A Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention (2407.19332v1)

Abstract: Micro-blogs and cyber-space social networks are the main communication mediums to receive and share news nowadays. As a side effect, however, the networks can disseminate fake news that harms individuals and the society. Several methods have been developed to detect fake news, but the majority require large sets of manually labeled data to attain the application-level accuracy. Due to the strict privacy policies, the required data are often inaccessible or limited to some specific topics. On the other side, quite diverse and abundant unlabeled data on social media suggests that with a few labeled data, the problem of detecting fake news could be tackled via semi-supervised learning. Here, we propose a semi-supervised self-learning method in which a sentiment analysis is acquired by some state-of-the-art pretrained models. Our learning model is trained in a semi-supervised fashion and incorporates LSTM with self-attention layers. We benchmark our model on a dataset with 20,000 news content along with their feedback, which shows better performance in precision, recall, and measures compared to competitive methods in fake news detection.

• The paper introduces a semi-supervised network using pseudo-labeling to mitigate limited labeled data in fake news detection.
• It combines LSTM with self-attention and sentiment encoding from pre-trained models to capture subtle textual patterns.
• Experimental results show improved precision, recall, and F1-scores, demonstrating the model's robustness for real-world applications.

Semi-supervised Fake News Detection using Sentiment Encoding and LSTM with Self-Attention

This paper presents a novel approach to addressing the challenge of fake news detection on social media platforms through a semi-supervised deep learning method that leverages sentiment encoding and LSTM with self-attention mechanisms. The approach is particularly noteworthy due to its application in semi-supervised learning—a domain typically constrained by the scarcity of labeled data, which is a significant barrier when dealing with vast datasets available in social networks.

Methodology and Contributions

The authors utilize the FakeNewsNet dataset, which offers comprehensive content, social, and spatiotemporal data, to facilitate their research. This dataset spans multiple subjects, including politics and entertainment, pulling data from websites such as GossipCop and PolitiFact. Despite challenges encountered in data collection, such as removed tweets or suspended user accounts, the authors successfully prepared the data for their model by implementing effective crawling and data retrieval techniques.

Key components of the proposed methodology include:

1. Semi-supervised Learning with Pseudo-labeling: The paper introduces a self-learning semi-supervised network designed to handle label scarcity by augmenting a small set of labeled data with pseudo-labeling techniques. This approach effectively expands the dataset available for training, maintaining robust performance across different data samples.
2. LSTM with Self-Attention: The incorporation of LSTM with self-attention layers augments the ability of the model to identify significant patterns and dependencies over time, enhancing textual data analysis. The self-attention layer aids in capturing semantic nuances within the input data by assessing the importance of different words in the sequence.
3. Sentiment Encoding: The integration of sentiment analysis, using pre-trained RoBERTa models, enhances the model's ability to detect emotional tonality and subjective tendencies in the texts. This feature extraction enriches the dataset with sentiment-based features, allowing the model to differentiate potentially fraudulent content based on emotional cues.
4. Hybridization of Content and Social Features: The network combines textual data with social context information, incorporating user metadata and behavior patterns to provide a multifaceted view of news dissemination and its credibility.

Experimental Results

The authors conducted a series of evaluations to compare their method's performance with traditional approaches as well as previous state-of-the-art models. The experimental setup showed that the proposed model achieved higher precision, recall, and F1-scores than existing methods, demonstrating the robustness and efficacy of leveraging sentiment encoding and attention mechanisms in fake news detection tasks.

Implications and Future Outlook

This research underscores the potential of semi-supervised learning frameworks in combating the proliferation of fake news, suggesting that even limited labeled datasets can be maximized with advanced network architectures and specific attention mechanisms. The implications of this paper are pertinent to fields like social media analytics and cybersecurity, where misinformation poses a significant threat.

Future developments may explore adaptive mechanisms for updating confidence thresholds dynamically in pseudo-labeling phases or incorporating more granular sentiment models for richer emotional tagging. Moreover, expanding the dataset scope by including diverse topics such as sports and finance could enhance the model's applicability across different news domains.

In conclusion, this paper presents a compelling semi-supervised methodology for fake news detection that contributes to ongoing efforts in refining algorithmic approaches capable of discerning misinformation with higher accuracy, addressing both practical and theoretical challenges in the field.