Skip to main content
SpringerLink
Log in

Twitter as a Personalizable Information Service

  • Chapter
  • First Online:
Multimedia Data Mining and Analytics

  • 2456 Accesses

Abstract

Twitter is a free social networking microblogging service that allows registered members to broadcast, in real-time, short posts called tweets. Twitter members can broadcast tweets and follow other users’ tweets by using multiple devices, making this information system one of the fastest in the world. In this chapter, we leverage this characteristic to introduce a novel topic-detection method aimed at informing, in real-time, a specific user about the most emerging arguments expressed by the network around his/her domain interests. With this goal, we aim at formalizing the information spread over the network by studying the topology of the network and by modeling the implicit and explicit connections among the users. Then, we propose an innovative term aging model, based on a biological metaphor, to retrieve the freshest arguments of discussion, represented through a minimal set of terms, expressed by the community within the foci of interest of a specific user. We finally test the proposed model through various experiments and user studies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.twitter.com.

  2. 2.

    http://www.tumblr.com.

  3. 3.

    http://www.facebook.com.

  4. 4.

    http://palatnikfactor.com/2010/01/29/twitter-demographic-report-who-is-really-on-twitter/.

  5. 5.

    A deeper analysis of the Twitter network is also provided in [1].

  6. 6.

    http://tweettabs.com.

  7. 7.

    http://where-what-when.husk.org.

  8. 8.

    http://www.flickr.com.

  9. 9.

    http://trendistic.com.

  10. 10.

    http://twopular.com.

  11. 11.

    These protests have also been knows for the massive use of Twitter post because of the protesters’ reliance on Twitter and other social-networking Internet sites to communicate with each other.

  12. 12.

    The dumping factor \(d\), introduced by the authors in [3], represents the probability that a “random surfer” of the graph \(G\) moves from a user to another; it is usually set to \(0.85\).

  13. 13.

    Notice that, considering that the semantic similarity of each pair of term is based on the external knowledge base of Wikipedia, this information is precomputed offline and it is periodically updated in order to reflect the novel information introduced in Wikipedia.

  14. 14.

    We used \(\chi =5\) as default value.

  15. 15.

    http://apiwiki.Twitter.com.

  16. 16.

    The sampling rate of the used standard Twitter account is 1 % over an average of 200 million per day.

  17. 17.

    http://www.ap.org.

  18. 18.

    In our experimental evaluation we set \(r\) equals to 30 in order to adapt our system to the high dynamicity of Twitter users. This is clearly in agreement with the experimental results shown in [27]. In fact, in this work, the authors revealed that there are few topics that last for longer times, while most topics decay in about 20–40 min.

  19. 19.

    Note that we do not compute any recall value. In fact, recall is strictly dependent on the considered ground truth (CNN, AP, some online newspaper, etc.) and its news domain. For example, some important news can be reported by some authoritative news source and ignored by others. For this, counting how many news articles are detected is dependent on a specific ground truth and the resulting analyzing could not be considered significative for the evaluation task.

  20. 20.

    http://www.weibo.com.

References

  1. Poblete B, Garcia R, Mendoza M, Jaimes A (2011) In: Proceedings of the 20th ACM international conference on Information and knowledge management, CIKM ’11. ACM, New York, pp 1025–1030

    Google Scholar 

  2. Cataldi M, Di Caro L, Schifanella C (2010) In: Proceedings of the 10th international workshop on multimedia data mining, MDMKDD ’10. ACM, New York, pp 4:1–4:10. http://doi.acm.org/10.1145/1814245.1814249.

