Data Mining and Knowledge Discovery

, Volume 32, Issue 3, pp 737–763 | Cite as

Targeted interest-driven advertising in cities using Twitter

  • Aris Anagnostopoulos
  • Fabio Petroni
  • Mara SorellaEmail author
Part of the following topical collections:
  1. Special Issue on Data Mining for Smart Cities


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.


Targeted outdoor advertising Twitter Geotag 



We would like to thank the anonymous reviewers for their valuable comments.


  1. Anagnostopoulos A, Petroni F, Sorella M (2016) Targeted interest-driven advertising in cities using Twitter. In: Proceedings of the 10th international AAAI conference on web and social media (ICWSM), pp 527–530Google Scholar
  2. Belch GE, Belch MA (2011) Advertising and promotion: an integrated marketing communications perspective. McGraw-Hill, New YorkGoogle Scholar
  3. Bhattacharya P, Zafar MB, Ganguly N, Ghosh S, Gummadi KP (2014) Inferring user interests in the Twitter social network. In: Proceedings of the 8th ACM conference on recommender systems (RecSys), ACM, pp 357–360Google Scholar
  4. Chen J, Nairn R, Nelson L, Bernstein M, Chi E (2010) Short and tweet: experiments on recommending content from information streams. In: Proceedings of the 28th ACM conference on human factors in computing systems (SIGCHI), ACM, pp 1185–1194Google Scholar
  5. Cheng Z, Caverlee J, Barthwal H, Bachani V (2014) Who is the barbecue king of texas?: a geo-spatial approach to finding local experts on Twitter. In: Proceedings of the 37th international ACM conference on research and development in information retrieval (SIGIR), ACM, pp 335–344Google Scholar
  6. Cranshaw J, Schwartz R, Hong JI, Sadeh NM (2012) The livehoods project: utilizing social media to understand the dynamics of a city. In: Proceedings of the 6th international AAAI conference on weblogs and social media (ICWSM), pp 58–65Google Scholar
  7. Donthu N, Cherian J, Bhargava M (1993) Factors influencing recall of outdoor advertising. J Advert Res 33(3):64–73Google Scholar
  8. Farahat A, Bailey MC (2012) How effective is targeted advertising? In: Proceedings of the 21st international conference on world wide web (WWW), ACM, pp 111–120Google Scholar
  9. Ghosh S, Sharma N, Benevenuto F, Ganguly N, Gummadi K (2012) Cognos: crowdsourcing search for topic experts in microblogs. In: Proceedings of the 35th international ACM conference on research and development in information retrieval (SIGIR), ACM, pp 575–590Google Scholar
  10. Gulmez M, Karaca S, Kitapci O (2010) The effects of outdoor advertisements on consumers: a case study. Stud Bus Econ 5(2):70–88Google Scholar
  11. Gupta AK, Nadarajah S (2004) Handbook of beta distribution and its applications. CRC Press, Boca RatonzbMATHGoogle Scholar
  12. Ha L, McCann K (2008) An integrated model of advertising clutter in offline and online media. Int J Advert 27(4):569–592CrossRefGoogle Scholar
  13. Huber PJ (1967) The behavior of maximum likelihood estimates under nonstandard conditions. In: Proceedings of the 5th Berkeley symposium on mathematical statistics and probability, vol 1, pp 221–233Google Scholar
  14. Iyer G, Soberman D, Villas-Boas JM (2005) The targeting of advertising. Market Sci 24(3):461–476CrossRefGoogle Scholar
  15. Kisilevich S, Krstajic M, Keim D, Andrienko N, Andrienko G (2010) Event-based analysis of people’s activities and behavior using flickr and panoramio geotagged photo collections. In: Proceedings of the 14th IEEE international conference on information visualization (IV), IEEE, pp 289–296Google Scholar
  16. Kumar A (2012) Dimensionality of consumer beliefs toward billboard advertising. J Market Commun 8(1):22–26Google Scholar
  17. Le Falher G, Gionis A, Mathioudakis M (2015) Where is the soho of rome? Measures and algorithms for finding similar neighborhoods in cities. In: Proceedings of the 9th international AAAI conference on web and social media (ICWSM), pp 228–237Google Scholar
