skip to main content
10.1145/3277103.3277123acmotherconferencesArticle/Chapter ViewAbstractPublication PagescommConference Proceedingsconference-collections
research-article

Social Networks as Real-time Data Distribution Platforms for Smart Cities

Published: 03 October 2018 Publication History

Abstract

Cities need to collect, store and process huge amounts of data in order to provide smarter services, such as mobility, traffic, and disaster management. Social networks, such as Twitter and Instagram, are participatory sensing systems where communities generate a large amount of data every day. Existing studies have been often focused on collecting and transforming social media data into insights that may help citizens, with no concern whether the dataset represents the phenomenon they want to understand. In this paper, we explore the adequacy of social networking services to be used as platforms for smart city applications. Here we propose a methodology composed of three steps and undertake a performance analysis with Twitter as a proof of concept. Our results show that in spite of well-known data delivery constraints of the Twitter Streaming API, the majority of tweets could be successfully captured with a delay that makes it adequate to real-time processing.

References

[1]
H. Achrekar, A. Gandhe, R. Lazarus, S. Yu, and B. Liu. 2011. Predicting flu trends using twitter data. Computer Communications Workshops (INFOCOM), pp. 702--707.
[2]
J. Albuquerque, M. Eckle, B. Herfort, and A. Zipf. 2016. Crowdsourcing geographic information for disaster management and improving urban resilience: an overview of recent developments and lessons learned. European Handbook of Crowdsourced Geographic Information. pp. 309--321.
[3]
F. Angaramo and C. Rossi. 2018. Online clustering and classification for realtime event detection in Twitter. International Conference on Information Systems for Crisis Response and Management (ISCRAM), pp. 1098--1107.
[4]
E. Aramaki, S. Maskawa, and M. Morita. 2011. Twitter catches the flu: detecting influenza epidemics using Twitter. International Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1568--1576.
[5]
L. Assis, J. Albuquerque, B. Herfort, E. Steiger, and F. Horita. 2016. Geographic prioritization of social network messages in near real-time using sensor data stream: an application to floods. Brazilian Journal of Cartography and Brazilian Society of Cartography, 68(6), pp. 1--16.
[6]
S. Ghosh, M. Zafar, P. Bhattacharya, N. Sharma, N. Ganguly, and K. Gummadi. 2013. On sampling the wisdom of crowds: Random vs. expert sampling of the twitter stream. 22nd ACM Intl conference on Conference on Information & Knowledge Management (CIKM), pp. 1739--1744.
[7]
S. González-Bailón, N. Wang, A. Rivero, J. Borge-Holthoefer, and Y. Moreno. 2012. Assessing the bias in communication networks sampled from twitter. arXiv preprint arXiv:1212.1684.
[8]
M. Goodchild. 2007. Citizen as sensors: the world of volunteered geography. GeoJournal, 29(4), pp.211--221.
[9]
B. Guo, Z. Yu, X. Zhou, and C. Zhang. 2014. From participatory sensing to Mobile Crowd Sensing. 2014 IEEE PERCOM Workshops, pp. 593--598, March 2014.
[10]
F. Horita, J. Albuquerque, V. Marchezini, and E. Mendiondo. 2017. Bridging the gap between decision-making and emerging big data sources: an application of a model-based framework to disaster management in Brazil. Decision Support Systems, 97, pp. 12--22.
[11]
K. Joseph, P. Landwehr, and K. Carley. 2014. Two 1%s don't make a whole: Comparing simultaneous samples from Twitter's streaming API. In Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction (SBP), pp. 75--83.
[12]
Y. Kryvasheyeu, H. Chen, N. Obradovich, E. Moro, P. van Hentenryck, J. Fowler, and M. Cebrian. 2016. Rapid assessment of disaster damage using social media activity. Science Advances, 2(3), pp. 1--11.
[13]
R. Li, K. Lei, R. Khadiwala, and K. Chang. 2012. Tedas: A twitter-based event detection and analysis system. 28th International Conference on Data Engineering (ICDE), pp. 1273--1276.
[14]
R. Li, S. Wang, and C. Chang. 2014. Automatic topic-focused monitor for twitter stream. PVLDB, Hangzhou, China, pp. 1966--1977.
[15]
S. Lomborg, and A. Bechmann. 2014. Using APIs for data collection on social media. The Information Society, 30(4), pp. 256--265.
[16]
F. Morstatter, J. Pfeffer, H. Liu, and K. Carley, 2013. Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose. International AAAI Conference on Weblogs and Social Media (ICWSM), Ann Arbor, USA, pp. 1--9.
[17]
R. Perera, S. Anand, K. Subbalakshmi, and R. Chandramouli. 2010. Twitter analytics: Architecture, tools and analysis. In Proceedings of the 2010 Military Communications Conference (MILCOM), San Jose, USA, pp. 2186--2191.
[18]
T. Sakaki, M. Okazaki, and Y. Matsuo. 2010. Earthquake shakes Twitter users: real-time event detection by social sensors. 19th International Conference on World Wide Web (WWW), pp. 851--860.
[19]
J. Weng and B. Lee. Event detection in twitter. 2011. Proceedings of the International AAAI Conference on Web and Social Media (ICWSM), Barcelona, Spain, pp. 401--408.
[20]
I. Zyrianoff, F. Borelli, G. Biondi, A. Heideker, and C. Kamienski, 2018. Scalability of Real-Time IoT-based Applications for Smart Cities. IEEE Symposium on Computers and Communications (ISCC 2018), June 2018.

Cited By

View all
  • (2024)A multi-DL fuzzy approach to image recognition for a real-time traffic alert systemJournal of Ambient Intelligence and Smart Environments10.3233/AIS-230433(1-17)Online publication date: 20-Jun-2024
  • (2024)Breaking barriers for breaking ground: A categorisation of public sector challenges to smart city project implementationPublic Policy and Administration10.1177/09520767241263233Online publication date: 19-Jul-2024
  • (2022)Participatory Citizen Sensing with a Focus on Urban IssuesInternet of Things for Smart Environments10.1007/978-3-031-09729-4_5(71-91)Online publication date: 17-Sep-2022

Index Terms

  1. Social Networks as Real-time Data Distribution Platforms for Smart Cities

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      LANC '18: Proceedings of the 10th Latin America Networking Conference
      October 2018
      130 pages
      ISBN:9781450359221
      DOI:10.1145/3277103
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      In-Cooperation

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 03 October 2018

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. Performance Analysis
      2. Smart City Platform
      3. Social Networks

      Qualifiers

      • Research-article
      • Research
      • Refereed limited

      Conference

      LANC '18
      LANC '18: Latin America Networking Conference
      October 3 - 4, 2018
      São Paulo, Brazil

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)9
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 17 Feb 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)A multi-DL fuzzy approach to image recognition for a real-time traffic alert systemJournal of Ambient Intelligence and Smart Environments10.3233/AIS-230433(1-17)Online publication date: 20-Jun-2024
      • (2024)Breaking barriers for breaking ground: A categorisation of public sector challenges to smart city project implementationPublic Policy and Administration10.1177/09520767241263233Online publication date: 19-Jul-2024
      • (2022)Participatory Citizen Sensing with a Focus on Urban IssuesInternet of Things for Smart Environments10.1007/978-3-031-09729-4_5(71-91)Online publication date: 17-Sep-2022

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media