The WWW'13 workshop on Multidisciplinary Approaches to Big Social Data Analysis (MABSDA) explored the new frontiers of big data computing for opinion mining through machine learning techniques, knowledge-based systems, adaptive and transfer learning, in order to more efficiently retrieve and extract social information from the Web.

As the Web rapidly evolves, Web users are evolving with it. In the era of social connectedness, people are becoming increasingly enthusiastic about interacting, sharing, and collaborating through social networks, online communities, blogs, Wikis, and other online collaborative media. In recent years, this collective intelligence has spread to many different areas, with particular focus on fields related to everyday life such as commerce, tourism, education, and health, causing the size of the Social Web to expand exponentially. The distillation of knowledge from such a large amount of unstructured information, however, is an extremely difficult task, as the contents of today's Web are perfectly suitable for human consumption, but remain hardly accessible to machines. The opportunity to capture the opinions of the general public about social events, political movements, company strategies, marketing campaigns, and product preferences has raised growing interest both within the scientific community, leading to many exciting open challenges, as well as in the business world, due to the remarkable benefits to be had from marketing and financial market prediction.

MABSDA aims to provide an international forum for researchers in the field of big data computing for opinion mining and sentiment analysis to share information on their latest investigations in social information retrieval and their applications both in academic research areas and industrial sectors. The broader context of the workshop comprehends information retrieval, natural language processing, web mining, semantic web, and artificial intelligence. Topics of interest include but are not limited to:
• Machine learning for sentiment mining
• Concept-level sentiment analysis
• Biologically-inspired opinion mining
• Sentiment identification & classification
• Association rule learning for opinion mining
• Time evolving opinion & sentiment analysis
• Multi-modal sentiment analysis
• Multi-domain & cross-domain evaluation
• Knowledge base construction & integration with opinion analysis
• Transfer learning of opinion & sentiment with knowledge bases
• Sentiment topic detection & trend discovery
• Social ranking
• Social network analysis
• Opinion spam detection

13:00-13:15 Welcoming and introduction (Erik Cambria)
13:15-14:25 The Web as a Laboratory (Bebo White)

14:30-15:00 Coffee break

15:00-15:25 Like Prediction: Modeling Like Counts by Bridging Facebook Pages with Linked Data (Shohei Ohsawa)
15:30-15:55 Tower of Babel: A Crowdgame Building Sentiment Lexicons for Resource-Scarce Languages (Yoonsung Hong)
16:00-16:25 Rule-based Opinion Target and Aspect Extraction to Acquire Affective Knowledge (Stefan Gindl)

16:30-17:00 Coffee break

17:00-17:25 A Graph-Based Approach to Commonsense Concept Extraction and Semantic Similarity Detection (Dheeraj Rajagopal)
17:30-17:55 Spanish Knowledge Base Generation for Polarity Classification from Masses (Arturo Montejo-Ráez)
18:00-18:25 Revised Mutual Information Approach for German Text Sentiment Classification (Farag Saad)
18:30-18:45 Concluding remarks (Erik Cambria)

Bebo White is a Departmental Associate (Retired) at the Stanford Linear Accelerator Center (SLAC), the high-energy physics and basic energy science laboratory operated by Stanford University. Prior to his retirement, he was permanent staff/faculty at SLAC from 1981 to 2005. While his initial responsibilities at SLAC were in computational physics, in recent years Prof White's work has been dominated by his involvement with World Wide Web technology. He first became involved with WWW development while on sabbatical at CERN in 1989 and was instrumental in establishing the first non-European Web site at SLAC in 1991.

KEYNOTE (The Web as a Laboratory)
Insights from Web Science and Big Data Analysis have led many researchers to the conclusion that the Web not only represents an almost unlimited data store but also a remarkable multi-disciplinary laboratory environment. A new challenge is how to best leverage the potential of this experimental space. What are the procedures for defining, implementing and evaluating “Web-scale” experiments? What are acceptable measures of robustness and repeatability? What are the opportunities for experimental collaboration? What disciplines are likely to benefit from this new research model? This talk will likely have more questions than answers, but should provide fertile ground for ongoing discussion.

• Erik Cambria, National University of Singapore (Singapore)
• Yunqing Xia, Tsinghua University (China)
• Newton Howard, MIT Media Laboratory (USA)