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Discovering Social Bursts by Using Link Analytics on Large-Scale Social Networks

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Abstract

Social Network Services (SNSs) have been regarded as an important source for identifying events in our society. Detecting and understanding social events from SNS has been investigated in many different contexts. Most of the studies have focused on detecting bursts based on textual context. In this paper, we propose a novel framework on collecting and analyzing social media data for i) discovering social bursts and ii) ranking these social bursts. Firstly, we detect social bursts from the photos textual annotations as well as visual features (e.g., timestamp and location); and then effectively identify social bursts by considering the spreading effect of social bursts in the spatio-temporal contexts. Secondly, we use these relationships among social bursts (e.g., spatial contexts, temporal contexts and content) for enhancing the precision of the algorithm. Finally, we rank social bursts by analyzing relationships between them (e.g., locations, timestamps, tags) at different period of time. The experiments have been conducted with two different approaches: i) offline approach with the collected dataset, and i i ) online approach with the streaming dataset in real time.

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References

  1. Becker H, Naaman M, Gravano L (2010) Learning similarity metrics for event identification in social media. In: Proceedings of the third ACM international conference on Web search and data mining. ACM, pp 291–300

  2. Belém FM, Martins EF, Almeida JM, Gonçalves MA (2014) Personalized and object-centered tag recommendation methods for web 2.0 applications. Inf Process Manag 50(4):524–553

    Article  Google Scholar 

  3. Bello-Orgaz Gema, Jung JJ, Camacho D (2016) Social big data: Recent achievements and new challenges. Information Fusion 28:45–59

    Article  Google Scholar 

  4. Chen L, Roy A (2009) Event detection from flickr data through wavelet-based spatial analysis. In: Proceedings of the 18th ACM conference on Information and knowledge management. ACM, pp 523–532

  5. Clements M, Serdyukov P, de Vries AP, Reinders MJT (2010) Using flickr geotags to predict user travel behaviour. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, pages 851–852, Geneva. ACM, Switzerland

  6. Ding C, He X, Husbands P, Zha H, Simon HD (2002) Pagerank, hits and a unified framework for link analysis. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval, pages 353–354. ACM

  7. Hong M, Jung JJ (2016) MyMovieHistory: social recommender system by discovering social affinities among users. Cybern Syst 47(1–2):88–110

  8. Jung JJ (2012) Discovering community of lingual practice for matching multilingual tags from folksonomies. Comput J 55(3):337–346

    Article  Google Scholar 

  9. Jung JJ (2013) Cross-lingual query expansion in multilingual folksonomies: A case study on flickr. Knowl-Based Syst 42:60– 67

    Article  Google Scholar 

  10. Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM) 46(5):604–632

    Article  MathSciNet  MATH  Google Scholar 

  11. Lee I, Cai G, Lee K (2014) Exploration of geo-tagged photos through data mining approaches. Expert Syst Appl 41(2):397– 405

    Article  Google Scholar 

  12. Li X, Cai H, Zi H, Yang Y, Zhou X (2014) Social event identification and ranking on flickr. World Wide Web:1–27

  13. Morrison JP (2008) Tagging and searching: Search retrieval effectiveness of folksonomies on the world wide web. Inf Process Manag 44(4):1562–1579

    Article  MathSciNet  Google Scholar 

  14. Nguyen DT, Jung JE (2015) Real-time event detection on social data stream. Mobile Networks and Applications 20(4):475– 486

    Article  Google Scholar 

  15. Nguyen DT, Jung JE (2017) Real-time event detection for online behavioral analysis of big social data. Futur Gener Comput Syst 66:137–145

    Article  Google Scholar 

  16. Nguyen HL et al. (2015) KELabTeam: a statistical approach on figurative language sentiment analysis in twitter. In: Proceedings of the 9th international workshop on semantic evaluation (SemEval 2015), pp 679–683

  17. Nguyen HL, Jung JJ (2015) Privacy-aware framework for matching online social identities in multiple social networking services. Cybern Syst 46(1-2):69–83

    Article  Google Scholar 

  18. Nguyen TT, Nguyen HL, Huwang D, Jung JJ (2015) Pagerank-based approach on ranking social events: a case study with flickr. In: Proceedings of the 2nd NAFOSTED conference on information and computer science (NICS 2015), pp. 147–152, IEEE, HCM, Vietnam, September 16–18, 2015

  19. Nguyen TT, Jung JJ (2014) Exploiting geotagged resources to spatial ranking by extending hits algorithm. Computer Science and Information Systems 12(1):185–201

    Google Scholar 

  20. Pham XH, Nguyen TT, Jung JJ, Nguyen NT (2014) <A,V>-spear: a new method for expert based recommendation systems. Cybern Syst 45(2):165–179

  21. Yang Y, Pierce T, Carbonell J (1998) A study of retrospective and on-line event detection. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval, pages 28–36 ACM

  22. Zhong Z, Liu Z (2010) Ranking events based on event relation graph for a single document. Inf Technol J 9(1):174–178

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2014R1A2A2A05007154).

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Authors and Affiliations

  1. Department of Computer Engineering, Chung-Ang University, Seoul, Korea

    Jai E. Jung

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  1. Jai E. Jung
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Correspondence to Jai E. Jung.

Additional information

This paper is significantly revised from earlier version presented at the 2nd NAFOSTED Conference on Information and Computer Science (NICS 2015) held in HCM, Vietnam in September 16–18, 2015.

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Jung, J.E. Discovering Social Bursts by Using Link Analytics on Large-Scale Social Networks. Mobile Netw Appl 22, 625–633 (2017). https://doi.org/10.1007/s11036-016-0804-7

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