  3. Page L, Brin S, Motwani R, Winograd T (1998) In: Proceedings of the 7th international world wide web conference. ACM, New York, pp 161–172

    Google Scholar 

  4. Allan J (2002) Topic detection and tracking: event-based information organization. Kluwer International series on information retrieval. Kluwer Academic Publishers, Norwell. http://books.google.it/books?id=50hnLI_Jz3cC

  5. Makkonen J, Ahonen-Myka H, Salmenkivi T (2004) Inf Retr 7(3–4), p 347. http://dx.doi.org/10.1023/B:INRT.0000011210.12953.86

  6. Treeratpituk P, Callan T (2006) In: dg.o ’06: Proceedings of the 2006 international conference on digital government research. ACM, New York, pp 167–176. http://doi.acm.org/10.1145/1146598.1146650

  7. Goldberg D, Nichols D, Oki BM, Terry D (1992) Commun ACM 35(12):61

    Article  Google Scholar 

  8. Hassan A, Radev DR, Cho J, Joshi A (2009) In: Proceedings of the 3rd international aaai conference on weblogs and social media (ICWSM). The AAAI Press, Menlo Park, pp 34–41

    Google Scholar 

  9. Melville P, Mooney RJ, Nagarajan R (2001) In: Proceedings of the 2001 SIGIR workshop on recommender systems. ACM, New York, pp 16–23

    Google Scholar 

  10. Balabanovic M, Shoham Y (1997) Commun ACM 40:66

    Article  Google Scholar 

  11. Chen J, Nairn R, Nelson L, Bernstein M, Chi E (2010) In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI ’10. ACM, New York, pp 1185–1194. doi:10.1145/1753326.1753503

  12. Chen J, Geyer E, Dugan C, Muller M, Guy I (2009) In: CHI ’09: Proceedings of the 27th international conference on Human factors in computing systems. ACM, New York, pp 201–210. http://doi.acm.org/10.1145/1518701.1518735

  13. Jäschke R, Marinho L, Hotho A, Schmidt-Thieme M, Stumme G (2007) In: PKDD 2007. Springer, Berlin, pp 506–514. http://dx.doi.org/10.1007/978-3-540-74976-9

  14. Granovetter M (1978) Am J Sociol 83(6):1420. doi:10.1086/226707

    Article  Google Scholar 

  15. Goldenberg J, Libai B, Muller E (2001) Mark Lett 12(3):211

    Article  Google Scholar 

  16. Gruhl D, Guha R, Liben-Nowell D, Tomkins A (2004) In: Proceedings of the 13th international conference on world wide web, WWW ’04. ACM, New York, pp 491–501. doi:10.1145/988672.988739

  17. Goyal A, Bonchi F, Lakshmanan LV (2010) In: Proceedings of the third ACM international conference on web search and data mining, WSDM ’10. ACM, New York, pp 241–250. doi:10.1145/1718487.1718518

  18. Yang J, Leskovec J (2010) In: Data mining (ICDM), 2010 IEEE 10th international conference on IEEE computer society, Washington, pp 599–608

    Google Scholar 

  19. Cha M, Haddadi H, Benevenuto F, Gummadi KP (2010) In: Proceedings of the 4th international AAAI conference on weblogs and social media (ICWSM). The AAAI Press, Menlo Park, pp 10–17

    Google Scholar 

  20. Yang J, Counts S (2010) In: Proceedings of the 4th international AAAI conference on weblogs and social media (ICWSM). The AAAI Press, Menlo Park, pp 355–358

    Google Scholar 

  21. Griffiths TL, Steyvers M (2004) Proceedings of the National Academy of Sciences 101 (Suppl. 1):5228

    Google Scholar 

  22. Zhao Q, Mitra P, Chen B (2007) In: Proceedings of the 22nd national conference on artificial intelligence—volume 2, AAAI’07. AAAI Press, Menlo Park, pp 1501–1506. http://dl.acm.org/citation.cfm?id=1619797.1619886

  23. Qi Y, Candan KS (2006) In: HYPERTEXT ’06. ACM, New York, pp 1–10. http://doi.acm.org/10.1145/1149941.1149944

  24. Favenza A, Cataldi M, Sapino ML, Messina A (2008) In: NLDB ’08. Springer, Berlin, pp 226–232. http://dx.doi.org/10.1007/978-3-540-69858-6

