Maximizing the effectiveness of advertising campaigns on Twitter

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

4 Citations (Scopus)

Abstract

Influence Maximization (IM) has received lots of attention from academia as well as industry. In this work, we study a specific influence maximization problem, selecting a set of seed users to maximize the effectiveness of advertising campaigns on Twitter. This problem requires that the information diffusion model must have the capability to support: (a) an active user can make multiple attempts to activate his neighbors, and (b) a user can accept an advertising message many times. Considering the characteristics of advertisement propagation on Twitter, we propose a diffusion model that inherits the classic independent cascade model and removes the constraints such as single attempt in activating neighbors and taking single influence from other users. The influence probability is calculated based on users' action history including tweet, favorite, reply and retweet. Influence decay is also introduced to reflect the temporal features associated with influence. A new metric 'advertising effectiveness' is defined as the maximization objective. Several existing seed selection algorithms are analyzed based on the proposed diffusion model against a real dataset from Twitter including 3,292 users in Darwin city in Australia. Experimental results and analysis are provided to show the soundness of the proposed model.

Original languageEnglish
Title of host publicationBigData Congress 2017
Subtitle of host publicationProceedings of 2017 IEEE 6th International Congress on Big Data
EditorsG Karypis, J Zhang
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages73-80
Number of pages8
ISBN (Electronic)9781538619964
ISBN (Print)9781538619971
DOIs
Publication statusPublished - 7 Sep 2017
Event6th IEEE International Congress on Big Data, BigData Congress 2017 - Honolulu, United States
Duration: 25 Jun 201730 Jun 2017

Conference

Conference6th IEEE International Congress on Big Data, BigData Congress 2017
CountryUnited States
CityHonolulu
Period25/06/1730/06/17

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  • Cite this

    Mei, Y., Zhao, W., & Yang, J. (2017). Maximizing the effectiveness of advertising campaigns on Twitter. In G. Karypis, & J. Zhang (Eds.), BigData Congress 2017: Proceedings of 2017 IEEE 6th International Congress on Big Data (pp. 73-80). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/BigDataCongress.2017.19