identify them at a large scale and the typical classification task was to differentiate between real, fake, or satire news [112,109].…”

“…To train the model typical features include entity mentions [116] coherence information [117], distributions of parts-of-speech, sentiment, and exaggerations [118]. Other approaches include neural network and attention mechanism to incorporate paragraphlevel linguistic features [115], hierarchical deep neural network to incorporate sentence level and at the document level [119] and multimodal learning [109] using state-of-the-art visionlinguistic model ViLBERT.…”