  25. Wu Y, Ding Y, Wang X, Xu J (2010) J Comput 5(4):549

    Article  MathSciNet  Google Scholar 

  26. Abrol S, Khan L (2010) In: GIR ’10: Proceedings of the 6th workshop on geographic information retrieval. ACM, New York, pp 1–8

    Google Scholar 

  27. Asur S, Huberman BA, Szabó G, Wang C (2011) In: Fifth international AAAI conference on weblogs and social media. The AAAI Press, Menlo Park, California

    Google Scholar 

  28. Katz JS, Katz JS, Martin BR, Martin BR (1997) Res Policy 26:1

    Article  Google Scholar 

  29. Crane D (1969) Am Soc Rev 3:335

    Article  Google Scholar 

  30. Chubin DE (1976) Soc Q 17(4):448

    Article  Google Scholar 

  31. Shapin S (1981) Med Hist 25(3):341

    Article  Google Scholar 

  32. de Beaver D, Rosen R (1979) Scientometrics 1(2):133

    Article  Google Scholar 

  33. Melin G, Persson O (1996) Scientometrics 36:363

    Article  Google Scholar 

  34. Newman MEJ (2001) Phys Rev E 64(1). http://dx.doi.org/10.1103/PhysRevE.64.016131

  35. Barabasi AL, Jeong H, Neda Z, Ravasz E, Schubert A, Vicsek T (2002) Physica A: Stat Mech Appl 311(3–4):590

    Article  MATH  MathSciNet  Google Scholar 

  36. Schifanella C, Caro LD, Cataldi M, Aufaure MA (2012) in KDD. ACM, New York, NY, USA

    Google Scholar 

  37. Di Caro L, Cataldi M, Schifanella C (2012) Scientometrics pp 1–25. doi: 10.1007/s11192-012-0762-1. http://dx.doi.org/10.1007/s11192-012-0762-1

    Google Scholar 

  38. Moon S, You J, Kwak H, Kim D, Jeong H (2010) In: Communication systems and networks (COMSNETS), 2010 second international conference on IEEE Press, Piscataway, pp 1–10

    Google Scholar 

  39. Hou H, Kretschmer H, Liu Z (2008) Scientometrics 75(2):189

    Article  Google Scholar 

  40. Chen CC, Chen YT, Sun YS, Chen MC (2003) in ECML. Springer, Berlin

    Google Scholar 

  41. Wang C, Zhang M, Ru L, Ma S (2008) In: CIKM ’08. ACM, New York, pp 1033–1042. http://doi.acm.org/10.1145/1458082.1458219

  42. He Q, Chang K, Lim EP (2007) Data mining, IEEE international conference on 0, 493. http://doi.ieeecomputersociety.org/10.1109/ICDM.2007.17

  43. Sriram B, Fuhry D, Demir E, Ferhatosmanoglu H, Demirbas M (2010) In Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval, SIGIR ’10. ACM, New York, pp 841–842. doi:10.1145/1835449.1835643. http://doi.acm.org/10.1145/1835449.1835643

  44. Becker H, Naaman M, Gravano L (2011) In: Fifth international AAAI conference on weblogs and social media. The AAAI Press, Menlo Park

    Google Scholar 

  45. Becker H, Naaman M, Gravano L (2010) in WSDM. ACM, New York

    Google Scholar 

  46. Sankaranarayanan J, Samet H, Teitler B, Lieberman M, Sperling J (2009) In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems. ACM, New York, pp 42–51

    Google Scholar 

  47. Petrović S, Osborne M, Lavrenko V (2010) In: Human language technologies: The 2010 annual conference of the north american chapter of the association for computational linguistics, HLT ’10. Association for Computational Linguistics, Stroudsburg, pp 181–189. http://dl.acm.org/citation.cfm?id=1857999.1858020

  48. Bun KK, Ishizuka M, Ishizuka BM (2002) In: Proceedings of 3rd Int’l conference on web informtion systems engineering (WISE 2002). IEEE Computer Society, Washington, 2002), pp 73–82

