Sarcasm Detection on Twitter: A Behavioral Modeling Approach

By Ashwin Rajadesingan, Reza Zafarani, Huan Liu. Web Search and Data Mining, 2015

429 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media (2018, background): “Keywords are also prone to missing sarcasm and forms of humor, as these genres of language are particularly challenging to classify (Rajadesingan et al. 2015).”
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (2019, background): “A variety of contextual features have been explored, including speaker’s background and behavior in online platforms (Rajadesingan et al., 2015), embeddings of expressed sentiment and speaker’s personality traits (Poria et al., 2016), learning of user-speciﬁc representations (Wallace et al., 2016;…”
Contextualized Sarcasm Detection on Twitter (2015, methodology): “On Twitter, modeling the relationship between a tweet and an author's past tweets can improve accuracy on this task (Rajadesingan, Zafarani, and Liu 2015).”
A Unified Deep Learning Architecture for Abuse Detection (2018, background): “In the dataset by [31], tweets are characterized as sarcastic or nonsarcastic.”
Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases (2021, background): “Despite wide use (Sood et al., 2012; Mondal et al., 2017; Simonite, 2021) they can be at a significant mismatch with human ratings (Davidson et al., 2017b) and can’t easily capture phenomenons such as sarcasm, humor (Rajadesingan et al., 2015) or polysemy (Sahlgren et al., 2018).”