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Emotion and Information Disorder in NLP: A Systematic Mapping and Benchmark Blueprint

Online misinformation research in NLP has expanded rapidly, including approaches that model affective signals such as sentiment, discrete emotions, and emotion dynamics. However, the Information Disorder framework distinguishes misinformation, disinformation, and malinformation along dimensions of intention, harm, and contextual dependence, which are rarely operationalised in current datasets, tasks, and evaluation protocols. We provide a systematic mapping of 82 studies at the intersection of Information Disorder and emotion-aware NLP (51 model papers, 7 dataset papers, 24 survey/theory papers). Across empirical works (58), veracity-centric supervision dominates (72.4% binary labels), while explicit intention and harm variables appear in only 1.7% each. Evaluation relies mostly on random splits (79.3%), limiting robustness to source and temporal shifts. Emotion is represented in 43.1% of model papers, mostly as static features, with emotion dynamics and audience emotion rare. Based on these findings, we propose an operational taxonomy aligned with Information Disorder and a benchmark blueprint specifying tasks, annotation variables, split strategies, and evaluation protocols to support theory-grounded, comparable progress.