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Spatiotemporal Analysis of Social Media Data

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Encyclopedia of GIS

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Synonyms

Social media analysis; Spatiotemporal data mining; Spatiotemporal modeling

Definition

Social media provide a convenient platform for users to create and share content or to participate in online social activities. With the development of sensor technologies, it also generates large amount of spatiotemporal data, such as check-in records, user restaurant reviews, and geo-temporal tagged tweets. This entry specifically considers analyzing the spatiotemporal patterns in social media data. The problem involves identifying spatiotemporal correlations, building spatiotemporal models, and making predictions in space and time. Given that spatiotemporal observations have complex correlations, the major challenge of the problem is how to take into account the spatial and temporal correlations within the context of social media.

Historical Background

Spatiotemporal analysis for social media data is a relatively young area. Many efforts have been focused on geographical topic discovery,...

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References

  • Agarwal D, Chen B-C, Elango P (2010) Fast online learning through offline initialization for time-sensitive recommendation. In: KDD, Washington, DC. ACM, pp 703–712

    Google Scholar 

  • Ahmed A, Hong L, Smola AJ (2013) Hierarchical geographical modeling of user locations from social media posts. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro. International World Wide Web Conferences Steering Committee, pp 25–36

    Chapter  Google Scholar 

  • Backstrom L, Sun E, Marlow C (2010) Find me if you can: improving geographical prediction with social and spatial proximity. In: WWW, Raleigh. ACM, pp 61–70

    Google Scholar 

  • Bahadori MT, Liu Y, Xing EP (2013) Fast structure learning in generalized stochastic processes with latent factors. In: KDD, Chicago. ACM, pp 284–292

    Google Scholar 

  • Bahadori MT, Yu QR, Liu Y (2014) Fast multivariate spatiotemporal analysis via low rank tensor learning. In: NIPS, Montreal, pp 3491–3499

    Google Scholar 

  • Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  • Chae J, Thom D, Bosch H, Jang Y, Maciejewski R, Ebert DS, Ertl T (2012) Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition. In: IEEE conference on visual analytics science and technology, Seattle

    Book  Google Scholar 

  • Cheng Z, Caverlee, J, Lee K (2010) You are where you tweet: a content-based approach to geo-locating Twitter users. In: CIKM, Toronto. ACM, pp 759–768

    Google Scholar 

  • Cho E, Myers SA, Leskovec J (2011) Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego. ACM, pp 1082–1090

    Google Scholar 

  • Crandall DJ, Backstrom L, Cosley D, Suri S, Huttenlocher D, Kleinberg J (2010) Inferring social ties from geographic coincidences. PNAS 107(52):22436–22441

    Article  Google Scholar 

  • Dalvi N, Kumar R, Pang B (2012) Object matching in tweets with spatial models. In: WSDM, Seattle. ACM, pp 43–52

    Google Scholar 

  • Deshpande A, Vempala S (2006) Adaptive sampling and fast low-rank matrix approximation. In: Approximation, randomization, and combinatorial optimization. Algorithms and techniques. Springer, pp 292–303

    MATH  Google Scholar 

  • Karatzoglou A, Baltrunas L, Church K, Böhmer M (2012) Climbing the app wall: enabling mobile app discovery through context-aware recommendations. In: Proceedings of the 21st ACM international conference on information and knowledge management, Maui. ACM, pp 2527–2530

    Google Scholar 

  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  MathSciNet  MATH  Google Scholar 

  • Lauw HW, Lim E-P, Pang H, Tan T-T (2005) Social network discovery by mining spatiotemporal events. Comput Math Organ Theory 11(2):97–118

    Article  MATH  Google Scholar 

  • Li R, Li B, Jin C, Xue X, Zhu X (2011) Tracking user-preference varying speed in collaborative filtering. In: AAAI, San Francisco

    Google Scholar 

  • Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, New York. ACM, pp 831–840

    Google Scholar 

  • Lin Y-R, Sundaram H, De Choudhury M, Kelliher A (2009) Temporal patterns in social media streams: theme discovery and evolution using joint analysis of content and context. In: IEEE international conference on multimedia and expo (ICME 2009), New York. IEEE, pp 1456–1459

    Chapter  Google Scholar 

  • Liu G, Fu Y, Xu T, Xiong H, Chen G, Discovering temporal retweeting patterns for social media marketing campaigns

