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Social media discourse from US politicians frequently consists of 'seeminglysimilar language used by opposing sides of the political spectrum'. But often,it translates to starkly contrasting real-world actions. For instance, "We needto keep our students safe from mass shootings" may signal either "armingteachers to stop the shooter" or "banning guns to reduce mass shootings"depending on who says it and their political stance on the issue. In thispaper, we define and characterize the context that is required to fullyunderstand such ambiguous statements in a computational setting and ground themin real-world entities, actions, and attitudes. To that end, we propose twochallenging datasets that require an understanding of the real-world context ofthe text to be solved effectively. We benchmark these datasets againstbaselines built upon large pre-trained models such as BERT, RoBERTa, GPT-3,etc. Additionally, we develop and benchmark more structured baselines buildingupon existing 'Discourse Contextualization Framework' and 'Political ActorRepresentation' models. We perform analysis of the datasets and baselinepredictions to obtain further insights into the pragmatic languageunderstanding challenges posed by the proposed social grounding tasks.

"We Demand Justice!": Towards Grounding Political Text in Social Context