  In this study, the authors present a novel methodology adept at decoding
multilingual topic dynamics and identifying communication trends during crises.
We focus on dialogues within Tunisian social networks during the Coronavirus
Pandemic and other notable themes like sports and politics. We start by
aggregating a varied multilingual corpus of comments relevant to these
subjects. This dataset undergoes rigorous refinement during data preprocessing.
We then introduce our No-English-to-English Machine Translation approach to
handle linguistic differences. Empirical tests of this method showed high
accuracy and F1 scores, highlighting its suitability for linguistically
coherent tasks. Delving deeper, advanced modeling techniques, specifically LDA
and HDP models are employed to extract pertinent topics from the translated
content. This leads to applying ARIMA time series analysis to decode evolving
topic trends. Applying our method to a multilingual Tunisian dataset, we
effectively identified key topics mirroring public sentiment. Such insights
prove vital for organizations and governments striving to understand public
perspectives during crises. Compared to standard approaches, our model
outperforms, as confirmed by metrics like Coherence Score, U-mass, and Topic
Coherence. Additionally, an in-depth assessment of the identified topics
revealed notable thematic shifts in discussions, with our trends identification
indicating impressive accuracy, backed by RMSE-based analysis.
