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

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.

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Notes

  1. 1.

    www.weibo.com

  2. 2.

    www.twitter.com

  3. 3.

    A road in Shanghai, China

  4. 4.

    http://ictclas.org/

  5. 5.

    https://www.openstreetmap.org/

  6. 6.

    http://wkf.shu.edu.cn/

  7. 7.

    The biggest city with about 23 million people in China

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):81–87

    Article  Google Scholar 

  2. 2.

    Ma H (2011) Internet of things: objectives and scientific challenges. J Computer Science and Tech 26(6):919–924

    Article  Google 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):1531–1539

    Article  Google Scholar 

  4. 4.

    Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39

    Article  Google Scholar 

  5. 5.

    Guo B et al. (2014) From participatory sensing to mobile crowd sensing. IEEE PerCom Workshops

  6. 6.

    Lane N et al. (2008) Urban sensing systems: opportunistic or participatory?, Proc Hot Mobile pp. 11–16

  7. 7.

    Chakrabarti D, Punera K (2011) Event summarization using tweets. In: Proc. ICWSM, pp. 66–73

  8. 8.

    Ma H, Zhao D, Yuan P (2014) Opportunities in mobile crowd sensing. IEEE Commun Mag 52(8):29–35

    Article  Google 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):2020–2033

    Article  Google 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. 593–598

  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. 379–392

  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. 105–116

  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. 1436–1444

  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. 127–140

  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. 43–52

  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. 481–490

  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. 13–24

  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. 357–370

  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):1244–1254

    Article  Google 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):1283–1295

    Article  Google Scholar 

  21. 21.

    Zhang D, Wang L, Xiong H, Guo B (2014) 4W1H in mobile crowd sensing. IEEE Commun Mag 52(8):42–48

    Article  Google 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):22–28

    Article  Google 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):86–91

    Article  Google Scholar 

  24. 24.

    Hong L, Ahmed A, Gurumurthy S et al. (2012) Discovering geographical topics in the twitter stream. In: WWW 2012, pp. 769–778

  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. 4:1–4:10

  26. 26.

    Lehmann J, Goncalves B, Ramasco JJ, Cattuto C (2012) Dynamical classes of collective attention in twitter. In: WWW 2012, pp. 251–260

  27. 27.

    Sakaki T, Okazaki M, Matsuo Y (2010) Earthquake shakes twitter users: Real-time event detection by social sensors. In: WWW 2010, pp. 851–860

  28. 28.

    Sankaranarayanan J, Samet H, Teitler BE, Lieberman MD, Sperling J (2009) Twitterstand: News in tweets. In: ACM SIGSPATIAL, pp. 42–51

  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, Spain

  30. 30.

    Walther M, Kaisser M (2013) Geo-spatial event detection in the twitter stream. In: European Conference on Advances in Information Retrieval, pp. 356–367

  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. 2240–2253

  32. 32.

    Crooks A, Croitoru A, Stefanidis A, Radzikowski J (2012) Earthquake: twitter as a distributed sensor system. Transaction in GIS, pp. 1–26

  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. 73–80

  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. 803–813

  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. 1053–1063

  36. 36.

    Sharifi B, Hutton M-A, Kalita J (2010) Summarizing microblogs automatically. In: Proc. NAACL HLT, pp. 685–688

  37. 37.

    Inouye D, Kalita JK (2011) Comparing Twitter summarization algorithms for multiple post summaries. In: Proc Social Com, pp. 298–306

  38. 38.

    Lin C, Lin C, Li J, Wang D, Chen Y, Li T (2012) Generating event storylines from microblogs. In: Proc. CIKM, pp. 175–184

  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 2016:44

    Article  Google 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. 605–610

  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 

Download references

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.

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Correspondence to Zheng Xu.

Additional information

This paper is the extended version (about 50% new content) accepted by 9th EAI International Conference on Mobile Multimedia Communications (MobiMedia 2016).

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Cite this article

Xu, Z., Liu, Y., Zhang, H. et al. Building the Multi-Modal Storytelling of Urban Emergency Events Based on Crowdsensing of Social Media Analytics. Mobile Netw Appl 22, 218–227 (2017). https://doi.org/10.1007/s11036-016-0789-2

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Keywords

  • Crowdsensing
  • Emergency events
  • Multimedia storytelling
  • Social media