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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Chorley, Martin; Rossi, Luca; Tyson, Gareth; Williams, Matthew
Publisher: AAAI
Languages: English
Types: Unknown
Subjects: H1, QA75
There has been a recent surge of research looking at the reporting of food consumption on social media. The topic of alcohol consumption, however, remains poorly investigated. Social media has the potential to shed light on a topic that, traditionally, is difficult to collect fine-grained information on. One social app stands out in this regard: Untappd is an app that allows users to ‘check-in’ their consumption of beers. It operates in a similar fashion to other location-based applications, but is specifically tailored to the collection of information on beer consumption. In this paper, we explore beer consumption through the lens of social media. We crawled Untappd in real time over a period of 112 days, across 40 cities in the United States and Europe. Using this data, we shed light on the drinking habits of over 369k users. We focus on per-user and per-city characterisation, highlighting key behavioural trends.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abbar, S.; Mejova, Y.; and Weber, I. 2014. You tweet what you eat: Studying food consumption through twitter. arXiv preprint arXiv:1412.4361.
    • 2007. Analysis of topological characteristics of huge online social networking services. In Proceedings of the 16th international conference on World Wide Web, 835-844. ACM.
    • Aiello, L. M.; Barrat, A.; Schifanella, R.; Cattuto, C.; Markines, B.; and Menczer, F. 2012. Friendship Prediction and Homophily in Social Media. ACM Trans. Web 6(2):9:1- 9:33.
    • Alstott, J.; Bullmore, E.; and Plenz, D. 2014. powerlaw: a python package for analysis of heavy-tailed distributions.
    • PloS one 9(1):e85777.
    • CGAStrategy. 2013. Craft beer sales jump 79%.
    • Clarke, I.; Kell, I.; Schmidt, R.; and Vignali, C. 2000. Thinking the thoughts they do: Symbolism and meaning in the consumer experience of the british pub. British Food Journal 102(9):692-710.
    • Cramer, H.; Rost, M.; and Holmquist, L. E. 2011. Performing a check-in: emerging practices, norms and'conflicts' in location-sharing using foursquare. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, 57-66. ACM.
    • Culotta, A. 2013. Lightweight methods to estimate influenza rates and alcohol sales volume from twitter messages. Language resources and evaluation 47(1):217-238.
    • Isserman, A. M. 1977. The location quotient approach to estimating regional economic impacts. Journal of the American Institute of Planners 43(1):33-41.
    • Kanny, D.; Liu, Y.; Brewer, R. D.; for Disease Control, C.; (CDC), P.; et al. 2011. Binge drinkingunited states, 2009.
    • MMWR Surveill Summ 60(Suppl):101-4.
    • Kwak, H.; Lee, C.; Park, H.; and Moon, S. 2010. What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, 591- 600. ACM.
    • Lin, J. 1991. Divergence measures based on the shannon entropy. Information Theory, IEEE Transactions on 37(1):145-151.
    • McPherson, M.; Smith-Lovin, L.; and Cook, J. M. 2001.
    • Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology 27:415-444.
    • Mejova, Y.; Haddadi, H.; Noulas, A.; and Weber, I. 2015.
    • # foodporn: Obesity patterns in culinary interactions. In Proceedings of the 5th International Conference on Digital Health 2015, 51-58. ACM.
    • Naylor, T. 2014. The craft beer revolution: how hops got hip. The Guardian.
    • Paul, M. J., and Dredze, M. 2011. You are what you tweet: Analyzing twitter for public health. In ICWSM, 265-272.
    • Peter Anderson, L. M., and Galea, G. 2012. Alcohol in the european union. consumption, harm and policy approaches.
    • Salton, G. 1989. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer.
    • Silva, T. H.; de Melo, P. O.; Almeida, J.; Musolesi, M.; and Loureiro, A. 2014. You are what you eat (and drink): Identifying cultural boundaries by analyzing food & drink habits in foursquare. arXiv preprint arXiv:1404.1009.
    • Tamersoy, A.; De Choudhury, M.; and Chau, D. H. 2015.
    • Characterizing smoking and drinking abstinence from social media. In Proceedings of the 26th ACM Conference on Hypertext & Social Media, 139-148. ACM.
  • Inferred research data

    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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