Big Data Approaches for Sentiment Analysis and Fake News Detection

The exponential growth of online platforms has made sentiment analysis and fake news detection two of the most critical challenges in the era of Big Data. Understanding public emotions while detecting and mitigating misinformation at scale is essential for healthy information ecosystems, trustworthy decision-making, and societal resilience.

This Research Topic seeks contributions that explore scalable, robust, and explainable approaches to sentiment and misinformation analytics across large-scale, multilingual, and multimodal data streams. We welcome work spanning theory, methodology, systems, applications, and ethical considerations.


Topics of interest include (but are not limited to):
• Large Language Models & Multimodal Approaches for joint sentiment and misinformation detection
• Real-time and streaming analytics for social media and news platforms
• Federated and privacy-preserving methods for sensitive data scenarios
• Knowledge graphs and neuro-symbolic reasoning for claim verification
• Bias, fairness, and explainability in large-scale sentiment and misinformation systems
• Benchmarks, datasets, and evaluation protocols for multilingual and low-resource settings
• Applications in elections, health communication, crisis monitoring, and reputation management

Expected impact:
This Research Topic aims to establish best practices for trustworthy large-scale sentiment and misinformation analytics, foster the release of open resources and benchmarks, and encourage responsible, transparent applications with measurable societal benefits.

We warmly invite researchers from academia, industry, and policy to submit their work and join us in shaping the future of sentiment analysis and fake news detection in the Big Data era.

Manuscript types accepted:
Original Research, Methods, Brief Research Reports, Data Reports, System & Application papers, Reviews, and Perspectives.


Journal Metrics
For Frontiers in Artificial Intelligence:
• Journal Impact Factor: 4.7
• CiteScore: 7.3
• Total number of citations: 18,000
• JIF quartile: Q1
• CiteScore quartile: Q2

For Frontiers in Big Data:
• Journal Impact Factor: 2.3
• CiteScore: 6.1
• Total number of citations: 8,000
• JIF quartile: Q2
• CiteScore quartile: Q1

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Clinical Trial
• Community Case Study
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: big data, sentiment analysis, fake news detection, misinformation, large language models, multimodal analytics, streaming data, privacy-preserving methods, knowledge graphs, explainable AI

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.