Formal and Multimodal Approach to Hard News as Genre, Structure and Metalanguage in Social and Digital Media Contexts. The Example of Twitter

ABSTRACT The goal of this paper is to improve heuristics for hard news discourse by proposing a cognitive model of abstraction, regarding social media contexts. To this end, hard news is discussed as a genre, structure and metalanguage, under the formal definition of a semiotic mode. Annotation is a successful technology to control the effects of genre operations, reveal relations, and to inquire about data and documents. It proceeds to characterize Twitter’s interface, in terms of formal and material regularities, employed recursively. It further demonstrates the formalization of a semantics for hard news discourse in the so-called logical forms, which has been adapted from analytical tools developed by the Segmented Theory of Discourse Representation (SDRT) in order to examine coherence in different levels of detail. Finally, the implication of this approach as a discipline is discussed, regarding the production of transversal knowledge aiming at digital literacy, which urges in present times.1

摘要 本文针对社交媒体语境下的硬新闻语篇（hard news discourse），通过提出抽象认知模型优化其启发式分析方法。为此，本文将硬新闻置于符号模态（semiotic mode）的形式化定义之下，从文体、结构与元语言三个维度展开探讨。标注（Annotation）是一项成熟的技术，可用于管控文体操作的效果、揭示语义关联，并实现对数据与文档的探究。本文继而以形式与物质层面的规律性为依据，采用递归分析方法对推特（Twitter）的界面特征进行刻画。此外，本文还针对硬新闻语篇的语义展开形式化建模，采用所谓的逻辑形式表征；该建模方法借鉴了语篇表征分段理论（Segmented Theory of Discourse Representation, SDRT）所开发的分析工具，用以在不同细节层级上考察语篇的连贯性。最后，本文探讨了该研究路径作为一门学科的意蕴，其聚焦于旨在提升数字素养（digital literacy）的跨领域知识生产，而这一目标在当下极具紧迫性。