# Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media
###### Abstract  
Social media is a popular platform for information sharing. Any piece of information can be spread rapidly across the globe at lightning speed. The biggest challenge for social media platforms like Twitter is how to trust news shared on them when there is no systematic news verification process, which is the case for traditional media. Detecting false information, for example, 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 scheme on a collection of predictions of neural networks using time-series vector representation of Twitter data for fast detection of rumors. Experimental results show that proposed neural network models outperformed classical machine learning models in terms of micro F1 score. When compared to our previous works the improvements are 12.5% and 7.9%, respectively. social media; rumor detection; time-series data; machine learning; deep learning; Twitter +
Footnote †: journal: Medical  
and disinformation means information that is used to deceive its consumers), which are forms of false information [4,5]. If the spread of false information is not stopped early it may cause turmoil in the society. In case of time critical events, the effects may be dreadful. So detecting rumors in social media must be done in a timely fashion. Recently machine learning and deep learning gained huge popularity in addressing rumor detection in social media [6], and they typically applies trained classification models to predict new data samples as rumors or non-rumors [7]. One of the main concerns for applying these techniques is finding a dataset with good quality. On the other hand, performing extensive feature engineering on the dataset to extract a variety of useful features, for example, content-based and context-based features, for the rumor identification problem may help in improving a classification model's performance. However, it will significantly slow down the training procedure since employing complex features in training process is cumbersome in terms of computational complexity and availability of hardware resources to deal with extremely large sized feature set [8]. Hence, extensive feature engineering may not be suitable for timely rumor detection. In this paper, we explore the temporal features of Twitter data for timely detection of rumors in social media. Tweet creation timestamp can readily be extracted from tweets, and there is no time delay to collect timestamp features and no sophisticated data pre-processing is required to convert them into useful features to train a classification model. Based on this observation, we proposed an ensemble-based multiple time-series analysis model using deep learning models for timely detection of rumors in social media. Specifically, we generated time-series data by transforming Twitter conversations, where each conversation contains a list of tweets, into times-series vectors that contain reaction counts (i.e., the total number of reactions per time step along a conversation) as features, and fed them as input to deep learning models. The contributions of our proposed method are:  
* With the proposed method, computational complexity can be significantly reduced as we just need timestamps of tweets rather than their contents or user social engagements to perform feature extraction. Moreover, the extracted feature set is of numeric type, which is amicable to classification models. * Our proposed ensemble model improves the performances of classification models since it uses the majority-voting scheme on multiple neural networks that are part of the ensemble model and takes advantage of their individual strengths.