Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks

Abstract

Culture has been recognized as a driving impetus for human development. It co-evolves with both human belief and behavior. When studying culture, Cultural Mapping is a crucial tool to visualize different aspects of culture (e.g., religions and languages) from the perspectives of indigenous and local people. Existing cultural mapping approaches usually rely on large-scale survey data with respect to human beliefs, such as moral values. However, such a data collection method not only incurs a significant cost of both human resources and time, but also fails to capture human behavior, which massively reflects cultural information. In addition, it is practically difficult to collect large-scale human behavior data. Fortunately, with the recent boom in Location-Based Social Networks (LBSNs), a considerable number of users report their activities in LBSNs in a participatory manner, which provides us with an unprecedented opportunity to study large-scale user behavioral data. In this article, we propose a participatory cultural mapping approach based on collective behavior in LBSNs. First, we collect the participatory sensed user behavioral data from LBSNs. Second, since only local users are eligible for cultural mapping, we propose a progressive “home” location identification method to filter out ineligible users. Third, by extracting three key cultural features from daily activity, mobility, and linguistic perspectives, respectively, we propose a cultural clustering method to discover cultural clusters. Finally, we visualize the cultural clusters on the world map. Based on a real-world LBSN dataset, we experimentally validate our approach by conducting both qualitative and quantitative analysis on the generated cultural maps. The results show that our approach can subtly capture cultural features and generate representative cultural maps that correspond well with traditional cultural maps based on survey data.

Membership Inference Attacks Against Machine Learning Models

“…We created a location dataset from the publicly available set of mobile users' location "check-ins" in the Foursquare social network, restricted to the Bangkok area and collected from April 2012 to September 2013 [36]. 7 The check-in dataset contains 11, 592 users and 119, 744 locations, for a total of 1, 136, 481 check-ins.…”

HistoSketch: Fast Similarity-Preserving Sketching of Streaming Histograms with Concept Drift

“…As a key statistical tool in empirical data analysis, histograms have been widely used not only as a popular visualization of empirical data distribution [21], but also as a feature to measure data similarity that is further exploited in many machine learning tasks [1], [4], [5], [22]. Although the histogram of a static dataset can often be easily computed, it is practically difficult to compute histograms for data streams with typically unknown cardinality and which thus require an unbounded amount of memory to maintain the histogram.…”

Anticipating Information Needs Based on Check-in Activity

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