Perceived and Intended Sarcasm Detection with Graph Attention Networks (2021)

Abstract: Existing sarcasm detection systems focus on exploiting linguistic markers, context, or user-level priors. However, social studies suggest that the relationship between the author and the audience can be equally relevant for the sarcasm usage and interpretation. In this work, we propose a framework jointly leveraging (1) a user context from their historical tweets together with (2) the social information from a user’s neighborhood in an interaction graph, to contextualize the interpretation of the post. We distinguish between perceived and self-reported sarcasm identification. We use graph attention networks (GAT) over users and tweets in a conversation thread, combined with various dense user history representations. Apart from achieving state-of-the-art results on the recently published dataset of 19k Twitter users with 30K labeled tweets, adding 10M unlabeled tweets as context, our experiments indicate that the graph network contributes to interpreting the sarcastic intentions of the author more than to predicting the sarcasm perception by others.

## introduction sarcasm is a form of non-literal language, in which the intended meaning of the utterance differs from the literal meaning, fulfilling a social function in a discourse @xcite . sarcasm detection poses a challenge for numerous nlp tasks, such as sentiment or stance prediction @xcite . early sarcasm detection systems are based on lexical and syntactic cues @xcite @xcite @xcite . however, sarcasm interpretation requires context, even for humans @xcite . more recent works hence incorporate discourse information such as contrast @xcite @xcite , and contextualize the post by using features from user history @xcite @xcite . the relationship between an author and the audience has been given comparably less attention, despite its relevance for the sarcasm interpretation @xcite @xcite @xcite . in this work, we propose a graph neural network framework jointly leveraging a user context from their historical tweets together with the social information from a user's neighborhood modeled by heterogeneous graph structures. the key contributions of this paper are: leveraging user network an emerging line of research makes use of social interactions to encode information about the user induced by neural architecture @xcite . network information improves performance on detecting cyberbullying @xcite , abusive language use @xcite , suicide ideation @xcite or fake news @xcite . to the best of our knowledge, no graph networks have been used in the sarcasm detection task so far. perceived and intended sarcasm perceiving sarcasm in text is not trivial even for humans, not only due to the lack of acoustic markers @xcite but also due to the sociocultural diversity @xcite where in many cases the audience may misinterpret a sarcastic statement as sincere. this has been only recently reflected in sarcasm detection models @xcite . ## results and analysis our proposed gat base model significantly outperforms all the baselines ( in addition, we replace user2vec with gat embeddings tuned on user-only social graph, and we achieve 6.1% improvement on bert and 3% over 'bert + user2vec', presumably thanks to exploiting the homophily relations between users. finally, applying gat on the full heterogeneous user and tweet graph (as per figure 2 ) provides a large performance boost thanks to incorporating the conversational thread context between tweets. ## conclusions in this work, we explore social networks of user interactions, and contextual information to interpret sarcastic intentions in social media. we propose a graph attention-based model, which combines contextual information of users, linguistic features, and social networks. the heterogeneous social network modeling dynamically exploits relationships between users and tweets in a conversation and significantly improves the state-of-the-art results. in addition, any user-augmented classification efforts risk invoking stereotyping and essentialism, as the algorithm may lean towards label people rather than posts (e.g. "this is a sarcastic person"). such stereotypes can cause harm even if they are accurate on average differences @xcite . these can be emphasized by the semblance of objectivity created by the use of a computer algorithm @xcite . it is important to be mindful of these effects when interpreting the model results in an own end-application context.