    Google Scholar 

  • Lu Z, Agarwal D, Dhillon IS (2009) A spatiotemporal approach to collaborative filtering. In: RecSys, New York. ACM, pp 13–20

    Google Scholar 

  • Mazumder A, Das A, Kim N, Gokalp S, Sen A, Davulcu H (2013) Spatiotemporal signal recovery from political tweets in Indonesia. In: Social computing, Washington, DC, pp 280–287

    Google Scholar 

  • Mei Q, Liu C, Su H, Zhai C (2006) A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th international conference on World Wide Web, Edinburgh. ACM, pp 533–542

    Google Scholar 

  • Munro R (2011) Subword and spatiotemporal models for identifying actionable information in Haitian Kreyol. In: CoNLL, Portland, pp 68–77

    Google Scholar 

  • Rattenbury T, Good N, Naaman M (2007) Towards automatic extraction of event and place semantics from Flickr tags. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, Amsterdam. ACM, pp 103–110

    Google Scholar 

  • Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th international conference on World Wide Web, Raleigh. ACM, pp 851–860

    Google Scholar 

  • Sengstock C, Gertz M, Flatow F, Abdelhaq H (2013) A probablistic model for spatiotemporal signal extraction from social media. In: SIGSPATIAL, Orlando. ACM, pp 274–283

    Google Scholar 

  • Sidiropoulos ND, Bro R, Giannakis GB (2000) Parallel factor analysis in sensor array processing. IEEE Trans Signal Process 48(8):2377–2388

    Article  Google Scholar 

  • Sigurbjörnsson B, Van Zwol R (2008) Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th international conference on World Wide Web, Beijing. ACM, pp 327–336

    Google Scholar 

  • Singh VK, Gao M, Jain R (2010) Situation detection and control using spatiotemporal analysis of microblogs. In: WWW, Raleigh. ACM, pp 1181–1182

    Google Scholar 

  • Snoek J, Larochelle H, Adams RP (2012) Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems, Lake Tahoe, pp 2951–2959

    Google Scholar 

  • Wang E, Salazar E, Dunson D, Carin L et al (2013) Spatiotemporal modeling of legislation and votes. Bayesian Anal 8(1):233–268

    Article  MathSciNet  MATH  Google Scholar 

  • Weng J, Lee B-S (2011) Event detection in Twitter. ICWSM 11:401–408

    Google Scholar 

  • Xu J-M, Bhargava A, Nowak R, Zhu X (2012) Socioscope: spatiotemporal signal recovery from social media. In: Machine learning and knowledge discovery in databases, Beijing. Springer, pp 644–659

    Chapter  Google Scholar 

  • Xu J-M, Bhargava A, Nowak R, Zhu X (2013) Socioscope: spatiotemporal signal recovery from social media. In: ECML PKDD, Prague

    Google Scholar 

  • Yan X, Guo J, Lan Y, Cheng X (2013) A biterm topic model for short texts. In: Proceedings of the 22nd international conference on World Wide Web, Rio de Janeiro. International World Wide Web Conferences Steering Committee, pp 1445–1456

    Chapter  Google Scholar 

  • Yin Z, Cao L, Han J, Zhai C, Huang T (2011) Geographical topic discovery and comparison. In: Proceedings of the 20th international conference on World Wide Web, Hyderabad. ACM, pp247–256

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Southern California, Los Angeles, CA, USA

    Rose Yu

  2. Computer Science Department, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

    Yan Liu

Authors
  1. Rose Yu
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  2. Yan Liu
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Corresponding author

Correspondence to Yan Liu .

Editor information

Editors and Affiliations

  1. University of Minnesota, Minneapolis, MN, USA

    Shashi Shekhar

  2. Management Science and Information Systems Department, Rutgers Business School, Rutgers, The State University of New Jersey, Newark, NJ, USA

    Hui Xiong

  3. Department of Management Sciences, Tippie College of Business, University of Iowa, Iowa City, IA, USA

    Xun Zhou

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Yu, R., Liu, Y. (2017). Spatiotemporal Analysis of Social Media Data. In: Shekhar, S., Xiong, H., Zhou, X. (eds) Encyclopedia of GIS. Springer, Cham. https://doi.org/10.1007/978-3-319-17885-1_1629

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