Analyzing and Modeling Special Offer Campaigns in Location-based Social Networks

The proliferation of mobile handheld devices in combination with the technological advancements in mobile computing has led to a number of innovative services that make use of the location information available on such devices. Traditional yellow pages websites have now moved to mobile platforms, giving the opportunity to local businesses and potential, near-by, customers to connect. These platforms can offer an affordable advertisement channel to local businesses. One of the mechanisms offered by location-based social networks (LBSNs) allows businesses to provide special offers to their customers that connect through the platform. We collect a large time-series dataset from approximately 14 million venues on Foursquare and analyze the performance of such campaigns using randomization techniques and (non-parametric) hypothesis testing with statistical bootstrapping. Our main finding indicates that this type of promotions are not as effective as anecdote success stories might suggest. Finally, we design classifiers by extracting three different types of features that are able to provide an educated decision on whether a special offer campaign for a local business will succeed or not both in short and long term.Comment: in The 9th International AAAI Conference on Web and Social Media (ICWSM 2015

Realizing the Activation Potential of Online Communities

Online communities suffer from the 1-9-90 principle, which states that 1% of the community\u27s user base generates original content, an additional 9% is limited to interacting with existing content, while the remaining 90% of the participants is passively lurking. In this work we present a data-driven stochastic framework that estimates (1) the activation potential (i.e., the users that are currently lurkers but present a high likelihood of becoming heavy contributors) of an online community and (2) when and which users are more likely to become heavy contributors. Our proposed framework captures the transitional evolution of a user by a Hidden Markov Model, and estimates each user\u27s propensity to become a heavy contributor by employing parametric survival models. We build and evaluate our models on a unique large dataset of a specialized online community about diabetes

The Relationship Between Disclosing Purchase Information and Reputation Systems in Electronic Markets

In this work we investigate how the introduction of the Verified Purchase (VP) badge on Amazon.com affected both the review helpfulness and the product ratings. We first conduct a propensity score matching study and find that all else equal, camera reviews are on average ranked 7 positions higher than non-VP reviews, while book VP reviews are on average ranked 11 positions higher than non-VP reviews. Next, we use a natural experiment setting to identify whether the entry of the VP feature had an effect on the (1) overall review helpfulness (both VP and non-VP reviews), and (2) average product rating. Our results show that the introduction of VP caused an increase in review helpfulness of 7.7% for books, and 1.7% for electronics. Furthermore, it caused on average an increase of 20 and 18 positions in the ranks on book and electronic products respectively

A Team-Formation Algorithm for Faultline Minimization

In recent years, the proliferation of online resumes and the need to evaluate large populations of candidates for on-site and virtual teams have led to a growing interest in automated team-formation. Given a large pool of candidates, the general problem requires the selection of a team of experts to complete a given task. Surprisingly, while ongoing research has studied numerous variations with different constraints, it has overlooked a factor with a well-documented impact on team cohesion and performance: team faultlines. Addressing this gap is challenging, as the available measures for faultlines in existing teams cannot be efficiently applied to faultline optimization. In this work, we meet this challenge with a new measure that can be efficiently used for both faultline measurement and minimization. We then use the measure to solve the problem of automatically partitioning a large population into low-faultline teams. By introducing faultlines to the team-formation literature, our work creates exciting opportunities for algorithmic work on faultline optimization, as well as on work that combines and studies the connection of faultlines with other influential team characteristics

Economic impact and policy implications from urban shared transportation: The case of Pittsburgh’s shared bike system

During the last years the number of cities that have installed and started operating shared bike systems has significantly increased. These systems provide an alternative and sustainable mean of transportation to the city dwellers. Apart from the energy sustainability benefits, shared bike systems can have a positive effect on residents' health, air quality and the overall condition of the currently crumbling road network infrastructure. Anecdotal stories and survey studies have also identified that bike lanes have a positive impact on local businesses. In this study, driven by the rapid adoption of shared bike systems by city governments and their potential positive effects on a number of urban life facets we opt to study and quantify the value of these systems. We focus on a specific aspect of this value and use evidence from the real estate market in the city of Pittsburgh to analyze the effect on dwellers' properties of the shared bike system installed in the city in June 2015. We use quasi-experimental techniques and find that the shared bike system led to an increase in the housing prices (both sales and rental prices) in the zip codes where shared bike stations were installed. We further bring into the light potential negative consequences of this impact (i.e., gentrification) and discuss/propose two public policies that can exploit the impact of the system for the benefit of both the local government as well as the city dwellers

Should patients with diabetes be encouraged to integrate social media into their care plan?

AIM: To evaluate the use of social media of individuals with diabetes mellitus (DM). MATERIALS & METHODS: Both web-based and in-clinic surveys were collected from individuals with DM. Descriptive and correlation analyses were employed to evaluate respondents\u27 diabetes-specific social networking site behaviors. RESULTS: Forty-five patients with DM completed the web-based survey and 167, the clinic-based survey, of whom only 40 visited diabetes-specific social networking sites. Analysis of online survey data indicated that self-reported adherence to lifestyle recommendations was significantly correlated (p \u3c 0.01) with visiting the sites. Clinic-based survey data found that patients who reported using DM-specific web sites monitored home glucose values more often and had better compliance with insulin administration (both p \u3c 0.05) compared with nonusers. CONCLUSION: This study provides insight into why individuals visit DM-specific social networking sites. Certain self-management behaviors may improve as a result of visiting these sites. Further work is needed to explore how to leverage social media technology to assist patients with the management of DM