The sixth workshop on Resources for African Indigenous Languages (RAIL) was held on 10 November 2025 at the CSIR International Convention Centre in Pretoria, South Africa. It was co-located with the Digital Humanities Association of Southern Africa (DHASA) 2025 conference, which took place from 11 to 14 November 2025

Folktales are literary forms that reveal the soul of any society; they express its wishes, desires, hopes, and beliefs about the world. They have fictional characters and situations, mostly oral traditions, before they were written down. According to Cynthia McDaniel (1993), folktales can be used in all disciplines to convey knowledge and communicate ideas; they serve as an inherent vehicle for intergenerational communication that prepares and assigns roles and responsibilities to different generations in their communities. They are more pedagogic devices and less literary pieces. They cultivate universal values such as compassion, generosity, and honesty while disapproving of attributes such as cruelty, greed, and dishonesty. To illustrate McDaniel's claims, this paper will firstly use the ideational metafunctional framework found in Systemic Functional Linguistics, which expresses the clausal experiences and content from a grammatical perspective, coupled with syntagmatic analysis, which describes the text (folktale) in chronological order as reported by the storyteller. Secondly, the presentation will use a textual metafunctional framework that fulfills the thematic function of the clause, coupled with the paradigmatic analysis where the folkloristic text's patterns are regrouped more analytically to reveal the text's latent content, or theme. The Voyant Tool, a web-based text reading and analysis environment designed to facilitate the analysis of various text formats, was used to extract and analyze data from a Sesotho folktale to illustrate how folktales may be integrated with technology for research and educational purposes. This paper employed a descriptive research design that incorporates qualitative (content analysis) and quantitative (statistical analysis) methodologies to analyze and interpret the story. It is observed, through the Voyant tool, that the story is built out of 191 Sesotho word formations, and through the ideational analysis, that the storyteller employed more material process types than mental process types, and lastly, with the textual interpretation, indicating the value of oral literature in our daily lives as well as the significant role folktales may play in interpreting sociopolitical events in contemporary communities.

South Africa, with its twelve official languages, is an inherently multilingual country. As such, speakers of many of the languages have been in direct contact. This has led to a cross-over of words and phrases between languages. In this article, we provide a methodology to identify words that are (potentially) borrowed from another language. We test our approach by trying to identify words that moved from English into Sesotho (or potentially the other way around). To do this, we start with a bilingual Sesotho-English dictionary (Bukantswe).
We then develop a lexicographic comparison method that takes a pair of lexical items (English and Sesotho) and computes a range of distance metrics. These distance metrics are applied to the raw words (i.e., comparing orthography), but using the Soundex algorithm, an approximate phonological comparison can be made as well. Unfortunately, Bukantswe does not contain complete annotation of loan words, so a quantitative evaluation is not currently possible. We provide a qualitative analysis of the results, which shows that many loan words can be found, but in some cases lexical items that have a high similarity are not loan words. We discuss different situations related to the influence of orthography, phonology, syllable structure, and morphology. The approach itself is language independent, so it can also be applied to other language pairs, e.g., Afrikaans and Sesotho, or more related languages, such
as isiXhosa and isiZulu.

The critical lack of structured terminological data for South Africa’s official languages hampers progress in multilingual NLP, despite the existence of numerous government and academic terminology lists. These valuable assets remain fragmented and locked in non-machine-readable formats, rendering them unusable for computational research and development. Mafoko addresses this challenge by systematically aggregating, cleaning, and standardising these scattered resources into open, interoperable datasets. We introduce the foundational Mafoko dataset, released under the equitable, Africa-centered NOODL framework. To demonstrate its immediate utility, we integrate the terminology into a Retrieval-Augmented Generation (RAG) pipeline. Experiments show substantial improvements in the accuracy and domain-specific consistency of English-to-Tshivenda machine translation for large language models. Mafoko provides a scalable foundation for developing robust and equitable NLP technologies, ensuring South Africa’s rich linguistic diversity is represented in the digital age.

Contemporary scholarship increasingly recognises the need to document the growing corpus of African literature being produced and distributed via social media and other online platforms. In African literature and the future, Ogundipe (2015) declared that: In the search for a viable path for the future of African literature, a well-crafted vision of the future and effective strategies to engender transformation are imperative. This raises the practical application of the digital space, the internet and related innovative technology as new paradigms of knowledge to African literary engagement. But the absence of a critical standard remains a bane of this development. To address this critical imperative and further explore the prevalence of such works, I collected a dataset to find examples of literary trends and key recent examples of significant works, informed by Moretti's scholarship on distant reading. The dataset focuses on poetry written by younger South African authors from the Born Free Generation, in line with my broader research. The main purpose of this paper is to present my findings and the theoretical and methodological framework that informed them. The paper concludes by briefly proposing some possible means of expanding this research and proposing a large-scale online archival project.

In times of collective discomfort and dissatisfaction, people often find solace in shared adversity on social media platforms like X (formerly known as Twitter). These platforms offer a unique window into the public’s emotions andviewpoints concerning common challenges. I n2022, South Africa experienced an electricity crisis, during which the country was subjectedto rolling blackouts, commonly known as load-shedding, by Eskom, the country’s primary electricity provider, to prevent a national electricity grid shutdown. This study conducted adata-driven exploration of the public discourse surrounding Eskom and loadshedding on X using natural language processing and data science techniques. The dataset utilised for thisstudy comprised tweets containing keywords related to Eskom and loadshedding. The studydelved into the topics of discussion by applying topic modelling techniques to uncover latent themes within the discourse. The topics were analysed through a multifaceted lens to unpack and highlight patterns within the sentiments, emotions and biases that underpin conversations related to loadshedding and Eskom. A notable inclusion in the analysis was the incorporation of sarcasm classifications,which enhanced the interpretation of the emotion and sentiment within the topics discussed.The findings uncovered from the analysis were contrasted with loadshedding-related events in 2022 to understand the public discourse as the electricity crisis escalated. The methodologyof this study provides a framework for utilising natural language processing techniques touncover and examine the perspectives of a collective within discourse related to events of shared interest.