Manual and automatic verification of the trustworthiness of information is an important task. Knowing whether the author of a statement was an eyewitness to the reported event(s) is a useful clue. In linguistics, such information is expressed through “evidentiality”. Evidentials are especially important in Bulgarian, as Bulgarian journalists often use a specific type of evidential (“renarrative”) to report events that they did not directly observe, nor verify. Unfortunately, there are no automatic tools to detect Bulgarian renarrative. This article presents the first two automatic solutions for this task. Specifically - a fine-tuned BERT classifier (renarrative BERT detector, BGRenBERT), achieving 0.98 Accuracy on the test split, and a renarrative rulebased detector (BGRenRules), created with regular expressions, matching a parser’s output. Both solutions detect Bulgarian texts containing the most frequently encountered forms of renarrative. Additionally, we compare the results of the two detectors with the manual annotation of subsets of two Bulgarian fake text datasets. BGRenRules obtains substantially higher results than BGRenBERT. The error analysis shows that the errors from BGRenRules most frequently correspond to cases in which humans also have doubts. The training dataset (BgRenData), the annotated dataset subsets, and the two detectors are made publicly accessible on Zenodo, GitHub, and HuggingFace. We expect that these new resources will be of invaluable assistance to 1) Bulgarian-language researchers, 2) researchers of other languages with similar phenomena, especially those working on verifying information.

Readability assessment plays a crucial role in education and text accessibility. While numerous indices exist for English and have been extended to Romance and Slavic languages, Bulgarian remains under- served in this regard. This paper reviews established readability metrics across these language families, examining their underlying features and modelling methods. We then report the first attempt to develop a readability index for Bulgarian, using end-of-school-year assessment questions and literary works targeted at children of various ages. Key linguistic attributes, namely, word length, sentence length, syllable count, and information content (based on word frequency), were extracted, and their first two statistical moments, mean and variance, were modelled against grade levels using linear and polynomial regression. Results suggest that polynomial models outperform linear ones by capturing non-linear relationships between textual features and perceived difficulty, but may be harder to interpret. This work provides an initial framework for building a reliable readability measure for Bulgarian, with applications in educational text design, adaptive learning, and corpus annotation.

This article introduces SM-FEEL-BG – the first Bulgarian-language package, containing 6 datasets with Social Media (SM) texts with emotion, feeling, and sentiment labels and 4 classifiers trained on them. All but one dataset from these are freely accessible for research purposes. The largest dataset contains 6000 Twitter, Telegram, and Facebook texts, manually annotated with 21 fine-grained emotion/feeling categories. The fine-grained labels are automatically merged into three coarse-grained sentiment categories, producing a dataset with two parallel sets of labels. Several classification experiments are run on different subsets of the fine-grained categories and their respective sentiment labels with a Bulgarian fine-tuned BERT. The highest Acc. reached was 0.61 for 16 emotions and 0.70 for 11 emotions (incl. 310 ChatGPT 4-generated texts). The sentiments Acc. of the 11 emotions dataset was also the highest (0.79). As Facebook posts cannot be shared, we ran experiments on the Twitter and Telegram subset of the 11 emotions dataset, obtaining 0.73 Acc. for emotions and 0.80 for sentiments. The article describes the annotation procedures, guidelines, experiments, and results. We believe that this package will be of significant benefit to researchers working on emotion detection and sentiment analysis in Bulgarian.