Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
A Competitive Combat Strategy and Tactics in RTS Games AI and StarCraft Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

Hot Topic Trend Prediction of Topic Based on Markov Chain and Dynamic Backtracking

verfasst von : Feng Xu, Jue Liu, Ying He, Yating Hou

Erschienen in: Advances in Multimedia Information Processing – PCM 2017

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Predicting topic trend in social networks can provide good reference value for public opinion guidance and commercial marketing. In this paper, we discuss the hot topic evaluation methods, and then present a method for evaluating the topic popularity of microblog based on multiple factors, which comprehending four factors (the number of micro blog, number of forwarding, number of comments, and number of praise) and using relative ranking method to define the value of micro blog popularity. In order to improve the prediction accuracy of hot topics, we present a prediction algorithm based on Markov chain and dynamic backtracking, which is based our evaluation method. In the algorithm, we use the simulated annealing method to find the optimal parameters and improve the accuracy of the prediction algorithm based on the Markov chain by historical backtracking. Analysis and simulation results demonstrate that the proposed algorithm is more accurate than some conventional methods.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Vectorized Data Combination and Binary Search Oriented Reweight for CPU-GPU Based Real-Time 3D Ball Tracking
Nächstes Kapitel Fast Circular Object Localization and Pose Estimation for Robotic Bin Picking
Literatur
1.
Hoff, P.D.: Multiplicative latent factor models for description and prediction of social networks. Comput. Math. Organ. Theor. 15(4), 261–272 (2009)CrossRef
2.
Chen, K.H., Liang, T.J.: Friendship prediction on social network users. In: 2013 International Conference on Social Computing (Social Com), pp. 379–384. IEEE (2013)
3.
4.
Oentaryo, R.J., Lim, E.P., Lo, D., et al.: Collective churn prediction in social network. In: Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012, pp. 210–214. IEEE Computer Society (2012)
5.
Liu, R., Guo, W.: HMM-based state prediction for Internet hot topic. In: 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE), vol. 1, pp. 157–161. IEEE (2011)
6.
Yan, C., Shi, S., Huang, H., et al.: A method for network topic attention forecast based on feature words. In: 2013 International Conference on Asian Language Processing (IALP), pp. 211–214. IEEE (2013)
7.
8.
Zhang, B., Guan, X., Khan, M.J., et al.: A time-varying propagation model of hot topic on BBS sites and blog networks. Inf. Sci. 187, 15–32 (2012)CrossRef
9.
10.
Das, D., Bandyopadhyay, S.: Emotions on Bengali blog texts: role of holder and topic. In: 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 587–592. IEEE (2011)
11.
Thi, D.B., Hoang, T.A.N.: Features extraction for link prediction in social networks. In: 2013 13th International Conference on Computational Science and Its Applications (ICCSA), pp. 192–195. IEEE (2013)
12.
13.
Ruan, Y., Purohit, H., Fuhry, D., et al.: Prediction of topic volume on twitter. In: ACM Web Science, pp. 397–402. ACM (2012)
14.
Mathioudakis, M., Koudas, N.: TwitterMonitor: trend detection over the twitter stream. In: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, pp. 1155–1158. ACM (2010)
15.
Pengyi, F., Hui, W., Zhihong, J., et al.: Measurement of microblogging network. J. Comput. Res. Develop. 49(4), 691–699 (2012)
16.
Bao, P., Shen, H.W., Huang, J., et al.: Popularity prediction in microblogging network: a case study on Sina Weibo. In: Proceedings of the 22nd International Conference on World Wide Web Companion, pp. 177–178. International World Wide Web Conferences Steering Committee (2013)
17.
Han, Y., Fang, B., Jia, Y.: Predicting the topic influence trends in social media with multiple models. Neurocomputing 144, 463–470 (2014)CrossRef
18.
19.
Liu, Y., Wang, J., Jiang, Y.: PT-LDA: a latent variable model to predict personality traits of social network users. Neurocomputing 210, 155–163 (2016)CrossRef
20.
Fu, C., Shaobin, Z., Guangjun, S.: A study on trend prediction in Sina Weibo community. In: 2014 IEEE International Congress on Big Data (Big Data Congress), pp. 364–365. IEEE (2014)
21.
Gupta, M., Gao, J., Zhai, C.X., et al.: Predicting future popularity trend of events in microblogging platforms. Proc. Am. Soc. Inf. Sci. Technol. 49(1), 1–10 (2012)CrossRef
22.
Wang, P., Xu, B.W., Wu, Y.R., et al.: Link prediction in social networks: the state-of-the-art. Sci. China Inf. Sci. 58(1), 1–38 (2015)
23.
Tong, H., Liu, Y., Peng, H., et al.: Internet users’ psychosocial attention prediction: web hot topic prediction based on adaptive AR model. In: 2008 International Conference on Computer Science and Information Technology, ICCSIT 2008, pp. 458–462. IEEE (2008)
24.
Wang, X., Qi, L., Chen, C., et al.: Grey system theory based prediction for topic trend on Internet. Eng. Appl. Artif. Intell. 29, 191–200 (2014)CrossRef
25.
Wang, T., Krim, H., Viniotis, Y.: A generalized Markov graph model: application to social network analysis. IEEE J. Selected Topics Sig. Process. 7(2), 318–332 (2013)CrossRef
26.
Chen, Y., Ying, J.: Modeling community influence in social networks with Markov chains. In: Cloud Computing and Big Data (CloudCom-Asia)
27.
Thissen, U., Van Brakel, R., De Weijer, A.P., et al.: Using support vector machines for time series prediction. Chemometr. Intell. Lab. Syst. 69(1), 35–49 (2003)CrossRef
28.
Li, J., Peng, W., Li, T., et al.: Social network user influence sense-making and dynamics prediction. Exp. Syst. Appl. 41(11), 5115–5124 (2014)CrossRef
29.
Zhou, Y., Guan, X., Zhang, Z., et al.: Predicting the tendency of topic discussion on the online social networks using a dynamic probability model. In: Proceedings of the Hypertext 2008 Workshop on Collaboration and Collective Intelligence, pp. 7–11. ACM (2008)
30.
Ding, H., Wu, J.: Predicting retweet scale using log-normal distribution. In: 2015 IEEE International Conference on Multimedia Big Data, pp. 56–63. IEEE (2015). 2013 IEEE International Conference, pp. 515–520. IEEE
Metadaten
Titel
Hot Topic Trend Prediction of Topic Based on Markov Chain and Dynamic Backtracking
verfasst von
Feng Xu
Jue Liu
Ying He
Yating Hou
Copyright-Jahr
2018
Buch
Advances in Multimedia Information Processing – PCM 2017

Print ISBN: 978-3-319-77382-7

Electronic ISBN: 978-3-319-77383-4

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-77383-4_51

Neuer Inhalt

    Bildnachweise