Abstract
In this paper, we study the task of detecting mothers of babies on Twitter. This could be beneficial for baby mother users to find friends, and for companies, organizations or experts to deliver accurately targeted information. Prior works have proposed supervised classification methods to detect generic latent attributes of Twitter users such as age, gender, and political orientation. However, methods and features for classifying generic attributes do not perform well for more specific attributes, such as whether a user is a mother of a young baby. We design feature sets based on followed accounts and profile pictures, which are largely overlooked in existing work. Comparing to three established feature sets, the experimental evaluation shows that our specifically-designed feature sets considerably improve classification accuracy.
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For example, 17 Funny Moms on Twitter, http://mashable.com/2013/05/10/funny-twitter-moms/. Particularly, we pick four accounts from this article: @jennawrites, @laneymg, @shriekhouse, and @MarinkaNYC. The selection considers the number of followers in these accounts and Twitter API limit for collecting followers.
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Currently Twitter API allows searching 200 followers each minute.
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References
Barberá, P.: Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Political Anal. 23(1), 76–91 (2015)
De Choudhury, M., Counts, S., Horvitz, E.: Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267–3276. ACM (2013)
De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the Seventh International Conference on Web and Social Media, vol. 13, pp. 1–10 (2013)
MacLean, D., Gupta, S., Lembke, A., Manning, C., Heer, J.: Forum77: an analysis of an online health forum dedicated to addiction recovery. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1511–1526. ACM (2015)
Morris, M.R.: Social networking site use by mothers of young children. In: Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1272–1282. ACM (2014)
Pennacchiotti, M., Popescu, A.M.: A machine learning approach to Twitter user classification. In: Proceedings of the Fifth International Conference on Weblogs and Social Media, pp. 281–288 (2011)
Rangel, F., Rosso, P., Potthast, M., Stein, B.: Overview of the 5th author profiling task at pan 2017: gender and language variety identification in Twitter. In: Working Notes Papers of the CLEF (2017)
Rao, D., Yarowsky, D., Shreevats, A., Gupta, M.: Classifying latent user attributes in Twitter. In: Proceedings of the 2nd International Workshop on Search and Mining User-Generated Contents, pp. 37–44 (2010)
Sharma, N.K., Ghosh, S., Benevenuto, F., Ganguly, N., Gummadi, K.: Inferring who-is-who in the Twitter social network. ACM SIGCOMM Comput. Commun. Rev. 42(4), 533–538 (2012)
Zhang, Y., Szabo, C., Sheng, Q.Z.: Improved object and event monitoring on Twitter through lexical analysis and user profiling. In: Proceedings of the 17th International Conference on Web Information System Engineering, pp. 19–34 (2016)
Acknowledgement
This research has been supported by JSPS KAKENHI Grant Numbers #17H01828, #18K19841 and by MIC/SCOPE #171507010.
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Zhang, Y., Jatowt, A., Kawai, Y. (2019). Finding Baby Mothers on Twitter. In: Bakaev, M., Frasincar, F., Ko, IY. (eds) Web Engineering. ICWE 2019. Lecture Notes in Computer Science(), vol 11496. Springer, Cham. https://doi.org/10.1007/978-3-030-19274-7_16
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