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Social media is a popular platform for timely information sharing. One of theimportant challenges for social media platforms like Twitter is whether totrust news shared on them when there is no systematic news verificationprocess. On the other hand, timely detection of rumors is a non-trivial task,given the fast-paced social media environment. In this work, we proposed anensemble model, which performs majority-voting on a collection of predictionsby deep neural networks using time-series vector representation of Twitter datafor timely detection of rumors. By combining the proposed data pre-processingmethod with the ensemble model, better performance of rumor detection has beendemonstrated in the experiments using PHEME dataset. Experimental results showthat the classification performance has been improved by 7.9% in terms of microF1 score compared to the baselines.

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media