![]() Concretely, DatingSec leverages long short-term memory neural networks (LSTM) and an attentive module to capture the interplay of users' temporal-spatial behaviors and user-generated textual content. To tackle this, we propose DatingSec, a novel malicious user detection system for dating apps. Existing methods overlooked the signals hidden in the textual information of user interactions, particularly the interplay of temporal-spatial behaviors and textual information, leading to limited detection performance. In this work, we focus on malicious user detection in dating apps. While bringing convenience to hundreds of millions of users, dating apps are vulnerable to become targets of adversaries. Compared with traditional offline dating means, dating apps ease the process of partner finding significantly. The results are able to assist individuals, governors and business leaders in making better decisions regarding traveling, immigrating, measuring city improvements and cooperation with cities.ĭating apps have gained tremendous popularity during the past decade. Finally, we leverage machine learning techniques to build a model for identifying the most influential cities in the world according to the Skout data. Moreover, we look into Skout users’ mobility patterns by discovering the most popular inter-city routes, destinations, and tightly connected city groups, and analyze the impact on the mobility patterns from geographical distances, languages and cultures. Based on the collected information, we model the inter-city mobility of Skout users with a global city network, and analyze the evolution of the network based on its structural characteristics. In this work, we collected all the location information published by over 1.2 million Skout users during December 2012 and June 2016. Location-based social apps, such as Skout, have been widely used by millions of users for sharing their location information. ![]()
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March 2023
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