A particular phenomenon of interest in Retail Eco-nomics is the spillover effect of anchor stores (specific stores with a reputable brand) to non-anchor stores in terms of customer traffic. Prior works in this area rely on small and survey-based datasets that are often confidential or expensive to collect on a large scale. Also, very few works study the underlying causal mechanisms between factors that underpin the spillover effect. In this work, we analyze the causal relationship between anchor stores and customer traffic to non-anchor stores and employ a propensity score matching framework to investigate this effect more efficiently. First of all, to demonstrate the effect, we leverage open and mobile data from London Datastore and Location-Based Social Networks (LBSNs) such as Foursquare. We then perform a large-scale empirical analysis of customer visit patterns from anchor stores to non-anchor stores (e.g., non-chain restaurants) located in the Greater London area as a case study. By studying over 600 neighbourhoods in the Greater London area, we find that anchor stores cause a 14.2-26.5% increase in customer traffic for the non-anchor stores reinforcing the established economic theory Moreover, we evaluate the efficiency of our methodology by studying the confounder balance, dose difference and performance of the matching framework on synthetic data. Through this work, we point decision-makers in the retail industry to a more systematic approach to estimate the anchor store effect and pave the way for further research to discover more complex causal relationships underlying this effect with open data.

With the rise of Location-Based Social Networks (LBSNs) and their heavy reliance on User-Generated Content, it has become essential to attract and keep more users, which makes the churn prediction problem interesting. Recent research focuses on solving the task by utilizing complex neural networks. However, due to the black-box nature of those proposed deep learning algorithms, it is still a challenge for LBSN managers to interpret the prediction results and design strategies to prevent churning behavior. Therefore, in this paper, we perform the first investigation into the interpretability of the churn prediction in LBSNs. We proposed a novel attention-based deep learning network, Interpretable Attention Network (IAN), to achieve high performance while ensuring interpretability. The network is capable to process the complex temporal multivariate multidimensional user data from LBSN datasets (i.e. Yelp and Foursquare) and provides meaningful explanations of its prediction. We also utilize several visualization techniques to interpret the prediction results. By analyzing the attention output, researchers can intuitively gain insights into which features dominate the model's prediction of churning users. Finally, we expect our model to become a robust and powerful tool to help LBSN applications to understand and analyze user churning behavior and in turn remain users.

The survival of a business is undeniably pertinent to its success. A key factor contributing to its continuity depends on its customers. The surge of location-based social networks such as Yelp, Diangping, and Foursquare has paved the way for leveraging user-generated content on these platforms to predict business survival. Prior works in this area have developed several quantitative features to capture geography and user mobility among businesses. However, the development of qualitative features is minimal. In this work, we thus perform extensive feature engineering across four feature sets, namely, geography, user mobility, business attributes, and linguistic modelling to develop classifiers for business survival prediction. We additionally employ an interpretability framework to generate explanations and qualitatively assess the classifiers' predictions. Experimentation among the feature sets reveals that qualitative features including business attributes and linguistic features have the highest predictive power, achieving AUC scores of 0.72 and 0.67, respectively. Furthermore, the explanations generated by the interpretability framework demonstrate that these models can potentially identify the reasons from review texts for the survival of a business.

Social networks are effective platforms to study the real-life behavior of users. In this paper, we study users' political polarization during the times of crisis and its relation to nationalism. To this purpose, we focus on the reaction of Indian and Pakistani Twitter users during February 2019 crisis and the ensuing Indian General Elections in 2019. We show that a national crisis affects the polarization and discourse in both countries. Also, we show that user activities increase during a national crisis, and political discourse strengthens while polarization decreases on critical days. Finally, we highlight the links between this crisis and the Indian elections and show how the political parties discussed the crisis in their campaigns.

Understanding how much users disclose personal information in Online Social Networks (OSN) has served various scenarios such as maintaining social relationships and customer segmentation. Prior studies on self-disclosure have relied on surveys or users' direct social networks. These approaches, however, cannot represent the whole population nor consider user dynamics at the community level.