Global communication has been made easier by the emergence of online social media, but it has also made it easier for “fake news,” or information that is misleading or false, to spread. Since this phenomenon presents a significant challenge, reliable detection techniques are required to discern between authentic and fraudulent content. The primary goal of this study is to identify fake news on social media platforms and in Malayalam-language articles by using LSTM (Long Short-Term Memory) model. This research explores this approach in tackling the DravidianLangTech@EACL 2024 tasks. Using LSTM networks to differentiate between real and fake content at the comment or post level, Task 1 focuses on classifying social media text. To precisely classify the authenticity of the content, LSTM models are employed, drawing on a variety of sources such as comments on YouTube. Task 2 is dubbed the FakeDetect-Malayalam challenge, wherein Malayalam-language articles with fake news are identified and categorized using LSTM models. In order to successfully navigate the challenges of identifying false information in regional languages, we use lstm model. This algoritms seek to accurately categorize the multiple classes written in Malayalam. In Task 1, the results are encouraging. LSTM models distinguish between orignal and fake social media content with an impressive macro F1 score of 0.78 when testing. The LSTM model’s macro F1 score of 0.2393 indicates that Task 2 offers a more complex landscape. This emphasizes the persistent difficulties in LSTM-based fake news detection across various linguistic contexts and the difficulty of correctly classifying fake news within the context of the Malayalam language.

People often switch languages in conversations or written communication in order to communicate thoughts on social media platforms. The languages in texts of this type, also known as code-mixed texts, can be mixed at the sentence, word, or even sub-word level. In this paper, we address the problem of identifying language at the word level in code-mixed texts using a sequence of characters and word embedding. We feed machine learning and deep neural networks with a range of character-based and word-based text features as input. The data for this experiment was created by combining YouTube video comments from code-mixed Kannada and English (Kn-En) texts. The texts were pre-processed, split into words, and categorized as ‘Kannada’, ‘English’, ‘Mixed-Language’, ‘Name’, ‘Location’, and ‘Other’. The proposed techniques were able to learn from these features and were able to effectively identify the language of the words in the dataset. The proposed CK-Keras model with pre-trained Word2Vec embedding was our best-performing system, as it outperformed other methods when evaluated by the F1 scores.