Over the last few years, there has been a growing public and enterprise interest in social media and their role in modern society. At the heart of this interest is the ability for users to create and share content via a variety of platforms such as blogs, micro-blogs, collaborative wikis, multimedia sharing sites, social networking sites. The unprecedented volume and variety of user-generated content as well as the user interaction network constitute new opportunities for understanding social behavior and building socially intelligent systems.
This 5th workshop in the ACL series of workshops about language in social media will focus on the need for publicly available corpora and test benchmarks in order to allow a comprehensive evaluation of performance, and on the need for an objective comparison of different approaches. We especially (but not exclusively) encourage submissions that provide publicly shared benchmark data set to evaluate future approaches, or submissions that conduct evaluations on existing public data sets.
The workshop will provide a forum for discussion between leading names and researchers involved in text analysis and social networks in the context of natural language understanding, natural language generation, automatic categorization, topic detection, emotion analysis, and applications using computational approaches to process social networks. Besides methodologies and techniques for social media analysis, we also encourage the submission of papers that experiment with and describe applicative contexts in which analysis and detection of affective aspects are useful and beneficial.
Authors are invited to submit full papers on original, unpublished work in the topic area of this workshop. Submissions should be formatted using the EACL 2014 stylefiles for latex or MS Word (http://www.eacl2014.org/files/eacl-2014-styles.zip), with blind review and not exceeding 8 pages plus an extra page for references.
The PDF files will be submitted electronically via the Start system at https://www.softconf.com/eacl2014/LASM/