Recent Research Developments in Social Media Analytics for Aiding Real-Life Societal Issues

Dear Colleagues,

Social media platforms such Twitter, Facebook, and Flickr are now generating massive volumes of data as hundreds of millions of people share information on them. Such information may include observations of daily happenings, personal opinions regarding politics or public figures, professional advice, business promotions, propagation of news and announcements, and conversations between friends. The data that can be collected include text, images, time stamps, locations, and structural data such as user relationships.

Since the data offered by social media can be so abundant, many real-life societal issues have benefited from social media analytics. Examples include crime prediction, election result estimation, ideological identification for public entities, riot monitoring, financial market forecast, and public sentiment analysis on various topics. The benefits vary from improvements in prediction accuracy to discoveries of hidden knowledge. As social media still represents overall social activities, it is expected that more societal issues can benefit from social media analytics in the future.

Since its emergence around 2010, social media analytics for real-life societal issues have moved beyond simple regression analysis and binary text classification. In particular, we see that structural data and multi-modal data, as well as new techniques in artificial intelligence, have been considered in new research outputs.

This Special Issue calls for high-quality, multidisciplinary research papers in the fields of computer science, artificial intelligence, data mining, and social science that aim to address issues associated with the use of social media analytics for real-life applications. We particularly welcome papers that present new real-life societal issues that can benefit from social media analytics, as well as novel approaches that reflect state-of-the-art data analytics and artificial intelligence technologies.

Dr. Yihong Zhang
Dr. Panote Siriaraya
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Societies is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• social media analytics
• social media mining
• social media-based intelligent systems
• social computing
• short text mining
• text stream mining
• multi-modal real-time data