Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
This work addresses the challenge of verifying news on fast-paced platforms like Twitter, though it is incremental as it builds on existing deep learning and ensemble methods.
The paper tackled the problem of timely rumor detection in social media by proposing an ensemble deep learning model with time-series representation of tweets, achieving a 7.9% improvement in micro F1 score over baselines on the PHEME dataset.
Social media is a popular platform for timely information sharing. One of the important challenges for social media platforms like Twitter is whether to trust news shared on them when there is no systematic news verification process. 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 an ensemble model, which performs majority-voting on a collection of predictions by deep neural networks using time-series vector representation of Twitter data for timely detection of rumors. By combining the proposed data pre-processing method with the ensemble model, better performance of rumor detection has been demonstrated in the experiments using PHEME dataset. Experimental results show that the classification performance has been improved by 7.9% in terms of micro F1 score compared to the baselines.