Call for Papers – Social Media Mining


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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. Topics of interest include, but are not limited to:

  • 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.


SICSA – The Scottish Informatics and Computer Science Alliance

SICSA is a research pool supported by the Scottish Funding Council which brings together researchers from Universities across Scotland to create one of the largest top-quality research clusters in ICS in the world.

Our research covers virtually all areas of computer science and informatics from low-level hardware design, through networking and middleware, to wetware, artificial intelligence, human computer interaction and social informatics. We are world leaders in both theoretical and practical aspects of the discipline and have a strong interdisciplinary tradition involving maths, engineering, psychology, the humanities and the social sciences.

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9:45 – 10:00 Coffee and Registration
10:00 – 10:15 Welcome from Workshop Organizers
10:15 – 11:00 Prof. Barry Smyth (University College Dublin)
Sentimental Product Recommendation: Harnessing User-Generated Reviews for Product Recommendation.
Abstract The web is awash with user-generated reviews, which have become increasingly important in helping shoppers choose between product and services. These reviews can provide a rich source of product recommendation knowledge since they encode the opinions and experiences of large numbers of users. Can we extract and use such features as the basis for a type of experiential product description? Do these features represent a viable alternative to more conventional product descriptions made up of meta-data or catalog features? And can these experiential product cases be used for the purpose of recommendation?

For example, consider the 13″ Retina MacBook Pro. At the time of writing its features, as listed by Amazon, cover technical details such as screen-size, RAM, processor speed, and price. These are the type of catalog features that one might expect to find in a conventional content-based recommender. But often such features can be difficult to come by and they can be technical in nature, thereby limiting recommendation opportunities and making it difficult for casual shoppers to judge the relevance of suggestions in any practical sense. However, the MacBook Pro has more than 70 reviews that encode valuable insights into a great many of its features, from its beautiful design and great build quality to its high price. These features capture more detail than a handful of technical (catalog) features. They also encode the opinions of real users and, as such, provide an objective basis for product comparisons.

In this talk we will explore the use of opinion mining techniques to extract meaningful product descriptions from user-generated reviews and evaluate their use in a novel approach to product recommendation that combines product similarity and feature sentiment. We will describe the results of a recent evaluation using Amazon and TripAdvisor data to demonstrate the utility and generality of this approach for recommendation.

11:00 – 12:00

Session 1:

- New methods or approaches for mining social media data and new approaches to model users or other entities in social media – Chair: Dr Nirmalie Wiratunga.
• Adam Wyner: Semi-Automated Extraction of Arguments from Online Product Reviews.
• Sadiq Sani, Nirmalie Wiratunga, Stewart Massie and Robert Lothian (presented by Sadiq Sani): Sentiment Classification using Supervised Sub-Spacing

12:15 – 13:00 Lunch & Networking – Location: Costa back room.
13:00 – 13:45 Prof. Steve Schifferes (City University London – Dept. of Journalism)
Verifying News on the Social Web: Challenges and Prospects
Abstract The problem of verification is the key issue for journalists who use social media. This presentation argues for the importance of a user- centered approach in finding solutions to this problem. Because journalists have different needs for different types of stories, there is no one magic bullet that can verify social media. Any tool will need to have a multi-faceted approach to the problem, and will have to be adjustable to suit the particular needs of individual journalists and news organizations.
13:45 – 15:00

Session 2:

-Evaluation methods for mining and modelling in social media – Chair: Prof. Ayse Goker

• David Corney, Ayse Goker and Carlos Martin (presented by David Corney): Evaluating information retrieval in Social Media
• James McMinn: Issues in developing a test collection for evaluating event detection on Twitter
• Ben Horsburgh, Susan Craw and Stewart Massie (presented by Ben Horsburgh): Evaluating Music Recommendation Using Implicit and Explicit Feedback from Social Media

15:00 – 15:30 Tea/Coffee break – Location: Costa back room
15:30 – 16:15 Dr Miles Osborne (School of Informatics, University of Edinburgh)Cross Stream Event Detection
Abstract Cross Stream Event Detection

Social Media (especially Twitter) is widely seen as a source of real-time breaking news. For example, when Osama Bin Laden was killed by US forces the news was first made public on Twitter. Rapidly finding all breaking news has clear economic and humanitarian benefits.
Finding all such breaking news presents hard computational challenges. We need to detect news-related novelty in massive streams (upwards of two thousand posts per second) as quickly as possible. Efficiency is not the only consideration however and we also need to confront the enormous quantity of irrelevant posts. In this talk I will outline how we tackle the first problem using Locality Sensitive Hashing, taking constant time per post. In tandem I will discuss how we use Storm to parallelise this computation, yielding a system capable of processing 2k tweets per second. The second problem is tackled by intersecting the Twitter stream with Wikipedia page requests, filtering-out spurious first stories. Taken together, this results in processing more than 250 million items per day.
Joint work with Sasa Petrovic (Edinburgh), Craig MacDonald (Glasgow), Iadh Ounis (Glasgow) and Richard McCredie (Glasgow)

16:15 – 17:30

Session 3:

Applications and visualisations of social media mining – Chair: Dr. Carlos Martin- Analytics in social media.

• Elias de Oliveira, Patrick Marques Ciarelli, and Marco Toledo (presented by Elias Oliveira): Towards a Classification System for 1 Billion of Tweets
• Aminu Muhammad, Nirmalie Wiratunga, Robert Lothian and Richard Glassey (presented by Aminu Muhammad): Sentiment Retrieval and Visualization in Discussion
• David Wilson: Mining Crowdsourced Data for Geographic Knowledge

17:30 – 18:00 Summary & Wrap Up
18:00 – Networking

Important dates

Submission deadline – 12th May 2013
Beginning of the workshop – 24th May 2013


Registration is free, but places are limited. Please register your attendance at

Call for papers

Prospective authors are invited to submit abstracts of no more than two (2) pages. Please send your submissions to by 12th May 2013.


The event is hosted by the Information Retrieval & Reuse research group within the Digital Technologies Theme of the IDEAS Research Institute. IDEAS is a multi-disciplinary research centre that develops novel technologies highly relevant to industry, and generates creative spaces for new forms of practice.

Travel information

Directions can be found here.


Smart Web Technologies Centre, School of Computing Science and Digital Media
Robert Gordon University
St Andrew St
Aberdeen, Scotland
AB25 1HG