Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection (1704.05973v1)

Abstract: The proliferation of social media in communication and information dissemination has made it an ideal platform for spreading rumors. Automatically debunking rumors at their stage of diffusion is known as \textit{early rumor detection}, which refers to dealing with sequential posts regarding disputed factual claims with certain variations and highly textual duplication over time. Thus, identifying trending rumors demands an efficient yet flexible model that is able to capture long-range dependencies among postings and produce distinct representations for the accurate early detection. However, it is a challenging task to apply conventional classification algorithms to rumor detection in earliness since they rely on hand-crafted features which require intensive manual efforts in the case of large amount of posts. This paper presents a deep attention model on the basis of recurrent neural networks (RNN) to learn \textit{selectively} temporal hidden representations of sequential posts for identifying rumors. The proposed model delves soft-attention into the recurrence to simultaneously pool out distinct features with particular focus and produce hidden representations that capture contextual variations of relevant posts over time. Extensive experiments on real datasets collected from social media websites demonstrate that (1) the deep attention based RNN model outperforms state-of-the-arts that rely on hand-crafted features; (2) the introduction of soft attention mechanism can effectively distill relevant parts to rumors from original posts in advance; (3) the proposed method detects rumors more quickly and accurately than competitors.

• The paper introduces CallAtRumors, a novel framework that combines LSTM networks with a deterministic soft-attention mechanism for early rumor detection.
• It eliminates labor-intensive feature engineering by leveraging tf-idf word embeddings in time-ordered batches to capture dynamic contextual variations.
• Empirical results on Twitter and Weibo datasets demonstrate high detection accuracy, with precision and recall rates exceeding 85%, underscoring its practical impact.

Deep Attention-Based Recurrent Neural Networks for Early Rumor Detection

The paper, "Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection," addresses the crucial task of detecting rumors during their early dissemination on social media platforms. By employing a novel approach that integrates recurrent neural networks (RNNs) with an attention mechanism, the authors propose a method capable of effectively distinguishing rumors from non-rumors based on temporal sequences of social media posts.

Contributions and Methodology

The paper introduces a framework named CallAtRumors, emphasizing the use of deep learning to circumvent the labor-intensive process of feature engineering traditionally associated with rumor detection. This approach capitalizes on the strengths of Long Short-Term Memory (LSTM) networks, known for their ability to capture long-range dependencies in sequential data, and augments them with a deterministic soft-attention mechanism. The attention mechanism serves to selectively focus on specific parts of input sequences, thereby enhancing the model's ability to extract distinct features critical for differentiating between rumors and authentic information.

Key to the proposed framework is its capacity to model events as sequential data, treating each event as a series of posts. By leveraging tf-idf based word representations, encoded in time-ordered batches, the model learns to capture dynamic contextual variations and high duplication inherent in social media texts. This ability allows the model to deliver high precision and recall rates in detecting rumors early in their lifecycle, as evidenced by strong experimental results.

Empirical Evaluation

The framework was validated using two datasets from prominent social media platforms: Twitter and Weibo. Experimental results indicate that CallAtRumors consistently outperforms several state-of-the-art approaches, including traditional machine learning and recurrent neural network models. Specifically, the model achieved precision and recall figures of 88.63% and 85.71% on the Twitter dataset, and 87.10% and 86.34% on the Weibo dataset, respectively. These results highlight the model's efficacy in handling variable-length sequences and diverse data distributions.

Furthermore, the attention mechanism ensured that the model could focus on features that contribute most significantly to detection accuracy. This capability is particularly beneficial in contexts with a high prevalence of duplicate text content across posts, a common characteristic of rumors spreading on social media platforms.

Implications and Future Directions

The incorporation of attention mechanisms in the RNN framework underscores an important direction for advancing early rumor detection techniques. By enhancing sensitivity to relevant textual cues, the method not only improves detection accuracy but also facilitates earlier intervention in the control and management of misinformation. This capability can be instrumental in minimizing the social and economic impacts of rumor propagation, highlighting the practical significance of the proposed framework.

The paper also indirectly points to promising future avenues in AI research, including the integration of more sophisticated contextual and network features, such as user behavior and interaction patterns, to further refine detection performance. Additionally, adapting and extending the framework to other domains where misinformation is a concern presents an exciting opportunity for further exploration.

Overall, the paper presented in this paper offers a significant contribution to the field of rumor detection, proposing a novel computational approach that adeptly balances computational efficiency with the complexities of natural language in social media. The model's robustness and adaptability lend it substantial utility in various real-world applications, where early detection of misleading information is critical.