Machine Learning for Social Media Analysis

Dear Colleagues,

Social media has become a valuable source of information for a variety of applications. Social media platforms allow users to share their thoughts and opinions through a range of modalities, including text, images, video, or audio. These messages can provide highly personal insights into many topics that could not be obtained by other means. Moreover, social media, is in many scenarios, the fastest source of information.

The data stream generated by social media is massive, and requires automatic approaches to extract useful information for a given application. In recent years, such techniques have been developed in Natural Language Processing and Computervision, but also in other sub-fields of (deep) machine learning, such as network analysis. Social media data pose particular challenges for machine learning methods due to their intractable nature, e.g., their brevity, linguistic variety, misspellings, idiosyncratic spellings, or abbreviations and emoji, heavy reliance on context, low-quality images, wide-ranging recording channels, etc. As deep learning research rarely focuses on this type of data, methods are rarely developed to be robust to these issues.

The purpose of this Special Issue is to discuss the challenges of applying machine learning methods to social media data. We welcome contributions of original research, advancements, developments and experiments in the following fields (not exhaustive):

• Natural Language Processing approaches for social media (e.g., embeddings, multilingual approaches);
• Comutervision approaches for social media;
• Network analysis, e.g., Graph Neural Networks;
• Multimodal data and data fusion;
• Fusion of social media with other data sources;
• Data ethics in social media;
• Big data sources from social media, data access and storage, social media corpora;
• Rapid social media analysis for crises and disasters;
• Misinformation detection;
• Geographic information in social media;
• Information retrieval from social media;
• Challenges and opportunities of social media data;
• Applications of social media analysis, e.g., in healthcare, mobility, economy, political science, geosciences, etc.

Prof. Dr. Anna Kruspe
Guest Editor

Manuscript Submission Information

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• social media
• social media mining
• deep learning
• natural language processing
• computervision
• network analysis
• information retrieval
• misinformation
• crisis response and management
• healthcare
• mobility
• economy
• political science
• geosciences
• corpora
• data ethics
• privacy
• data fusion
• multimodal data