    Google Scholar 

  49. Lampos V, Cristianini N (2012) ACM Trans Intell Syst Technol 3(4), 72:1. doi: 10.1145/2337542.2337557 http://doi.acm.org/10.1145/2337542.2337557

  50. Takeshi Sakaki MO, Matsuo Y (2010) ACM. USA, New York

    Google Scholar 

  51. AlSumait L, Barbará D, Domeniconi C (2008) In: Proceedings of the 2008 eighth IEEE international conference on data mining, ICDM ’08. IEEE computer society, Washington, pp 3–12. doi:10.1109/ICDM.2008.140. http://dx.doi.org/10.1109/ICDM.2008.140

  52. Sugiyama K, Hatano K, Yoshikawa M (2004) In: Proceedings of the 13th international conference on world wide web, WWW ’04. ACM, New York, pp 675–684.doi:10.1145/988672.988764

  53. Teevan J, Dumais ST, Horvitz E (2005) In: Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’05. ACM, New York, pp 449–456. http://doi.acm.org/10.1145/1076034.1076111. http://doi.acm.org/10.1145/1076034.1076111

  54. Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) In: Proceedings of the 14th international conference on world wide web, WWW ’05. ACM, New York, pp 22–32. http://doi.acm.org/10.1145/1060745.1060754. http://doi.acm.org/10.1145/1060745.1060754

  55. Noll MG, Meinel C (2007) In: Proceedings of the 6th international the semantic web and 2nd Asian conference on Asian semantic web conference, ISWC’07/ASWC’07. Springer, Berlin, pp 367–380. http://dl.acm.org/citation.cfm?id=1785162.1785190

  56. Teevan J, Dumais S, Horvitz E (2005) In: Proceedings of the workshop on new technologies for personalized information access (PIA), pp 84–92

    Google Scholar 

  57. Lin GL, Peng H, Ma QL, Wei J, Qin JW (2010) In: Machine learning and cybernetics (ICMLC), 2010 international conference on, vol. 5, IEEE Computer Society, Washington, pp 2116–2421. doi:10.1109/ICMLC.2010.5580733

  58. Agrawal R, Gollapudi S, Halverson A, Ieong S (2009) In: Proceedings of the second ACM international conference on web search and data mining, WSDM’09. ACM, New York, pp 5–14. http://doi.acm.org/10.1145/1498759.1498766

  59. Radlinski F, Dumais S (2006) In: Proceedings of the 29th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’06. ACM, New York, pp 691–692. http://doi.acm.org/10.1145/1148170.1148320

  60. Wedig S, Madani (2006) In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’06. ACM, New York, pp 742–747. http://doi.acm.org/10.1145/1150402.1150497

  61. Cantador I, Bellogín A, Vallet D (2010) In: Proceedings of the fourth ACM conference on recommender systems, RecSys ’10. ACM, New York, pp 237–240. http://doi.acm.org/10.1145/1864708.1864756

  62. Xu S, Bao S, Fei B, Su Z, Yu Y (2008) In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’08. ACM, New York, pp 155–162. http://doi.acm.org/10.1145/1390334.1390363

  63. Han X, ShenZ, Miao C, Luo X (2010) In: Proceedings of the 6th international conference on Active media technology, AMT ’10. Springer, Berlin, pp 34–46. http://dl.acm.org/citation.cfm?id=1886192.1886201

    Google Scholar 

  64. Sieg A, Mobasher B, Burke R (2007) In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management, CIKM ’07. ACM, New York, pp 525–534. http://doi.acm.org/10.1145/1321440.1321515

  65. Gauch S, Chaffee J, Pretschner A (2003) Web intelligence and agent systems 1, 219. http://dl.acm.org/citation.cfm?id=1016416.1016421

  66. Wang Q, Jin H (2010) In: Proceedings of the 19th ACM international conference on Information and knowledge management CIKM ’10. ACM, New York, pp 999–1008. http://doi.acm.org/10.1145/1871437.1871564

