CLOSED Call for Papers: Special Issue on Foundation Model for Social Media
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Submissions Due: 1 July 2023
Submission deadline: 1 July 2023
Final Decision: 20 October 2023
Publication: November 2023
Social media has become an integral part of our lives, with billions of people using various platforms to connect, communicate, and share information every day. However, the increasing use of social media has also raised concerns about its impact on society, including issues related to privacy, misinformation, and polarization. To address these challenges, researchers and practitioners have been developing various models and algorithms that can help us better understand and navigate social media.
This special issue focuses on the foundation model for social media, which is a set of principles, concepts, and methods that underlie the development of effective and responsible social media systems. Current research in this issue covers a wide range of topics, including the use of natural language processing and machine learning to detect and mitigate fake news and hate speech, the design of user interfaces that promote positive interactions and reduce polarization, and the development of privacy-preserving mechanisms for social media platforms.
The beauty of foundation models is that they are able to learn a foundation number of language representations through pre-training, so that they can be fine-tuned on unlabeled data and perform well on various tasks. However, the underlying model also has some disadvantages, including problems such as high computational cost and data bias.
Computational cost is a major drawback of the foundation model. Because these models have billions of parameters, they require massive computing resources for training and inference. This makes it very difficult for small and medium-sized enterprises to train their own basic models, and multiple GPUs are required for calculation during inference, so the operating cost is very high.
Another disadvantage is data bias. Since the foundation model is pre-trained on unfiltered data from the internet, this data may contain harmful information such as bias, hate speech, etc. Even with human annotators, it is difficult to inspect every data point, so this can lead to a low level of trust in the model.
Despite these shortcomings, the development of foundation models is an important trend that can increase the efficiency and flexibility of the field of natural language processing. In the future, we may see more basic models applied to various tasks and applications, thereby driving the further development of artificial intelligence technology.
By bringing together the latest research and insights from experts in the field, this special issue provides a comprehensive overview of the foundation model for social media and its potential to help us build more trustworthy, inclusive, and ethical social media systems. Whether you are a researcher, practitioner, or anyone interested in the future of social media, we hope that research in this issue will inspire new ideas and collaborations that can make social media a force for good in the world.
The theme of this special issue is to explore the challenges that foundation models still need to overcome and the benefits and challenges of foundation models for social media.
Topics include but are not limited to:
Unforeseeable Consequences: The Unintended Effects of Foundation Models
The Quest for Precision: Approaches and Techniques for Enhancing the Precision of Complex Reasoning
The Illusion of Control: Examining the Limits of Foundation Models
Efficient Reasoning: Exploring the Efficiency and Limitations of Foundation Models
Deploying Foundation Models: Challenges and Solutions in Social Media Applications
Ethical and Moral Implications of Foundation Models in Social Media
The Role of Data Quality and Sources in Foundation Models for Social Media Analysis
Addressing Bias in Foundation Models for Social Media
Privacy and Security Concerns in Social Media Applications of Foundation Models
Social Responsibility and the Future of Foundation Models in Social Media
For author information and guidelines on submission criteria, please visit theTBD’s Author Information page. Please submit papers through the ScholarOne system, and be sure to select the special-issue name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal.