Towards social user profiling: unified and discriminative influence model for inferring home locations

By Rui Li, Shengjie Wang, Hongbo Deng, Rui Wang, Kevin Chen-Chuan Chang. Knowledge Discovery and Data Mining, 2012

354 citations recorded by the source corpus.

Subfields: Geography, Planning and Development, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/a9e31970b34a3cbdbe12e4157912ba4dfac262b3

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In Signal Processing

Citations in the corpus

Attribute Inference Attacks in Online Social Networks (2018, background): “[37] proposed a discriminative influence model to profile users’ home locations by combining their social networks and the geo-location of the tweets posted by the users.”
Everyone's Invited: A New Paradigm for Evaluation on Non-Transferable Datasets (2015, methodology): “Currently, geoinference methods have been tested in highly varied settings, with no standardization in evaluation metrics and with dataset sizes ranging from a few million posts (Li et al. 2012; Li, Wang, and Chang 2012; Rout et al. 2013) to hundreds of millions of posts or more (Jurgens 2013; Compt”
The Emerging Trends of Multi-Label Learning (2020, neutral): “predicting home location [247] and user gender”
Semi-supervised User Geolocation via Graph Convolutional Networks (2018, background): “None of the previous multiview approaches — with the exception of Li et al. (2012a) and Li et al. (2012b) that only use toponyms — effectively uses unlabelled data in the text view, and use only the unlabelled information of the network view via the user–user graph.”
Geolocation Prediction in Twitter Using Social Networks: A Critical Analysis and Review of Current Practice (2015, methodology): “The second approach evaluates systems using Accuracy@k, which measures the percentage of predictions that are within k distance units (e.g., kilometers or miles) of the true location (Li et al. 2012; Li, Wang, and Chang 2012; McGee, Caverlee, and Cheng 2013; Rout et al. 2013).”
Characterizing Silent Users in Social Media Communities (2015, methodology): “Our proposed approach to proﬁle lurkers is more closely related to those methods making use of social links and neighbors’ content (Li et al. 2012; Zamal, Liu, and Ruths 2012).”
Geolocation Prediction in Social Media Data by Finding Location Indicative Words (2012, background): “We then discuss evaluation metrics for geolocation prediction.”