Geoffrey Barbier

Harnessing the Crowdsourcing Power of Social Media for Disaster Relief

This article briefly describes the advantages and disadvantages of crowdsourcing applications applied to disaster relief coordination. It also discusses several challenges that must be addressed to make crowdsourcing a useful tool that can effectively facilitate the relief progress in coordination, accuracy, and security.

DATA MINING IN SOCIAL MEDIA

The rise of online social media is providing a wealth of social network data. Data mining techniques provide researchers and practitioners the tools needed to analyze large, complex, and frequently changing social media data. This chapter introduces the basics of data mining, reviews social media, discusses how to mine social media data, and highlights some illustrative examples with an emphasis on social networking sites and blogs.

Maximizing benefits from crowdsourced data

Crowds of people can solve some problems faster than individuals or small groups. A crowd can also rapidly generate data about circumstances affecting the crowd itself. This crowdsourced data can be leveraged to benefit the crowd by providing information or solutions faster than traditional means. However, the crowdsourced data can hardly be used directly to yield usable information. Intelligently analyzing and processing crowdsourced information can help prepare data to maximize the usable information, thus returning the benefit to the crowd. This article highlights challenges and investigates opportunities associated with mining crowdsourced data to yield useful information, as well as details how crowdsource information and technologies can be used for response-coordination when needed, and finally suggests related areas for future research.

Promoting coordination for disaster relief: from crowdsourcing to coordination

The efficiency at which governments and nongovernmental organizations (NGOs) are able to respond to a crisis and provide relief to victims has gained increased attention. This emphasis coincides with significant events such as tsunamis, hurricanes, earthquakes, and environmental disasters occuring during the last decade. Crowdsourcing applications such as Twitter, Ushahidi, and Sahana have proven useful for gathering information about a crisis yet have limited utility for response coordination. In this paper, we briefly describe the shortfalls of current crowdsourcing applications applied to disaster relief coordination and discuss one approach aimed at facilitating efficient collaborations amongst disparate organizations responding to a crisis.

Provenance Data in Social Media

Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media. Table of Contents: Information Provenance in Social Media / Provenance Attributes / Provenance via Network Information / Provenance Data

Information provenance in social media

Information appearing in social media provides a challenge for determining the provenance of the information. However, the same characteristics that make the social media environment challenging provide unique and untapped opportunities for solving the information provenance problem for social media. Current approaches for tracking provenance information do not scale for social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities for computer scientists and sociologists. This paper introduces a theoretical approach aimed guiding future efforts to realize a provenance capability for social media that is not available today. The guiding vision is the use of social media information itself to realize a useful amount provenance data for information in social media.

Understanding online groups through social media

Multiple fields including sociology, anthropology, and business are interested in understanding group behavior. Applying data mining techniques to social media can help provide insights into group behavior and divulge a groups characteristics by identifying a group, developing a profile for a group, revealing the sentiment of a group, and detailing a groups composition. The ability to accomplish these tasks has practical business and scientific applications such as understanding customers better and providing new insights into influence propagation, as well as the ability to accurately categorize groups over time. This paper highlights some ongoing research efforts aiming at understanding groups through social media.

A social network analysis approach to detecting suspicious online financial activities

Social network analysis techniques can be applied to help detect financial crimes. We discuss the relationship between detecting financial crimes and the social web, and use select case studies to illustrate the potential for applying social network analysis techniques. With the increasing use of online financing services and online financial activities, it becomes more challenging to find suspicious activities among massive numbers of normal and legal activities.

User Vulnerability and Its Reduction on a Social Networking Site

Privacy and security are major concerns for many users of social media. When users share information (e.g., data and photos) with friends, they can make their friends vulnerable to security and privacy breaches with dire consequences. With the continuous expansion of a user’s social network, privacy settings alone are often inadequate to protect a user’s profile. In this research, we aim to address some critical issues related to privacy protection: (1) How can we measure and assess individual users’ vulnerability? (2) With the diversity of one’s social network friends, how can one figure out an effective approach to maintaining balance between vulnerability and social utility? In this work, first we present a novel way to define vulnerable friends from an individual user’s perspective. User vulnerability is dependent on whether or not the user’s friends’ privacy settings protect the friend and the individual’s network of friends (which includes the user). We show that it is feasible to measure and assess user vulnerability and reduce one’s vulnerability without changing the structure of a social networking site. The approach is to unfriend one’s most vulnerable friends. However, when such a vulnerable friend is also socially important, unfriending him or her would significantly reduce one’s own social status. We formulate this novel problem as vulnerability minimization with social utility constraints. We formally define the optimization problem and provide an approximation algorithm with a proven bound. Finally, we conduct a large-scale evaluation of a new framework using a Facebook dataset. We resort to experiments and observe how much vulnerability an individual user can be decreased by unfriending a vulnerable friend. We compare performance of different unfriending strategies and discuss the security risk of new friend requests. Additionally, by employing different forms of social utility, we confirm that the balance between user vulnerability and social utility can be practically achieved.

Convergence of influential bloggers for topic discovery in the blogosphere

In this paper, we propose a novel approach to automatically detect “hot” or important topics of discussion in the blogosphere. The proposed approach is based on analyzing the activity of influential bloggers to determine specific points in time when there is a convergence amongst the influential bloggers in terms of their topic of discussion. The tool BlogTrackers, is used to identify influential bloggers and the Normalized Google Distance is used to define the similarity amongst the topics of discussion of influential bloggers. The key advantage of the proposed approach is its ability to automatically detect events which are important in the blogger community.