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        <title>Predicting Restaurant Consumption Level through Social Media Footprints</title>
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        <namePart type="given">Yang</namePart>
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    <name type="personal">
        <namePart type="given">Hangyu</namePart>
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    <name type="personal">
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            <namePart type="given">Yuji</namePart>
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    <abstract>Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a dataset of 8,844 users who have been linked across Dianping (similar to Yelp) and Sina Weibo. More specifically, we construct consumption level ground truth based on user self report spending. We build predictive models using both raw features and, especially, latent features, such as topic distributions and celebrities clusters. The employed methods demonstrate that online social media content has strong predictive power for offline spending. Finally, combined with qualitative feature analysis, we present the differences in words usage, topic interests and following behavior between different consumption level groups.</abstract>
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%0 Conference Proceedings
%T Predicting Restaurant Consumption Level through Social Media Footprints
%A Xiao, Yang
%A Wang, Yuan
%A Mao, Hangyu
%A Xiao, Zhen
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F xiao-etal-2016-predicting
%X Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a dataset of 8,844 users who have been linked across Dianping (similar to Yelp) and Sina Weibo. More specifically, we construct consumption level ground truth based on user self report spending. We build predictive models using both raw features and, especially, latent features, such as topic distributions and celebrities clusters. The employed methods demonstrate that online social media content has strong predictive power for offline spending. Finally, combined with qualitative feature analysis, we present the differences in words usage, topic interests and following behavior between different consumption level groups.
%U https://aclanthology.org/C16-1314
%P 3328-3338

Markdown (Informal)

[Predicting Restaurant Consumption Level through Social Media Footprints](https://aclanthology.org/C16-1314) (Xiao et al., COLING 2016)

• Predicting Restaurant Consumption Level through Social Media Footprints (Xiao et al., COLING 2016)

ACL

• Yang Xiao, Yuan Wang, Hangyu Mao, and Zhen Xiao. 2016. Predicting Restaurant Consumption Level through Social Media Footprints. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 3328–3338, Osaka, Japan. The COLING 2016 Organizing Committee.