Spatio-semantic user profiles in location-based social networks

Mohamed, Soha; Abdelmoty, Alia I.

doi:10.1007/s41060-017-0059-9

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…For one mobile application, each user action in one session shows differentiated performance. Inspired by the user information to profile [24][25][26][27][28], this paper illustrates mobile user behavior from multidimensional features such as location, online duration, and visitor's query term [29]. We then explore the behavioral patterns in each interactive session and categorize the users into different groups using the K -means clustering algorithm.…”

Section: Data Preprocessing and Data Analysismentioning

confidence: 99%

Modeling and Analysis of Group User Portrait through WeChat Mini Program

Chen²,

Yan

et al. 2022

Wireless Communications and Mobile Computing

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Meeting users’ preferences and increasing business revenue is an ongoing challenge in the mobile service application. In this paper, we address these challenges by mining mobile user behavior patterns and propose an approach to construct a group user portrait by analyzing access data collected from the users of the WeChat Mini Program. We extract the attributes of mobile users considering their geographic information, online duration, and age group. Using Z -score standardized processing and K -means clustering algorithm, we then model the user portraits through three dimensions including daily average duration, interaction intensity, and access frequency. Our analysis has two important features. Firstly, the significant log data used in our experiments was collected from the production environment ensuring that the results reflect the real attributes of WeChat Mini Program users’ behavior. Secondly, we provide data-driven decision-making to help marketers enhance the quality of the product and improve user experience. The experimental results indicate that by distilling and analyzing the key factors from the log data, the characters of typical users can be properly profiled to help product owners better optimize the exact set of the features which need to sustain and further grow.

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Section: Data Preprocessing and Data Analysismentioning

confidence: 99%

Modeling and Analysis of Group User Portrait through WeChat Mini Program

Chen²,

Yan

et al. 2022

Wireless Communications and Mobile Computing

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“…Different semantic information concerning the user and their association with places can be extracted from the shared content. This could include the user's interests, activities and sentiments (Mohamed and Abdelmoty 2017).…”

Section: The Data Dimensionsmentioning

confidence: 99%

Modelling perceived risks to personal privacy from location disclosure on online social networks

Alrayes

Abdelmoty

El-Geresy

et al. 2019

International Journal of Geographical Information Science

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As users increasingly rely on online social networks for their communication activities, personal location data processing through such networks poses significant risks to users' privacy. Location tracks can be mined with other shared information to extract rich personal profiles. To protect users' privacy, online social networks face the challenge of ensuring transparent communication to users of how their data are processed, and explicitly obtaining users' informed consent for the use of this data. In this paper, we explore the complex nature of the location disclosure problem and its risks to personal privacy. We evaluate, with an experiment involving 715 participants, the contributing factors to the perception of such risks with scenarios that mimic a) realistic modes of interaction, where users are not fully aware of the extent of their location-related data being processed, and b) with devised scenarios that deliberately inform users of the data they are sharing and its visibility to others. The results are used to represent the users' perception of privacy risks when sharing their location information online and to derive a possible model of privacy risks associated with this sharing behaviour. Such a model can inform the design of privacy-aware online social networks to improve users' trust and to ensure compliance with legal frameworks for personal privacy.

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