  This work introduces the L3Cube-MahaSocialNER dataset, the first and largest
social media dataset specifically designed for Named Entity Recognition (NER)
in the Marathi language. The dataset comprises 18,000 manually labeled
sentences covering eight entity classes, addressing challenges posed by social
media data, including non-standard language and informal idioms. Deep learning
models, including CNN, LSTM, BiLSTM, and Transformer models, are evaluated on
the individual dataset with IOB and non-IOB notations. The results demonstrate
the effectiveness of these models in accurately recognizing named entities in
Marathi informal text. The L3Cube-MahaSocialNER dataset offers user-centric
information extraction and supports real-time applications, providing a
valuable resource for public opinion analysis, news, and marketing on social
media platforms. We also show that the zero-shot results of the regular NER
model are poor on the social NER test set thus highlighting the need for more
social NER datasets. The datasets and models are publicly available at
https://github.com/l3cube-pune/MarathiNLP
