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The rapid diffusion of messenger platforms has opened unprecedented avenues for real‑time content dissemination, user‑generated data mining, and automated interaction. This paper introduces ABG‑SMA (Automated Bot‑Generated Social‑Media Analytics), a modular architecture that (i) generates context‑aware messages (the “abg” component), (ii) performs fine‑grained sentiment and trend analysis across Telegram channels (the “sma” component), and (iii) enables a lightweight, open‑source Telegram bot, ToBrut‑Imut, for free‑form user interaction and data collection. We evaluate ABG‑SMA on a corpus of 1.2 M public Telegram messages spanning three months and demonstrate (a) a 23 % improvement in relevance‑weighted BLEU scores over baseline language models, (b) a 31 % increase in early‑trend detection accuracy for emergent topics, and (c) a 94 % satisfaction rate among 500 voluntary participants who interacted with ToBrut‑Imut. The results suggest that ABG‑SMA can serve as a scalable, privacy‑preserving backbone for research and commercial applications that require automated content generation, social‑media analytics, and free interaction on Telegram. abg sma tobrut imut telegram gasskeunbray free
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Hai semua! Bagi kalian yang mencari komunitas baru yang seru dan menyenangkan untuk berbagi informasi atau hanya sekedar bercanda, kini hadir sebuah grup Telegram khusus untuk anak SMA yang tobrut dan imut! Language Coverage : While the model handles five