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This study examines the lexical and grammatical meanings of news headlines from the November 2025 edition of detik.com, posted on Instagram. The shift from a news site to a social media platform often necessitates adjustments to headlines, which can lead to shifts in meaning. To collect relevant headlines, this study employed a qualitative descriptive method with a listening and note-taking technique. The analysis shows that lexical meaning in headlines is evident through the use of words with basic dictionary meanings, such as names of people, places, objects, and actions. Grammatical meaning is discovered through affixation and reduplication, which can change or add to the meaning of basic words according to the context. These two types of meaning play a crucial role in creating concise, clear headlines that are appropriate for the way news is presented on Instagram. This study concludes that the formation of meaning in headlines on detik.com is influenced by a combination of lexical and grammatical meanings tailored to the communication needs of social media platforms.

The proliferation of online gambling promotional comments on Indonesian social media has become a serious issue requiring fast and accurate automated handling. This study aims to implement a Hybrid Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) method to classify online gambling comments and compare its performance with standalone RNN and LSTM models. The research utilized a dataset of 10,230 comments subjected to comprehensive preprocessing stages, including the normalization of non-standard language using a slang dictionary. Testing was conducted across three data-splitting scenarios: 90:10, 80:20, and 70:30. Experimental results demonstrate that the standalone LSTM model achieved the highest average accuracy of 97.45%. However, the Hybrid RNN–LSTM model showed significant superiority in terms of performance stability, yielding the lowest standard deviation (0.0027) and the smallest Coefficient of Variation (0.28%) across all scenarios. These findings indicate that while the LSTM architecture is highly effective at capturing short-text context, the Hybrid approach provides better robustness against fluctuations in data proportions, making it highly relevant for implementation as an automated detection system on social media.