Verbal-visual evaluation in disaster reporting: Attitudinal analyses of hard news in The Jakarta Post - Lina Meilinda; Yulizar Komarawan; Ilma Yullinda Rahmah (JOALL (Journal of Applied Linguistics and Literature), 2026)
Disaster news helps the public understand crisis events and is often presented as factual and objective because it is classified as hard news. However, disaster reports still contain evaluative elements that shape how events and social actors are perceived. These evaluations are expressed through both language and news photographs. This study examines how such evaluations are constructed in disaster reports from The Jakarta Post through the interaction between written texts and accompanying news photographs. The study draws on Appraisal Theory, as proposed by White and Martin within the framework of Systemic Functional Linguistics. It analyses attitude resources in hard news reporting, realised through verbal and visual modes, and their intersemiotic relations. The data consist of eleven hard news articles, purposively selected 2024 that meet specific inclusion criteria. Both verbal and visual elements were analysed qualitatively, supported by simple frequency counts to identify dominant patterns. The findings show 185 attitude instances distributed across three types. Negative appreciation is the most common (47%), followed by negative affect (24%). These are mainly used to show physical damage, loss, and human suffering. In contrast, positive judgment makes up 29% of the data and is used to present institutions, such as government agencies and rescue teams, as responsible and effective. News photographs support these meanings by showing destruction, danger, and humanitarian action. Across text and images, repetition and extension strengthen evaluative meanings and create a consistent viewpoint. The study shows that disaster reporting is evaluative and multimodal. Therefore, it highlights the need for critical multimodal literacy to understand how meaning is constructed through language and images.