  67. Carmel D, Zwerdling N, Guy I, Ofek-Koifman S, Har’el N, Ronen I, Uziel E, Yogev S, Chernov S (2009) In: Proceedings of the 18th ACM conference on Information and knowledge management, CIKM ’09. ACM, New York, pp 1227–1236. doi:10.1145/1645953.1646109

  68. Salton G, Buckley C (1988) In: Information processing and management. Cornell University, Ithaca

    Google Scholar 

  69. Weng J, Lim EP, Jiang J, He Q (2010) In: Proceedings of the third ACM international conference on web search and data mining, WSDM ’10. ACM, New York, pp 261–270. http://doi.acm.org/10.1145/1718487.1718520

  70. Bakshy E, Hofman JM, Mason WA, Watts DJ (2011) In: Proceedings of the fourth ACM international conference on Web search and data mining, WSDM ’11. ACM, New York, pp 65–74. http://doi.acm.org/10.1145/1935826.1935845

  71. Kwak H, Lee C, Park H, Moon S (2010) In: Proceedings of the 19th international conference on world wide web, WWW ’10. ACM, New York, pp 591–600. doi:10.1145/1772690.1772751

  72. Leskovec J, Faloutsos C (2006) In: KDD ’06: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 631–636. http://doi.acm.org/10.1145/1150402.1150479

  73. Glance NS, Hurst M, Tomokiyo T (2004) In: WWW 2004 Workshop on the weblogging ecosystem. ACM, New York. http://www.blogpulse.com/papers/www2004glance.pdf

  74. Liang X, Chen W, Bu J (2010) In: Computer engineering and technology (ICCET), 2010 2nd international conference on, vol. 6, IEEE Computer Society, Washington, pp 249–253

    Google Scholar 

  75. Cataldi M, Schifanella C, Candan KS, Sapino ML, Di Caro L (2009) In Proceedings of the international conference on management of emergent digital ecosystems, MEDES ’09. ACM, New York, pp 33:218–33:225. http://doi.acm.org/10.1145/1643823.1643864

  76. Ponzetto SP, Strube M (2007) In: Proceedings of the 45th annual meeting of the ACL on interactive poster and demonstration sessions, ACL ’07. Association for Computational Linguistics, Stroudsburg, pp 49–52. http://dl.acm.org/citation.cfm?id=1557769.1557785

  77. Ruthven I, Lalmas M (2003) Knowl Eng Rev 18(2), 95. doi:10.1017/S0269888903000638. http://dx.doi.org/10.1017/S0269888903000638

    Google Scholar 

  78. Aho AV, Hopcroft JE, Ullman J (1983) Data structures and algorithms, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston

    MATH  Google Scholar 

  79. Lu R, Xu Z, Zhang Y, Yang Q (2012) in PAKDD (2). Springer, Berlin

    Google Scholar 

  80. Acar A, Muraki Y (2011) Int J Web Based Commun 7(3):392

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. LIASD, Department of Computer Science, Université Paris 8, Paris, France

    Mario Cataldi

  2. Department of Computer Science, University of Turin, Corso Svizzera 185, Turin, Italy

    Luigi Di Caro & Claudio Schifanella

Authors
  1. Mario Cataldi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Luigi Di Caro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Claudio Schifanella
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mario Cataldi .

Editor information

Editors and Affiliations

  1. IBM Corp., Durham, North Carolina, USA

    Aaron K. Baughman

  2. Nokia Inc., Sunnyvale, California, USA

    Jiang Gao

  3. Google Inc., Mountain View, California, USA

    Jia-Yu Pan

  4. 4i, Inc., Carlsbad, California, USA

    Valery A. Petrushin

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Cataldi, M., Di Caro, L., Schifanella, C. (2015). Twitter as a Personalizable Information Service. In: Baughman, A., Gao, J., Pan, JY., Petrushin, V. (eds) Multimedia Data Mining and Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-14998-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14998-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14997-4

  • Online ISBN: 978-3-319-14998-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation