## 4 Dataset  
We found no specifically tailored or publicly available datasets that met our experimental needs at this project stage. To address this problem, we created our own using categorized texts from the European Media Monitor (EMM) website ([[https://emm.newsbrief.eu/](https://emm.newsbrief.eu/)]([https://emm.newsbrief.eu/](https://emm.newsbrief.eu/)) (accessed on 2 May 2022)). The harvested texts span a period of nine months, from May 2022 to March 2023. We excluded articles assigned to multiple categories, yielding a dataset of over 2.2 million articles across 41 categories in 68 languages. For our experiments, we exclusively used article titles for several reasons. Crawling complete texts from websites was cost-prohibitive due to their diverse structures. Additionally, longer texts tended to introduce ambiguity, necessitating segmentation and significant reannotation efforts. We divided this dataset into three subsets based on equal time intervals, aligning with the timestamps of the articles: the first three months for training, the next for validation, and the remainder for testing. This division accounts for the evolving nature of media content; although the list of topics may remain stable, the content within these topics changes over time, which can impact the model's accuracy. Our initial experiments with the complete dataset did not meet the desired accuracy levels, achieving only approximately \(\sim\)0.341 \(\pm\) 0.03 on the testing subset, averaged over five runs. These results were obtained after training and validating with the corresponding subsets using the Language Agnostic BERT Sentence Embedding (LaBSE) [34] model as the vectorizer and an optimized Feed Forward Neural Network (FFNN) as the classifier. To identify the causes of these low results, we manually reviewed a random sample of texts in English and Lithuanian to check the accuracy of the labels assigned. This analysis highlighted two main issues: (1) incorrect assignment of titles to their corresponding topics, and (2) titles that were not sufficiently informative or were misleading, considering the articles they represent. Manually verifying and relabeling this dataset would require enormous human resources, specifically experts proficient in 68 languages. Given that such a task was infeasible within the project's scope, we automatically cleaned the dataset. For this purpose, we employed the LaBSE sentence vectorizer, using cosine similarity to compare the sentence vectors of the title with the sentence vectors of all topics. Before vectorization, the names of topics were processed by separating concatenated words with white spaces (e.g., _Energy-MarketandStrategies_\(\rightarrow\)_Energy Market and Strategies_). After comparison, we excluded titles from the dataset based on the following criteria: (1) the title must achieve a similarity score greater than 0.5 with a specific topic pre-assigned by the EMM; (2) none of the other topicscould have a cosine similarity score higher than 0.3. This process resulted in the creation of our dataset, named MM18x58 (for statistics, see Table 1), which comprises approximately 15,000 texts across 18 topics and 58 languages (listed in descending order of coverage in our dataset: ru, en, sq, de, el, bg, es, uk, hu, pl, tr, ro, fr, pt, it, bs, mk, sr, hr, lt, vi, sk, cs, ar, ja, et, lv, sv, fi, no, id, nl, az, da, sl, fa, km, ca, sw, be, ka, ky, he, ko, ku, zh, hi, th, gl, lb, lo, ml, mn, ha, bn, hy, pap, so). This dataset is now publicly available. (The MM18x58 dataset is available at [[https://github.com/novian-pro/EMM_18x58_dataset](https://github.com/novian-pro/EMM_18x58_dataset)]([https://github.com/novian-pro/EMM_18x58_dataset](https://github.com/novian-pro/EMM_18x58_dataset)) (accessed on 16 May 2024). In this reference, you can find useful statistics about the distribution of texts among different languages and splits.) Despite the evident imbalance highlighted in Table 1, we made a deliberate choice to maintain all topics in our MM18x58 dataset.