Overview
This editorial highlights a special issue focusing on the extraction of actionable insights from online user-generated content, as featured in the Information Retrieval Journal. This initiative is particularly significant given the rapid growth of online platforms and the vast amounts of data they generate, which can be harnessed for various applications across multiple domains.
Background & Relevance
The last decade has seen a remarkable increase in the use of online platforms, leading to an explosion of user-generated content. This content not only reflects individual behaviors but also shapes public discourse and trends across various sectors, including marketing, education, and politics. The ability to mine this data for actionable insights is crucial for organizations seeking to understand user behavior and predict future trends. The focus of this special issue is on developing predictive models that can uncover hidden patterns and insights from user-generated data, moving beyond traditional methodologies.
Key Details
- Submission Deadline: November 1, 2019
- First Notification: February 1, 2020
- Revisions Due: April 1, 2020
- Final Notification: May 1, 2020
- Guest Editors:
- Marcelo G. Armentano, ISISTAN Research Institute (CONICET- UNICEN), Argentina
- Ebrahim Bagheri, Ryerson University, Canada
- Julia Kiseleva, Microsoft Research AI, USA
- Frank Takes, University of Amsterdam, The Netherlands
Eligibility & Participation
This call for papers invites contributions from researchers and practitioners across various fields, including computer science, data mining, machine learning, and social network analysis. The aim is to gather innovative research that addresses the challenges and opportunities in mining actionable insights from user-generated content.
Submission or Application Guidelines
- Manuscripts must be original and not under consideration for publication elsewhere.
- Previously published conference papers must include at least 30% new material.
- Submissions should be made through the journal’s editorial submission system, selecting “Mining Actionable Insights from Online User Generated Content” as the article type.
- Authors must adhere to the journal’s publication guidelines, which are available on the journal’s website.
- All submissions will undergo the journal’s standard review process.
Additional Context / Real-World Relevance
The ability to analyze user-generated content is increasingly vital in today’s data-driven world. Insights derived from this content can inform decision-making processes in various industries, enhancing competitive advantage and fostering innovation. As organizations become more adept at leveraging these insights, the implications for marketing strategies, product development, and user engagement are profound.
Conclusion
This special issue presents a valuable opportunity for researchers to contribute to the growing field of user-generated content analysis. Interested parties are encouraged to submit their work and share their findings with the broader academic and professional community. This is a chance to advance knowledge and practice in a rapidly evolving domain.
Category: CFP & Deadlines
Tags: data mining, machine learning, user-generated content, social network analysis, predictive modeling, information retrieval, sentiment analysis, influence maximization
