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Mobile Networks and Applications

, Volume 22, Issue 2, pp 218–227 | Cite as

Building the Multi-Modal Storytelling of Urban Emergency Events Based on Crowdsensing of Social Media Analytics

  • Zheng XuEmail author
  • Yunhuai Liu
  • Hui Zhang
  • Xiangfeng Luo
  • Lin Mei
  • Chuanping Hu
Article

Abstract

With the development of Web 2.0, ubiquitous computing, and corresponding technologies, social media has the ability to provide the concepts of information contribution, diffusion, and exchange. Different from the permitting the general public to issue the user-generated information, social media has enabled them to avoid the need to use centralized, authoritative agencies. One of the important functions of Weibo is to monitor real time urban emergency events, such as fire, explosion, traffic jam, etc. Weibo user can be seen as social sensors and Weibo can be seen as the sensor platform. In this paper, the proposed method focuses on the step for storytelling of urban emergency events: given the Weibo posts related to a detected urban emergency event, the proposed method targets at mining the multi-modal information (e.g., images, videos, and texts), as well as storytelling the event precisely and concisely. To sum up, we propose a novel urban emergency event storytelling method to generate multi-modal summary from Weibo. Specifically, the proposed method consists of three stages: irrelevant Weibo post filtering, mining multi-modal information and storytelling generation. We conduct extensive case studies on real-world microblog datasets to demonstrate the superiority of the proposed framework.

Keywords

Crowdsensing Emergency events Multimedia storytelling Social media 

Notes

Acknowledgement

This work was supported in part by the National Science and Technology Major Project under Grant 2013ZX01033002-003, in part by the National Natural Science Foundation of China under Grant 61300202, and in part by the Science Foundation of Shanghai under Grant 13ZR1452900.

