Targeted advertising is a key characteristic of online as well as traditional-media marketing. However it is very limited in outdoor advertising, that is, performing campaigns by means of billboards in public places. The reason is the lack of information about the interests of the particular passersby, except at very imprecise and aggregate demographic or traffic estimates. In this work we propose a methodology for performing targeted outdoor advertising by leveraging the use of social media. In particular, we use the Twitter social network to gather information about users’ degree of interest in given advertising categories and about the common routes that they follow, characterizing in this way each zone in a given city. Then we use our characterization for recommending physical locations for advertising. Given an advertisement category, we estimate the most promising areas to be selected for the placement of an ad that can maximize its targeted effectiveness. We show that our approach is able to select advertising locations better with respect to a baseline reflecting a current ad-placement policy. To the best of our knowledge this is the first work on offline advertising in urban areas making use of (publicly available) data from social networks.
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All the datasets used for this work are available at https://dandelion.eu/datamine/open-big-data/, released by Telecom Italia, the main Italian telecommunication provider, for the international competition Telecom Big Data Challenge 2014.
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We would like to thank the anonymous reviewers for their valuable comments.
A preliminary version of this work appeared as a poster paper in the Proceedings of the 10th International AAAI Conference on Web and Social Media, (ICWSM 2016).
This research was partially supported by the Google Focused Research Award “Algorithms for Large-Scale Data Analysis” and by the EU FET project MULTIPLEX 317532.
Responsible editor: Katharina Morik, Fosca Giannotti, Marta Gonzalez, Ioannis Katakis.
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Anagnostopoulos, A., Petroni, F. & Sorella, M. Targeted interest-driven advertising in cities using Twitter. Data Min Knowl Disc 32, 737–763 (2018). https://doi.org/10.1007/s10618-017-0529-7
- Targeted outdoor advertising