Missing cause bias is a specific type of bias in media reporting that relies on consistently omitting causal attribution to specific events, for example when omitting specific actors as causes of incidents. Identifying these patterns in news outlets can be helpful in assessing the level of bias present in media content. In this paper, we examine the prevalence of this bias in reporting on Palestine by identifying causal constructions in headlines. We compare headlines from three main news media outlets: CNN, the BBC, and AJ (AlJazeera), that cover the Israel-Palestine conflict. We also collect and compare these findings to data related to the Ukraine-Russia war to analyze editorial style within press organizations. We annotate a subset of this data and evaluate two causal language models (UniCausal and GPT-4o) for the identification and extraction of causal language in news headlines. Using the top performing model, GPT-4o, we machine annotate the full corpus and analyze missing bias prevalence within and across news organizations. Our findings reveal that BBC headlines tend to avoid directly attributing causality to Israel for the violence in Gaza, both when compared to other news outlets, and to its own reporting on other conflicts.

The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hypocrisy accusation detection is an understudied tool, most often defined as a smaller subtask of fallacious argument detection. In this paper, we define hypocrisy accusation detection as an independent task in NLP, and identify different relevant subtypes of hypocrisy accusations. Our Climate Hypocrisy Accusation Corpus (CHAC) consists of 420 Reddit climate debate comments, expert-annotated into two different types of hypocrisy accusations: personal versus political hypocrisy. We evaluate few-shot in-context learning with 6 shots and 3 instruction-tuned Large Language Models (LLMs) for detecting hypocrisy accusations in this dataset. Results indicate that the GPT-4o and Llama-3 models in particular show promise in detecting hypocrisy accusations (F1 reaching 0.68, while previous work shows F1 of 0.44). However, context matters for a complex semantic concept such as hypocrisy accusations, and we find models struggle especially at identifying political hypocrisy accusations compared to personal moral hypocrisy. Our study contributes new insights in hypocrisy detection and climate change discourse, and is a stepping stone for large-scale analysis of hypocrisy accusation in online climate debates.

In this paper, we present PolitiCAUSE, a new corpus of political texts annotated for causality. We provide a detailed and robust annotation scheme for annotating two types of information: (1) whether a sentence contains a causal relation or not, and (2) the spans of text that correspond to the cause and effect components of the causal relation. We also provide statistics and analysis of the corpus, and outline the difficulties and limitations of the task. Finally, we test out two transformer-based classification models on our dataset as a form of evaluation. The models achieve a moderate performance on the dataset, with a MCC score of 0.62. Our results show that PolitiCAUSE is a valuable resource for studying causality in texts, especially in the domain of political discourse, and that there is still room for improvement in developing more accurate and robust methods for this problem.