Generalisations are common in scientific communication, even though they are semantically ambiguous. An automated method is needed to identify and categorise claims according to their level of generalisation, in order help detect an over-reliance on generalisations and possible misrepresentations of scientific findings. We introduce a comprehensive taxonomy of generalisations in the scientific domain, NLPGENX, which labels claims according to their level of generality and framing within the text. We operationalise this taxonomy with an LLMpowered framework, NLPGENA, that automatically classifies sentences from scientific articles into 5 different generalisation classes. We validate our framework with human annotators and use the framework to construct a large-scale dataset of NLP papers annotated according to generality, with auxiliary labels for hedging and vague descriptors (NLPGENS). We use NLPGENS to analyse the use of generalisations in NLP papers across multiple venues and subdomains, and to examine associations with citation counts, hedging, and vague descriptors.

Misinformation detection models degrade in performance over time, but the precise causes of this remain under-researched, in particular for multimodal models. We present experiments investigating the impact of temporal shift on performance of multimodal automatic misinformation detection classifiers. Working with the r/Fakeddit dataset, we found that evaluating models on temporally out-of-domain data (i.e. data from time stretches unseen in training) results in a non-linear, 7-8% drop in macro F1 as compared to traditional evaluation strategies (which do not control for the effect of content change over time). Focusing on two factors that make temporal generalizability in misinformation detection difficult, content shift and class distribution shift, we found that content shift has a stronger effect on recall. Within the context of coarse-grained vs. fine-grained misinformation detection with r/Fakeddit, we find that certain misinformation classes seem to be more stable with respect to content shift (e.g. Manipulated and Misleading Content). Our results indicate that future research efforts need to explicitly account for the temporal nature of misinformation to ensure that experiments reflect expected real-world performance.