Analyzing the language of food on social media

Daniel Fried; Mihai Surdeanu; Stephen Kobourov; Melanie Hingle; Dane Bell

doi:10.1109/BigData.2014.7004305

Analyzing the language of food on social media

Daniel Fried, Mihai Surdeanu, Stephen Kobourov, Melanie Hingle, Dane Bell

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

60 Scopus citations

Abstract

We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have greatest predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.

Original language	English (US)
Title of host publication	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
Editors	Wo Chang, Jun Huan, Nick Cercone, Saumyadipta Pyne, Vasant Honavar, Jimmy Lin, Xiaohua Tony Hu, Charu Aggarwal, Bamshad Mobasher, Jian Pei, Raghunath Nambiar
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	778-783
Number of pages	6
ISBN (Electronic)	9781479956654
DOIs	https://doi.org/10.1109/BigData.2014.7004305
State	Published - Jan 7 2015
Event	2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014

Publication series

Name	Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014

Other

Other	2nd IEEE International Conference on Big Data, IEEE Big Data 2014
Country/Territory	United States
City	Washington
Period	10/27/14 → 10/30/14

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Access to Document

10.1109/BigData.2014.7004305

Cite this

Fried, D., Surdeanu, M., Kobourov, S., Hingle, M., & Bell, D. (2015). Analyzing the language of food on social media. In W. Chang, J. Huan, N. Cercone, S. Pyne, V. Honavar, J. Lin, X. T. Hu, C. Aggarwal, B. Mobasher, J. Pei, & R. Nambiar (Eds.), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 (pp. 778-783). [7004305] (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2014.7004305

Analyzing the language of food on social media. / Fried, Daniel; Surdeanu, Mihai ; Kobourov, Stephen et al.

Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. ed. / Wo Chang; Jun Huan; Nick Cercone; Saumyadipta Pyne; Vasant Honavar; Jimmy Lin; Xiaohua Tony Hu; Charu Aggarwal; Bamshad Mobasher; Jian Pei; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2015. p. 778-783 7004305 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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

Fried, D, Surdeanu, M , Kobourov, S , Hingle, M & Bell, D 2015, Analyzing the language of food on social media. in W Chang, J Huan, N Cercone, S Pyne, V Honavar, J Lin, XT Hu, C Aggarwal, B Mobasher, J Pei & R Nambiar (eds), Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014., 7004305, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014, Institute of Electrical and Electronics Engineers Inc., pp. 778-783, 2nd IEEE International Conference on Big Data, IEEE Big Data 2014, Washington, United States, 10/27/14. https://doi.org/10.1109/BigData.2014.7004305

Fried D, Surdeanu M , Kobourov S , Hingle M, Bell D. Analyzing the language of food on social media. In Chang W, Huan J, Cercone N, Pyne S, Honavar V, Lin J, Hu XT, Aggarwal C, Mobasher B, Pei J, Nambiar R, editors, Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc. 2015. p. 778-783. 7004305. (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014). doi: 10.1109/BigData.2014.7004305

Fried, Daniel ; Surdeanu, Mihai ; Kobourov, Stephen et al. / Analyzing the language of food on social media. Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. editor / Wo Chang ; Jun Huan ; Nick Cercone ; Saumyadipta Pyne ; Vasant Honavar ; Jimmy Lin ; Xiaohua Tony Hu ; Charu Aggarwal ; Bamshad Mobasher ; Jian Pei ; Raghunath Nambiar. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 778-783 (Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014).

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abstract = "We investigate the predictive power behind the language of food on social media. We collect a corpus of over three million food-related posts from Twitter and demonstrate that many latent population characteristics can be directly predicted from this data: overweight rate, diabetes rate, political leaning, and home geographical location of authors. For all tasks, our language-based models significantly outperform the majority-class baselines. Performance is further improved with more complex natural language processing, such as topic modeling. We analyze which textual features have greatest predictive power for these datasets, providing insight into the connections between the language of food, geographic locale, and community characteristics. Lastly, we design and implement an online system for real-time query and visualization of the dataset. Visualization tools, such as geo-referenced heatmaps and temporal histograms, allow us to discover more complex, global patterns mirrored in the language of food.",

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Analyzing the language of food on social media

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