Douglas Teixeira
ABSTRACT
Predicting a person's whereabouts is important in several scenarios. However, it is hard to obtain data that reliably reflects users' mobility patterns. This difficulty has led researchers to use social media data as a proxy to understand and predict human mobility. It has been shown, however, that such data is inherently biased and error-prone, and that such drawbacks may produce sub-par mobility prediction models. In a more narrow context, researchers have used social media data to predict users' check-in patterns. A common approach to alleviate the biases in social media data is to use more than one data source. We here show, however, that the use of data from different social networks does not necessarily increase the predictability of a person next check-in. Our experiments indicate that this result is due to how and where people use different social networks, and that user behavioral characteristics play an important role on the predictability of the next check-in.
- Mariano G. Beiró, André Panisson, Michele Tizzoni, and Ciro Cattuto. 2016. Predicting human mobility through the assimilation of social media traces into mobility models. EPJ Data Science 5, 1 (2016).Google Scholar
- Thomas M Cover and Joy A Thomas. 2012. Elements of Information Theory. John Wiley & Sons.Google Scholar
- Wei Dong, Nick Duffield, Zihui Ge, Seungjoon Lee, and Jeffrey Pang. 2013. Modeling Cellular User Mobility Using a Leap Graph. In Proc. 14th International Conference on Passive and Active Measurement. Google ScholarDigital Library
- Robert M Fano. 1961. Transmission of Information: A Statistical Theory of Communications. American Journal of Physics 29, 11 (1961).Google ScholarCross Ref
- Marta C Gonzalez, Cesar A Hidalgo, and A-L Barabasi. 2008. Understanding individual human mobility patterns. Nature 453 (2008).Google Scholar
- Juan C. Herrera, Daniel B. Work, Ryan Herring, Xuegang Ban, Quinn Jacobson, and Alexandre M. Bayen. 2010. Evaluation of traffic data obtained via GPS-enabled mobile phones: The Mobile Century field experiment. Transportation Research Part C: Emerging Technologies 18, 4 (2010).Google Scholar
- Andrea Hess, Karin Anna Hummel, Wilfried N. Gansterer, and Günter Haring. 2016. Data-driven Human Mobility Modeling: A Survey and Engineering Guidance for Mobile Networking. Comput. Surveys 48, 3 (2016). Google ScholarDigital Library
- Shan Jiang, Gaston A. Fiore, Yingxiang Yang, Joseph Ferreira, Jr., Emilio Frazzoli, and Marta C. González. 2013. A Review of Urban Computing for Mobile Phone Traces: Current Methods, Challenges and Opportunities. In Proc. 2nd ACM International Workshop on Urban Computing. Google ScholarDigital Library
- Raja Jurdak, Kun Zhao, Jiajun Liu, Maurice AbouJaoude, Mark Cameron, and David Newth. 2015. Understanding Human Mobility from Twitter. PLOS ONE (2015).Google Scholar
- I. Kontoyiannis, P. H. Algoet, Yu. M. Suhov, and A. J. Wyner. 2006. Nonparametric Entropy Estimation for Stationary Processes and Random Fields, with Applications to English Text. IEEE Transactions on Information Theory 44, 3 (2006). Google ScholarDigital Library
- Maxime Lenormand, Miguel Picornell, Oliva G. Cantú-Ros, Antònia Tugores, Thomas Louail, Ricardo Herranz, Marc Barthelemy, Enrique Frías-Martínez, and José J. Ramasco. 2014. Cross-Checking Different Sources of Mobility Information. PLOS ONE 9, 8 (2014).Google Scholar
- Xin Lu, Erik Wetter, Nita Bharti, Andrew J. Tatem, and Linus Bengtsson. 2013. Approaching the Limit of Predictability in Human Mobility. Scientific Reports (2013).Google Scholar
- Aarti Munjal, Tracy Camp, and William C. Navidi. 2011. SMOOTH: A Simple Way to Model Human Mobility. In Proc. 14th ACM Int. Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems. Google ScholarDigital Library
- A. Noulas, S. Scellato, N. Lathia, and C. Mascolo. 2012. Mining User Mobility Features for Next Place Prediction in Location-Based Services. In 2012 IEEE 12th International Conference on Data Mining. Google ScholarDigital Library
- Claude E Shannon and Warren Weaver. 1998. The mathematical theory of communication. University of Illinois press.Google Scholar
- Thiago H. Silva, Pedro O. S. Vaz de Melo, Jussara M. Almeida, Juliana Salles, and Antonio A. F. Loureiro. 2013. A Comparison of Foursquare and Instagram to the Study of City Dynamics and Urban Social Behavior. In Proc. ACM International Workshop on Urban Computing. Google ScholarDigital Library
- Lucas Maia Silveira, Jussara M. Almeida, Humberto Torres Marques-Neto, Carlos Sarraute, and Artur Ziviani. 2016. MobHet: Predicting human mobility using heterogeneous data sources. Computer Communications 95 (2016).Google Scholar
- Chaoming Song, Zehui Qu, Nicholas Blumm, and Albert-László Barabási. 2010. Limits of Predictability in Human Mobility. Science 327, 5968 (2010).Google Scholar
- Joanne Treurniet. 2014. A Taxonomy and Survey of Microscopic Mobility Models from the Mobile Networking Domain. Comput. Surveys (2014). Google ScholarDigital Library
- Yingzi Wang, Nicholas Jing Yuan, Defu Lian, Linli Xu, Xing Xie, Enhong Chen, and Yong Rui. 2015. Regularity and Conformity: Location Prediction Using Heterogeneous Mobility Data. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '15). ACM. Google ScholarDigital Library
- Zengbin Zhang, Lin Zhou, Xiaohan Zhao, Gang Wang, Yu Su, Miriam Metzger, Haitao Zheng, and Ben Y. Zhao. 2013. On the Validity of Geosocial Mobility Traces. In Proceedings of the Twelfth ACM Workshop on Hot Topics in Networks (HotNets-XII). ACM. Google ScholarDigital Library
- K. Zhao, D. Khryashchev, J. Freire, C. Silva, and H. Vo. 2016. Predicting taxi demand at high spatial resolution: Approaching the limit of predictability. In Proc. IEEE International Conference on Big Data.Google Scholar
- Yu Zheng, Licia Capra, Ouri Wolfson, and Hai Yang. 2014. Urban Computing: Concepts, Methodologies, and Applications. ACM Transactions on Intelligent Systems and Technology 5 (2014). Google ScholarDigital Library
- Yu Zheng, Yanchi Liu, Jing Yuan, and Xing Xie. 2011. Urban Computing with Taxicabs. In Proc. 13th International Conference on Ubiquitous Computing. Google ScholarDigital Library
Index Terms
- On the Predictability of a User's Next Check-in Using Data from Different Social Networks
Recommendations
Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
Location is an important feature for many applications, and wireless networks may serve their clients better by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with ...
Building social capital with Facebook: Type of network, availability of other media, and social self-efficacy matter#
Highlights- Type of friends affects building social capital via Facebook and traditional media.
AbstractFindings about Facebook's effect on relationships are mixed, possibly due to lack of models that acknowledge differences across users, types of their friends, and use of competing media. To address this, we proposed and tested how ...
Using proximity to predict activity in social networks
WWW '12 Companion: Proceedings of the 21st International Conference on World Wide WebThe structure of a social network contains information useful for predicting its evolution. We show that structural information also helps predict activity. People who are "close" in some sense in a social network are more likely to perform similar ...
Comments