References

  1. 1.
    Chua T-S, Luan H, Sun M, Yang S (2012) Next: nus-Tsinghua center for extreme search of user-generated content. IEEE MultiMedia Mag 19(3):81–87CrossRefGoogle Scholar
  2. 2.
    Ma H (2011) Internet of things: objectives and scientific challenges. J Computer Science and Tech 26(6):919–924CrossRefGoogle Scholar
  3. 3.
    Guo B et al (2013) Opportunistic IoT: exploring the harmonious interaction between human and the internet of things. J Network and Computer Applications 36(6):1531–1539CrossRefGoogle Scholar
  4. 4.
    Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39CrossRefGoogle Scholar
  5. 5.
    Guo B et al. (2014) From participatory sensing to mobile crowd sensing. IEEE PerCom WorkshopsGoogle Scholar
  6. 6.
    Lane N et al. (2008) Urban sensing systems: opportunistic or participatory?, Proc Hot Mobile pp. 11–16Google Scholar
  7. 7.
    Chakrabarti D, Punera K (2011) Event summarization using tweets. In: Proc. ICWSM, pp. 66–73Google Scholar
  8. 8.
    Ma H, Zhao D, Yuan P (2014) Opportunities in mobile crowd sensing. IEEE Commun Mag 52(8):29–35CrossRefGoogle Scholar
  9. 9.
    Guo B, Chen H, Yu Z, Xie X, Huangfu S, Zhang D (2015) FlierMeet: a mobile crowdsensing system for cross-space public information reposting, tagging, and sharing. IEEE Trans Mob Comput 14(10):2020–2033CrossRefGoogle Scholar
  10. 10.
    Guo B, Yu Z, Zhang D, Zhou X (2014) From participatory sensing to mobile crowd sensing. In: Proc. IEEE Pervasive Comput. Commun. Workshops, pp. 593–598Google Scholar
  11. 11.
    Zhou P, Zheng Y, Li M (2012) How long to wait?: Predicting bus arrival time with mobile phone based participatory sensing. In: Proc 10th Int Conf Mobile Syst Appl Serv, pp. 379–392Google Scholar
  12. 12.
    Rana RK, Chou CT, Kanhere SS, Bulusu N, Hu W (2010) Earphone: an end-to-end participatory urban noise mapping system. In: Proc 9th ACM/IEEE Int Conf Inf Process Sensor Netw, pp. 105–116Google Scholar
  13. 13.
    Zheng Y, Liu F, Hsieh HP (2013) U-Air: when urban air quality inference meets big data. In: Proc. 19th ACM SIGKDD Int Conf Knowl Discovery Data Mining, pp. 1436–1444Google Scholar
  14. 14.
    Koukoumidis E, Peh LS, Martonosi MR (2011) SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory. In: Proc 9th Int Conf Mobile Syst Appl Serv, pp. 127–140Google Scholar
  15. 15.
    Xu C, Li S, Liu G, Zhang Y, Miluzzo E, Chen YF, Li J, Firner B (2013) Crowdþþ: unsupervised speaker count with smartphones. In: Proc ACM Int Joint Conf. Pervasive Ubiquitous Comput, pp. 43–52Google Scholar
  16. 16.
    Chon Y, Lane ND, Li F, Cha H, Zhao F (2012) Automatically characterizing places with opportunistic crowdsensing using smartphones. In: Proc 14th Int Conf Ubiquitous Comput, pp. 481–490Google Scholar
  17. 17.
    Faulkner M, Olson M, Chandy R, Krause J, Chandy KM, Krause A (2011) The next big one: Detecting earthquakes and other rare events from community-based sensors. In: Proc 10th Int Conf Inf Process. Sensor Netw, pp. 13–24Google Scholar
  18. 18.
    Bao X, Choudhury R (2010) Movi: Mobile phone based video highlights via collaborative sensing. In: Proc 8th Int Conf Mobile Syst Appl Serv, pp. 357–370Google Scholar
  19. 19.
    Xie L, Natsev A, He X, Kender JR, Hill ML, Smith JR (2013) Tracking large-scale video remix in real-world events. IEEE Trans Multimedia 15(6):1244–1254CrossRefGoogle Scholar
  20. 20.
    Chen Y, Cheng A, Hsu WH (2013) Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Trans. Multimedia 15(6):1283–1295CrossRefGoogle Scholar
  21. 21.
    Zhang D, Wang L, Xiong H, Guo B (2014) 4W1H in mobile crowd sensing. IEEE Commun Mag 52(8):42–48CrossRefGoogle Scholar
  22. 22.
    Pankratius V, Lind F, Coster A, Erickson P, Semeter J (2014) Mobile crowd sensing in space weather monitoring: the mahali project. IEEE Commun Mag 52(8):22–28CrossRefGoogle Scholar
  23. 23.
    Rosen S, Lee S, Lee J, Congdon P, Mao Z, Burden K (2014) MCNet. Crowdsourcing wireless performance measurements through the eyes of mobile devices. IEEE Commun Mag 52(10):86–91CrossRefGoogle Scholar
  24. 24.
    Hong L, Ahmed A, Gurumurthy S et al. (2012) Discovering geographical topics in the twitter stream. In: WWW 2012, pp. 769–778Google Scholar
  25. 25.
    Cataldi M, Di Caro L, Schifanella C (2010) Emerging topic detection on twitter based on temporal and social terms evaluation. In: International Workshop on Multimedia Data Mining, pp. 4:1–4:10Google Scholar
