Language Identification (LID) is a challenging task, especially when the input texts are short and noisy such as posts and statuses on social media or chat logs on gaming forums. The task has been tackled by either designing a feature set for a traditional classifier (e.g. Naive Bayes) or applying a deep neural network classifier (e.g. Bi-directional Gated Recurrent Unit, Encoder-Decoder). These methods are usually trained and tested on a huge amount of private data, then used and evaluated as off-the-shelf packages by other researchers using their own datasets, and consequently the various results published are not directly comparable. In this paper, we first create a new massive labelled dataset based on one year of Twitter data. We use this dataset to test several existing language identification systems, in order to obtain a set of coherent benchmarks, and we make our dataset publicly available so that others can add to this set of benchmarks. Finally, we propose a shallow but efficient neural LID system, which is a ngram-regional convolution neural network enhanced with an attention mechanism. Experimental results show that our architecture is able to predict tens of thousands of samples per second and surpasses all state-of-the-art systems with an improvement of 5%.