Abstract
By now, even if we are still geographically situated, we’re able to reach, connect and know about each other through social networks like never before. Among all popular Social Networks, Twitter is considered as the most open social media platform used by celebrities, politicians, journalists and recently attracted a lot of attention among researcher mainly because of its unique potential to reach this large number of diverse people and for its interesting fast-moving timeline where lots of latent information can be mined such as finding influencers or understanding influence diffusion process. This studies have a significant value to various applications, e.g., understanding customer behavior, predicting flu trends, event detection and more. The purpose of this paper is to investigate the most recent research methods related to this topic and to compare them to each other. Finally, we hope that this summarized literature gives directions to other researchers for future studies on this topic.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
The digital universe of opportunities: rich data and the increasing value of the Internet of Things. https://www.emc.com/leadership/digital-universe/2014iview/executive-summary.htm. Accessed 2 Dec 2018
Twitter statistics. https://www.statisticbrain.com/twitter-statistics/. Accessed 2 Dec 2018
Makice, K.: Twitter API: up and running learn how to build applications with the Twitter API, 1st edn. O’Reilly Media, Sebastopol (2009)
Tweet data dictionary. https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/tweet-object. Accessed 2 Dec 2018
Rate limits. https://developer.twitter.com/en/docs/basics/rate-limits. Accessed 2 Dec 2018
Trung, D.N., Jung, J.: Sentiment analysis based on fuzzy propagation in online social networks: a case study on TweetScope. Comput. Sci. Inf. Syst. 11(1), 215–228 (2014)
Alp, Z.Z., Öğüdücü, S.G.: Topical influencers on twitter based on user behavior and network topology. Knowl. Based Syst. 141, 211–221 (2018)
Cha, M., Haddadi, H., Benevenuto, F., Gummadi, P.K.: Measuring user influence in twitter: the million follower fallacy. In: ICWSM 2010, pp. 10–17 (2010)
Cappelletti, R., Sastry, N.: IARank: ranking users on twitter in near real-time, based on their information amplification potential. In: International Conference on Social Informatics 2012, Lausanne, pp. 70–77 (2012)
Tinati, R., Carr, L., Hall, W., Bentwood, J.: Identifying communicator roles in Twitter. In: Proceedings of the 21st International Conference on World Wide Web, pp. 1161–1168. ACM, New York (2012)
Jansen, B.J., Zhang, M., Sobel, K., Chowdury, A.: Twitter power: tweets as electronic word of mouth. JASIST 60, 2169–2188 (2009)
Bakshy, E., Hofman, J.M., Mason, W., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining (WSDM 2011), pp. 65–74 (2011)
Galuba, W., Aberer, K., Chakraborty, D., Despotovic, Z., Kellerer, W.: Outtweeting the twitterers - predicting information cascades in microblogs. In: Proceedings of the 3rd Conference on Online Social Networks (WOSN 2010) (2010)
Rotabi, R., Kamath, K., Kleinberg, J., Sharma, A.: Cascades: a view from audience. In: Proceedings of the 26th International Conference on World Wide Web, pp. 587–596 (2017)
Riquelme, F., González-Cantergiani, P.: Measuring user influence on Twitter: a survey. Inf. Process. Manage. 52(5), 949–975 (2016)
Li, M., Wang, X., Gao, K., Zhang, S.: A survey on information diffusion in online social networks: models and methods. Information 8, 118 (2017)
Wu, X., Zhang, H., Zhao, X., Li, B., Yang, C.: Mining algorithm of microblogging opinion leaders based on user-behavior network. Appl. Res. Comput. 32, 2678–2683 (2015)
Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. In: Proceedings of the Seventh International Conference on World Wide Web 7 (WWW7), Amsterdam, The Netherlands, pp. 107–117 (1998)
Achrekar, H., Gandhe, A., Lazarus, R., Yu, S.-H., Liu, B.: Predicting flu trends using twitter data. In: IEEE Conference on Computer Communications Workshops 2011 (INFOCOM WKSHPS), Shanghai, pp. 702–707 (2011)
Piccialli, F., Jung, J.E.: Understanding customer experience diffusion on social networking services by big data analytics. Mobile Netw. Appl. 22, 605–612 (2017)
Chung, J.E.: Retweeting in health promotion: analysis of tweets about breast cancer awareness month. Comput. Hum. Behav. 74, 112–119 (2017)
Kreiss, D.: Seizing the moment: the presidential campaigns’ use of Twitter during the 2012 electoral cycle. New Media Soc. 18, 1473–1490 (2014)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)
Twitter by the numbers: stats, demographics & fun facts. https://www.omnicoreagency.com/twitter-statistics/. Accessed 2 Dec 2018
Sakaki, T., Okazaki, M., Matsuo, Y.: Earthquake shakes Twitter users: realtime 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)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
El Bacha, R., Zin, T.T. (2019). A Survey on Influence and Information Diffusion in Twitter Using Big Data Analytics. In: Zin, T., Lin, JW. (eds) Big Data Analysis and Deep Learning Applications. ICBDL 2018. Advances in Intelligent Systems and Computing, vol 744. Springer, Singapore. https://doi.org/10.1007/978-981-13-0869-7_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-0869-7_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0868-0
Online ISBN: 978-981-13-0869-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)