: a hybrid deep neural model with mixed fusion for rumour detection in social data streams.

The unrelenting trend of doctored narratives, content spamming, fake news and rumour dissemination on social media can lead to grave consequences that range from online intimidating and trolling to lynching and riots in real- life. It has therefore become vital to use computational techniques that can detect rumours, do fact-checking and inhibit its amplification. In this paper, we put forward a model for rumour detection in streaming data on social platforms. The proposed model is a hybrid deep neural model that combines the predictions of a hierarchical attention network (HAN) and a multi-layer perceptron (MLP) learned using context-based (text + meta-features) and user-based features, respectively. The concatenated context feature vector is generated using feature-level fusion strategy to train HAN. Eventually, a decision-level late fusion strategy using logical OR combines the individual classifier prediction and outputs the final label as rumour or non-rumour. The results demonstrate improved performance to the existing state-of-the-art approach on the benchmark PHEME dataset with a 4.45% gain in -score. The model can facilitate well-time intervention and curtail the risk of widespread rumours in streaming social media by raising an alert to the moderators.