Textual Processing in Social Network Analysis

  • Flora AmatoEmail author
  • Walter Balzano
  • Giovanni Cozzolino
  • Alessandro de Luca
  • Francesco Moscato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 927)


Social Networks are responsible of generating a huge amount of information, intrinsically heterogeneous and coming from different sources. In the social networks domain, the number of active users is impressive, active users process and publish information in different formats and data remain heterogeneous in their topics and in the published media (text, video, images, audio, etc.). In this work, we present a general framework for event detection in processing of heterogeneous data from social networks. The framework we propose, implements some techniques that users can exploit for malicious events detection on Twitter.


  1. 1.
    Diakopoulos, N., Naaman, M., Kivran-Swaine, F.: Diamonds in the rough: social media visual analytics for journalistic inquiry. In: 2010 IEEE Symposium on Visual Analytics Science and Technology (VAST), pp. 115–122, October 2010Google Scholar
  2. 2.
    Yardi, S., Boyd, D.: Tweeting from the town square: measuring geographic local networks. In: International Conference on Weblogs and Social Media. American Association for Artificial Intelligence, May 2010Google Scholar
  3. 3.
    Amato, F., Cozzolino, G., Mazzeo, A., Moscato, F.: Detect and correlate information system events through verbose logging messages analysis. Computing (2018). Cited By 0; Article in PressGoogle Scholar
  4. 4.
    Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 851–860. ACM, New York (2010)Google Scholar
  5. 5.
    Naaman, M., Boase, J., Lai, C.-H.: Is it really about me?: Message content in social awareness streams. In: Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work, CSCW 2010, pp. 189–192. ACM, New York (2010)Google Scholar
  6. 6.
    Becker, H., Naaman, M., Gravano, L.: Beyond trending topics: real-world event identification on Twitter (2011)Google Scholar
  7. 7.
    Becker, H., Naaman, M., Gravano, L.: Learning similarity metrics for event identification in social media. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, WSDM 2010, pp. 291–300. ACM, New York (2010)Google Scholar
  8. 8.
    Atefeh, F., Khreich, W.: A survey of techniques for event detection in Twitter. Comput. Intell. 31(1), 132–164 (2015)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Essmaeel, K., Gallo, L., Damiani, E., De Pietro, G., Dipanda, A.: Comparative evaluation of methods for filtering kinect depth data. Multimedia Tools Appl. 74(17), 7331–7354 (2015)CrossRefGoogle Scholar
  10. 10.
    Aramaki, E., Maskawa, S., Morita, M.: Twitter catches the flu: detecting influenza epidemics using Twitter. In: Proceedings of Conference on Empirical Methods in Natural Language Processing (2011)Google Scholar
  11. 11.
    Lamb, A., Paul, M.J., Dredze, M.: Separating fact from fear: tracking flu infections on Twitter. In: HLT-NAACL, pp. 789–795 (2013)Google Scholar
  12. 12.
    Chon, J., Raymond, R., Wang, H., Wang, F.: Modeling flu trends with real-time geo-tagged Twitter data streams. In: Wireless Algorithms, Systems, and Applications, pp. 60–69. Springer, Heidelberg (2015)Google Scholar
  13. 13.
    Amato, F., Cozzolino, G., Mazzeo, A., Romano, S.: Intelligent medical record management: a diagnosis support system. Int. J. High Performance Comput. Netw. 12(4), 391–399 (2018)CrossRefGoogle Scholar
  14. 14.
    Diaz-Aviles, E., Stewart, A.: Tracking Twitter for epidemic intelligence: case study: Ehec/hus outbreak in Germany. In: Proceedings of the 4th Annual ACM Web Science Conference, WebSci 2012, pp. 82–85. ACM, New York (2012)Google Scholar
  15. 15.
    Chunara, R., Andrews, J.R., Brownstein, J.S.: Social and news media enable estimation of epidemiological patterns early in the 2010 Haitian cholera outbreak. Am. J. Trop. Med. Hyg. 86(1), 39–45 (2012)CrossRefGoogle Scholar
  16. 16.
    Gomide, J., Veloso, A., Meira Jr, W., Almeida, V., Benevenuto, F., Ferraz, F., Teixeira, M.: Dengue surveillance based on a computational model of spatio-temporal locality of Twitter. In: Proceedings of ACM WebSci 2011 (2011)Google Scholar
  17. 17.
    Piccialli, F., Chianese, A.: The internet of things supporting context-aware computing: a cultural heritage case study. Mob. Netw. Appl. 22(2), 332–343 (2017)CrossRefGoogle Scholar
  18. 18.