  18. Lee R, Sumiya K (2010) Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on location based social networks, ACM, pp 1–10Google Scholar
  19. Liebig T, Stange H, Hecker D, May M, Kórner C, Hofmann U (2011) A general pedestrian movement model for the evaluation of mixed indoor–outdoor poster campaigns. In: Proceedings of the 3rd international conference on applied operation research (ICAOR), pp 289–300Google Scholar
  20. Osborne AC, Coleman R (2008) Outdoor advertising recall: a comparison of newer technology and traditional billboards. J Curr Issues Res Advert 30(1):13–30CrossRefGoogle Scholar
  21. Petroni F, Querzoni L, Beraldi R, Paolucci M (2016) Lcbm: a fast and lightweight collaborative filtering algorithm for binary ratings. J Syst Softw 117:583–594CrossRefGoogle Scholar
  22. Piórkowski M (2009) Sampling urban mobility through on-line repositories of GPS tracks. In: Proceedings of the 1st ACM international workshop on hot topics of planet-scale mobility measurements, ACM, pp 1–6Google Scholar
  23. Quercia D, Di Lorenzo G, Calabrese F, Ratti C (2011) Mobile phones and outdoor advertising: measurable advertising. IEEE Pervasive Comput 2(10):28–36CrossRefGoogle Scholar
  24. Rozenshtein P, Anagnostopoulos A, Gionis A, Tatti N (2014) Event detection in activity networks. In: Proceedings of the 20th international ACM SIGKDD conference on knowledge discovery and data mining, ACM, pp 1176–1185Google Scholar
  25. Saravanou A, Valkanas G, Gunopulos D, Andrienko G (2015) Twitter floods when it rains: a case study of the uk floods in early 2014. In: Proceedings of the 24th ACM international conference on world wide web (WWW), ACM, pp 1233–1238Google Scholar
  26. Shannon R, Stabeler M, Quigley A, Nixon P (2009) Profiling and targeting opportunities in pervasive advertising. In: Proceedings of the 1st workshop on pervasive advertising (PerAd)Google Scholar
  27. Shimp T, Andrews JC (2013) Advertising, promotion and other aspects of integrated marketing communications. South-Western Cengage Learning, Mason, OHGoogle Scholar
  28. Varga A (2014) Exploiting domain knowledge for cross-domain text classification in heterogeneous data sources. Ph.D. thesis, University of SheffieldGoogle Scholar
  29. Vieweg S, Hughes AL, Starbird K, Palen L (2010) Microblogging during two natural hazards events: what twitter may contribute to situational awareness. In: Proceedings of the 28th ACM conference on human factors in computing systems (SIGCHI), ACM, pp 1079–1088Google Scholar
  30. Villatoro D, Serna J, Rodríguez V, Torrent-Moreno M (2013) The tweetbeat of the city: microblogging used for discovering behavioural patterns during the mwc2012. In: Citizen in sensor networks, Springer, pp 43–56Google Scholar
  31. Wagner C, Liao V, Pirolli P, Nelson L, Strohmaier M (2012) It’s not in their tweets: modeling topical expertise of Twitter users. In: Proceedings of the 4th IEEE international conference on privacy, security, risk and trust (PASSAT), IEEE, pp 91–100Google Scholar
  32. Wang X, Zhang Y, Zhang W, Lin X (2014) Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream. In: Proceedings of the 37th international ACM conference on research and development in information retrieval (SIGIR), ACM, pp 1215–1218Google Scholar
  33. Weng J, Lim EP, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential Twitterers. In: Proceedings of the 3rd ACM international conference on web search and data mining (WSDM), ACM, pp 261–270Google Scholar
  34. Woodside AG (1990) Outdoor advertising as experiments. J Acad Mark Sci 18(3):229–237CrossRefGoogle Scholar
  35. Yamaguchi Y, Amagasa T, Kitagawa H (2011) Tag-based user topic discovery using Twitter lists. In: Proceedings of the 2011 international conference on advances in social networks analysis and mining (ASONAM), IEEE, pp 13–20Google Scholar
  36. Zheng Y, Capra L, Wolfson O, Yang H (2014) Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Technol (TIST) 5(3):38Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.Sapienza University of RomeRomeItaly

Personalised recommendations