Roadmapping discussion summary:social media and linked data for emergency response

This paper provides a summary of the Social Media and Linked Data for Emergency Response (SMILE) workshop, co-located with the Extended Semantic Web Conference, at Montpellier, France, 2013. Following paper presentations and question answering sessions, an extensive discussion and roadmapping session was organised which involved the workshop chairs and attendees. Three main topics guided the discussion - challenges, opportunities and showstoppers. In this paper, we present our roadmap towards effectively exploiting social media and semantic web techniques for emergency response and crisis management.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Social media in emergency management:Twitter as a tool for communicating risks to the public

One of the main challenges of emergency management lies in communicating risks to the public. On some occasions, risk communicators might seek to increase awareness over emerging risks, while on others the aim might be to avoid escalation of public reactions. Social media accounts offer an opportunity to rapidly distribute critical information and in doing so to mitigate the impact of emergencies by influencing public reactions. This article draws on theories of risk and emergency communication in order to consider the impact of Twitter as a tool for communicating risks to the public. We analyse 10,020 Twitter messages posted by the official accounts of UK local government authorities (councils) in the context of two major emergencies: the heavy snow of December 2010 and the riots of August 2011. Twitter was used in a variety of ways to communicate and manage associated risks including messages to provide official updates, encourage protective behaviour, increase awareness and guide public attention to mitigating actions. We discuss the importance of social media as means of increasing confidence in emergency management institutions.

The Impact of Social Media in the Workplace

Today, individuals communicate easier and faster due to accessibility of the Internet. However, when employees are distracted with social media, it can become a concern for organizations. This paper reviews literature concerning social media and its implications at workplaces, and provides recommendations to control it, using Adams’ equity theory (1963).

Using Social Media to Build Your Academic Brand

This professional development session will review recent research on the use of social media by faculty and academic staff. The bulk of the presentation will focus on social media strategies and techniques that attendees can use to develop and build their academic brand. This session will be useful to various audiences including established faculty, new faculty and graduate students.

Text analytics of social media: Sentiment analysis, event detection and summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. ^ Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. ^ In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data. ^

Text Analytics of Social Media: Sentiment Analysis, Event Detection and Summarization

In the last decade, large numbers of social media services have emerged and been widely used in people's daily life as important information sharing and acquisition tools. With a substantial amount of user-contributed text data on social media, it becomes a necessity to develop methods and tools for text analysis for this emerging data, in order to better utilize it to deliver meaningful information to users. Previous work on text analytics in last several decades is mainly focused on traditional types of text like emails, news and academic literatures, and several critical issues to text data on social media have not been well explored: 1) how to detect sentiment from text on social media; 2) how to make use of social media's real-time nature; 3) how to address information overload for flexible information needs. In this dissertation, we focus on these three problems. First, to detect sentiment of text on social media, we propose a non-negative matrix tri-factorization (tri-NMF) based dual active supervision method to minimize human labeling efforts for the new type of data. Second, to make use of social media's real-time nature, we propose approaches to detect events from text streams on social media. Third, to address information overload for flexible information needs, we propose two summarization framework, dominating set based summarization framework and learning-to-rank based summarization framework. The dominating set based summarization framework can be applied for different types of summarization problems, while the learning-to-rank based summarization framework helps utilize the existing training data to guild the new summarization tasks. In addition, we integrate these techneques in an application study of event summarization for sports games as an example of how to better utilize social media data.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.

Social media data in research : provenance challenges?

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You'll Never Ride Alone: The Role of Social Media in Supporting the Bus Passenger Experience

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Internet Surveillance after Snowden: A Critical Empirical Study of Computer Experts' Attitudes on Commercial and State Surveillance of the Internet and Social Media post-Edward Snowden

Acknowledgement: The research presented in this paper was conducted as part of the EU FP7 research project PACT (http://www.projectpact.eu), grant agreement number 285635.

Social Media Strategies in the Retail Sector: Analysis and Recommendations for Three Multi-National Retailers

During the last twenty years (1995-2015), the world of commerce has expanded beyond the traditional brick-and-mortar high street to a global shop front accessible to billions of users via the Worldwide Web (WWW). Consumers are now using the web to immerse themselves in virtual shop fronts, using Social Media (SM) to communicate and share product ideas with friends and family. Retail organisations recognise the need to develop and adapt their strategies to respond to the increasing use of SM. New goals must be set in order to identify how companies will integrate social media into current practices. This research aims to suggest an advisable and comprehensive SM strategy for companies operating in the global retail sector, based on an exploratory analysis of three multi-national retail organisations' existing SM strategies. This will be assessed in conjunction with a broader investigation into social media in the retail industry. From this, a strategy will be devised to improve internal and external communication as well as knowledge management through the use of social media. Findings suggest that the use of SM within the retail industry has dramatically improved collaboration and communication processes for organisations as they are now able to converse better with stakeholders and the tools are relatively simple to integrate and implement as they benefit one another.