This paper presents a corpus study that extends and generalises an existing annotation model which integrates functional content descriptions delivered via text, pictures and interactive components. The model is used to describe a new corpus with 20 online vegan recipe blogs in terms of their Attractiveness for at least two types of readers: vegan readers and readers interested in a vegan lifestyle. Arguably, these readers value a blog that shows that the target dish is Easy to Make which can be inferred from the number of ingredients, procedural steps and visualised actions, according to an Easy to Read cooking instruction that displays a coherent use of verbal and visual modalities presenting processes and results of the cooking actions involved. Moreover, added value may be attributed to invitations to Engage with the blog content and functionality through which information about the recipe, the author, diet and nutrition can be accessed. Thus, the corpus study merges generalisable annotations of verbal, visual and interaction phenomena to capture the Attractiveness of online vegan recipe blogs to inform reader and user studies and ultimately offer guidelines for authoring effective online multimodal instructions.

This paper presents a tool to investigate the design of multimodal instructions (MIs), i.e., instructions that contain both text and pictures. The benefit of including pictures in information presentation has been established, but the characteristics of those pictures and of their textual counterparts and the rela-tion(s) between them have not been researched in a systematic manner. We present the PAT Work-bench, a tool to store, annotate and retrieve MIs based on a validated coding scheme with currently 42 categories that describe instructions in terms of textual features, pictorial elements, and relations be-tween text and pictures. We describe how the PAT Workbench facilitates collaborative annotation and inter-annotator agreement calculation. Future work on the tool includes expanding its functionality and usability by (i) making the MI annotation scheme dynamic for adding relevant features based on empirical evaluations of the MIs, (ii) implementing algorithms for automatic tagging of MI features, and (iii) implementing automatic MI evaluation algorithms based on results obtained via e.g. crowdsourced assessments of MIs.