Introduction
Social media websites such as Twitter, Facebook, Instagram, and YouTube continue to share user-generated content on a massive scale. User’s attempting to find relevant information within such vast and dynamic volumes risk being overwhelmed. In response, efforts are being made to develop new tools and methods that help users make sense of – and make use of – social media sites. In this workshop we will bring together commercial and academic researchers to discuss these issues, and explore the challenges for social media mining.
The current expansion of social media leads to masses of affective data related to peoples’ emotions, sentiments and opinions. Knowledge discovery from such data is an emerging area of research in the past few years, with a potential number of applications of paramount importance to business organisations, individual users and governments. Data mining and machine learning techniques are used to discover knowledge from various types of affective data such as ratings, text or browsing data. Sentiment analysis techniques have grown tremendously over the last few years, addressing applications of paramount importance. Obama's presidential election campaign and Gap logo change are two of these examples. Business organisations, individuals and governments are keen on extracting what people think of a particular product, a newly introduced governmental policy, etc. Applications are growing rapidly and so are the techniques. However, the gap between techniques and applications is till an issue that needs to be addressed.
This workshop aims to bring together researchers in this area to present their latest work, to discuss the challenges in the field and identify where our efforts, as a research community, should focus.
The current expansion of social media leads to masses of affective data related to peoples’ emotions, sentiments and opinions. Knowledge discovery from such data is an emerging area of research in the past few years, with a potential number of applications of paramount importance to business organisations, individual users and governments. Data mining and machine learning techniques are used to discover knowledge from various types of affective data such as ratings, text or browsing data. Sentiment analysis techniques have grown tremendously over the last few years, addressing applications of paramount importance. Obama's presidential election campaign and Gap logo change are two of these examples. Business organisations, individuals and governments are keen on extracting what people think of a particular product, a newly introduced governmental policy, etc. Applications are growing rapidly and so are the techniques. However, the gap between techniques and applications is till an issue that needs to be addressed.
This workshop aims to bring together researchers in this area to present their latest work, to discuss the challenges in the field and identify where our efforts, as a research community, should focus.
Topics of interest
- New methods or approaches for mining social media data;
- new approaches to model users or other entities in social media;
- analytics in social media;
- evaluation methods for mining and modelling in social media;
- new visualisation approaches for social media (especially multimedia);
- new applications and demonstrations of social media mining in practice;
- sentiment analysis techniques in social media;
- hybrid approaches to social media analysis; and
- any other relevant topic.
Important dates
- Paper submission: 27 October 2013 (extended)
- Notification of acceptance: 20 November 2013
- Camera ready papers: 27 November 2013
- Workshop: 10 December 2013
Workshop chairs
- Mohamed Medhat Gaber, Robert Gordon University, UK
- Nirmalie Wiratunga, Robert Gordon University, UK
- Ayse Goker, Robert Gordon University, UK
- Mihaela Cocea, University of Portsmouth, UK
Programme committee
- Alexandra Balahur, European Commission Joint Research Centre, Italy
- Carlos Martin Dancausa, Robert Gordon University, UK
- Samhaa El-Beltagy, Nile University, Egypt
- Rosta Farza, University of Pittsburgh, US
- Joao Gomes, I2R - Institute for Infocomm Research, Singapore
- Jelena Jovanovic, University of Belgrade, Serbia
- Frederic Stahl, University of Reading, UK
- Gulden Uchyigit, University of Brighton, UK
- Berrin Yanikoglu, Sabanci University, Turkey
Paper submission
Two types of submissions are invited: long and short papers. Long papers should have a maximum of 12 pages, while short papers should be no longer than 8 pages. All papers should be formatted according to the Springer LNAI guidelines available at: http://www.springer.de/comp/lncs/authors.html
Authors of selected papers will be invited to submit extended versions of their papers for possible inclusion in a book volume with a provisional title "Advances in Intelligent Techniques for Social Media Analysis" in the Springer's Studies in Computational Intelligence Series (http://www.springer.com/series/7092).
To submit a paper, please send a PDF version of your paper to both chairs at "m.gaber1@rgu.ac.uk" and "n.wiratunga@rgu.ac.uk" with the title "BCS SGAI Workshop on Social Media Analysis Submission"
Authors of selected papers will be invited to submit extended versions of their papers for possible inclusion in a book volume with a provisional title "Advances in Intelligent Techniques for Social Media Analysis" in the Springer's Studies in Computational Intelligence Series (http://www.springer.com/series/7092).
To submit a paper, please send a PDF version of your paper to both chairs at "m.gaber1@rgu.ac.uk" and "n.wiratunga@rgu.ac.uk" with the title "BCS SGAI Workshop on Social Media Analysis Submission"