  26. 26.
    Lehmann J, Goncalves B, Ramasco JJ, Cattuto C (2012) Dynamical classes of collective attention in twitter. In: WWW 2012, pp. 251–260Google Scholar
  27. 27.
    Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: Real-time event detection by social sensors. In: WWW 2010, pp. 851–860Google Scholar
  28. 28.
    Sankaranarayanan J, Samet H, Teitler BE, Lieberman MD, Sperling J (2009) Twitterstand: News in tweets. In: ACM SIGSPATIAL, pp. 42–51Google Scholar
  29. 29.
    Becker H, Naaman M, Gravano L (2011) Beyond trending topics: Real-world event identification on twitter. In: International Conference on Weblogs and Social Media, Barcelona, Catalonia, SpainGoogle Scholar
  30. 30.
    Walther M, Kaisser M (2013) Geo-spatial event detection in the twitter stream. In: European Conference on Advances in Information Retrieval, pp. 356–367Google Scholar
  31. 31.
    Sheth A, Jadhav A, Kapanipathi P et al. (2014) Twitris: a system for collective social intelligence. In: Encyclopedia of Social Network Analysis and Mining, pp. 2240–2253Google Scholar
  32. 32.
    Crooks A, Croitoru A, Stefanidis A, Radzikowski J (2012) Earthquake: twitter as a distributed sensor system. Transaction in GIS, pp. 1–26Google Scholar
  33. 33.
    Longueville B, Smith R, Luraschi G (2009) OMG, from here I can see the flames, a use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: Proceedings of the International Workshop on Location-Based Social Networks, pp. 73–80Google Scholar
  34. 34.
    Liu Y, Alexandrova T, Nakajima T (2013) Using Stranger as Sensors: Temporal and Geo-sensitive Question Answering via Social Media. In: Proceedings of the 22th international World Wide Web conference, pp. 803–813Google Scholar
  35. 35.
    Qu Y, Zhang J (2013) Trade area analysis using user generated mobile location data. In: Proceedings of the 22th international World Wide Web conference, pp. 1053–1063Google Scholar
  36. 36.
    Sharifi B, Hutton M-A, Kalita J (2010) Summarizing microblogs automatically. In: Proc. NAACL HLT, pp. 685–688Google Scholar
  37. 37.
    Inouye D, Kalita JK (2011) Comparing Twitter summarization algorithms for multiple post summaries. In: Proc Social Com, pp. 298–306Google Scholar
  38. 38.
    Lin C, Lin C, Li J, Wang D, Chen Y, Li T (2012) Generating event storylines from microblogs. In: Proc. CIKM, pp. 175–184Google Scholar
  39. 39.
    Xu Z et al (2016) Crowdsourcing based description of urban emergency events using social media big data. IEEE Transactions on Cloud Computing. doi: 10.1109/TCC.2016.2517638 Google Scholar
  40. 40.
    Xu Z, Zhang H, Sugumaran V, Choo R, Mei L, Zhu Y (2016) Participatory sensing-based semantic and spatial analysis of urban emergency events using mobile social media. EURASIP J Wirel Commun Netw 2016:44CrossRefGoogle Scholar
  41. 41.
    Xu Z, Zhang H, Sugumaran V, Choo R, Mei L, Zhu Y (2016) Building knowledge base of urban emergency events based on crowdsourcing of social media. Concurrency and Computation: Practice and Experience. doi: 10.1002/cpe.3780 Google Scholar
  42. 42.
    Xu Z et al. (2015) Crowd Sensing of Urban Emergency Events based on Social Media Big Data. The 2014 I.E. International Conference on Big Data Science and Engineering, pp. 605–610Google Scholar
  43. 43.
    Xuan J, Luo X, Zhang G, Lu J, Xu Z (2016) Uncertainty analysis for the keyword system of web events. IEEE Transactions on Systems, Man, and Cybernetics: Systems. doi: 10.1109/TSMC.2015.2470645 Google Scholar
  44. 44.
    Liu W, Luo X, Gong Z, Xuan J, Kou NM, Xu Z (2016) Discovering the core semantics of event from social media. Futur Gener Comput Syst. doi: 10.1016/j.future.2015.11.023 Google Scholar
  45. 45.
    Xu Z et al (2015) Crowdsourcing based social media data analysis of urban emergency events. Multimedia tools and applications. doi: 10.1007/s11042-015-2731-1 Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zheng Xu
    • 1
    • 2
    Email author
  • Yunhuai Liu
    • 1
  • Hui Zhang
    • 2
  • Xiangfeng Luo
    • 3
  • Lin Mei
    • 1
  • Chuanping Hu
    • 1
  1. 1.The Third Research Institute of the Ministry of Public SecurityShanghaiChina
  2. 2.Tsinghua UniversityBeijingChina
  3. 3.Shanghai UniversityShanghaiChina

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