    Chianese, A., Marulli, F., Piccialli, F., Valente, I.: A novel challenge into multimedia cultural heritage: an integrated approach to support cultural information enrichment. In: 2013 International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 217–224. IEEE (2013)Google Scholar
  19. 19.
    Piccialli, F., Jung, J.E.: Understanding customer experience diffusion on social networking services by big data analytics. Mob. Netw. Appl. 22(4), 605–612 (2017)Google Scholar
  20. 20.
    Amato, F., Moscato, V., Picariello, A., Piccialli, F.: SOS: a multimedia recommender system for online social networks. Future Gen. Comput. Syst. (2017)Google Scholar
  21. 21.
    Xhafa, F., Asimakopoulou, E., Bessis, N., Barolli, L., Takizawa, M.: An event-based approach to supporting team coordination and decision making in disaster management scenarios. In: 2011 Third International Conference on Intelligent Networking and Collaborative Systems (INCoS), pp. 741–745. IEEE (2011)Google Scholar
  22. 22.
    Xhafa, F., Barolli, L.: Semantics, intelligent processing and services for big data (2014)Google Scholar
  23. 23.
    Moore, P., Xhafa, F., Barolli, L.: Semantic valence modeling: emotion recognition and affective states in context-aware systems. In: 2014 28th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 536–541. IEEE (2014)Google Scholar
  24. 24.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)Google Scholar
  25. 25.
    Cilardo, A.: Exploring the potential of threshold logic for cryptography-related operations. IEEE Trans. Comput. 60(4), 452–462 (2011)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Cilardo, A., Fusella, E., Gallo, L., Mazzeo, A.: Joint communication scheduling and interconnect synthesis for FPGA-based many-core systems. In: Design, Automation and Test in Europe Conference and Exhibition (DATE), pp. 1–4. IEEE (2014)Google Scholar
  27. 27.
    Cilardo, A., Fusella, E., Gallo, L., Mazzeo, A.: Exploiting concurrency for the automated synthesis of MPSoC interconnects. ACM Trans. Embedded Comput. Syst. (TECS) 14(3), 57 (2015)Google Scholar
  28. 28.
    Cilardo, A., Durante, P., Lofiego, C., Mazzeo, A.: Early prediction of hardware complexity in HLL-to-HDL translation. In: 2010 International Conference on Field Programmable Logic and Applications (FPL), pp. 483–488. IEEE (2010)Google Scholar
  29. 29.
    Hobbs, J.R., Riloff, E.: Information extraction. In: Indurkhya, N., Damerau, F.J. (eds.) Handbook of Natural Language Processing, 2nd edn. CRC Press, Taylor and Francis Group, Boca Raton (2010)Google Scholar
  30. 30.
    Butler, C.S.: Statistics in Linguistics. Blackwell, Oxford (1985)Google Scholar
  31. 31.
    Biber, D., Reppen, R., Conrad, S.: Corpus Linguistics: Investigating Language Structure and Use. Cambridge University Press, Cambridge (1998)CrossRefGoogle Scholar
  32. 32.
    Kennedy, G.D.: An Introduction to Corpus Linguistics. Longman, London (1998)Google Scholar
  33. 33.
    Balzano, W., Murano, A., Vitale, F.: Hypaco–a new model for hybrid paths compression of geodetic tracks. In: The International Conference on Data Compression, Communication, Processing and Security, CCPS 2016 (2016)Google Scholar
  34. 34.
    Balzano, W., Murano, A., Stranieri, S.: Logic-based clustering approach for management and improvement of VANETs. J. High Speed Netw. 23(3), 225–236 (2017)CrossRefGoogle Scholar
  35. 35.
    Balzano, W., Murano, A., Vitale, F.: SNOT-WiFi: sensor network-optimized training for wireless fingerprinting. J. High Speed Netw. 24(1), 79–87 (2018)CrossRefGoogle Scholar
  36. 36.
    Riloff, E.: Automatically constructing a dictionary for information extraction tasks. In: Proceedings of the Eleventh National Conference on Artificial Intelligence (1993)Google Scholar
  37. 37.
    Grishman, R.: Information extraction: Capabilities and challenges (2012)Google Scholar
  38. 38.
    Pantel, P., Pennacchiotti, M.: Espresso: leveraging generic patterns for automatically harvesting semantic relations. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Flora Amato
    • 1
    Email author
  • Walter Balzano
    • 1
  • Giovanni Cozzolino
    • 1
  • Alessandro de Luca
    • 1
  • Francesco Moscato
    • 2
  1. 1.Department of Electrical Engineering and Information TechnologyUniversity of Naples “Federico II”NaplesItaly
  2. 2.Department of Scienze PoliticheUniversity of Campania “Luigi Vanvitelli”CasertaItaly

Personalised recommendations