Ryan Skraba

Sensing Trending Topics in Twitter

Online social and news media generate rich and timely information about real-world events of all kinds. However, the huge amount of data available, along with the breadth of the user base, requires a substantial effort of information filtering to successfully drill down to relevant topics and events. Trending topic detection is therefore a fundamental building block to monitor and summarize information originating from social sources. There are a wide variety of methods and variables and they greatly affect the quality of results. We compare six topic detection methods on three Twitter datasets related to major events, which differ in their time scale and topic churn rate. We observe how the nature of the event considered, the volume of activity over time, the sampling procedure and the pre-processing of the data all greatly affect the quality of detected topics, which also depends on the type of detection method used. We find that standard natural language processing techniques can perform well for social streams on very focused topics, but novel techniques designed to mine the temporal distribution of concepts are needed to handle more heterogeneous streams containing multiple stories evolving in parallel. One of the novel topic detection methods we propose, based on -grams cooccurrence and topic ranking, consistently achieves the best performance across all these conditions, thus being more reliable than other state-of-the-art techniques.

Social-Based Web Services Discovery and Composition for Step-by-Step Mashup Completion

In this paper, we describe our work in progress on Web services recommendation for services composition in a Mashup environment, by proposing a new approach to assist end-users based social interactions capture and analysis. This approach uses an implicit social graph inferred from the common composition interests of users. We describe the transformation of users-services interactions into a social graph and a possible means to leverage that graph to derive service recommendation. As this work is in progress, this proposal was implemented within a platform called SoCo where preliminary experiments show interesting results.

Towards multiple identity detection in social networks

In this paper we discuss a piece of work which intends to provide some insights regarding the resolution of the hard problem of multiple identities detection. Based on hypothesis that each person is unique and identifiable whether in its writing style or social behavior, we propose a Framework relying on machine learning models and a deep analysis of social interactions, towards such detection.

Social Web Mashups Full Completion via Frequent Sequence Mining

In this paper we address the problem of Web Mashups full completion which consists of predicting the most suitable set of (combined) services that successfully meet the goals of an end-user Mashup, given the current service (or composition of services) initially supplied. We model full completion as a frequent sequence mining problem and we show how existing algorithms can be applied in this context. To overcome some limitations of the frequent sequence mining algorithms, e.g., efficiency and recommendation granularity, we propose FESMA, a new and efficient algorithm for computing frequent sequences of services and recommending completions. FESMA also integrates a social dimension, extracted from the transformation of user-service interactions into user-user interactions, building an implicit graph that helps to better predict completions of services in a fashion tailored to individual users. Evaluations show that FESMA is more efficient outperforming the existing algorithms even with the consideration of the social dimension. Our proposal has been implemented in a prototype, SoCo, developed at Bell Labs.

Developing Compelling Social-Enabled Applications with Context-Based Social Interaction Analysis

We present in this paper a new approach for constructing implicit social networks from electronic sources, like emails, SMS and phone calls. The main novelty is the use of Social Interaction Analysis to assist end-users in their communication needs. We discuss how a social proximity can be calculated between two persons in a social network and show how a weighted, directed network is constructed based on interactions between people. After a description of the architecture of the framework, we show how a contextual, weighted social network can help in automatically finding the contact with highest probability to know the whereabouts of a person who is currently not reachable. The implemented social helper application uses a specific contextual view of the social network, exploiting only interactions that occurred in the last 48 hours.