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‘Classifying Latent User Attributes in Twitter’ by Delip Rao, David Yarowsky, Abhishek Shreevats, & Manaswi Gupta (2010)

Published onApr 03, 2020
‘Classifying Latent User Attributes in Twitter’ by Delip Rao, David Yarowsky, Abhishek Shreevats, & Manaswi Gupta (2010)

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)



Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the userproperty classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.

Critical Annotation

Rao et al., experimenting with various classification models, infer a number of latent author attributes from the posts, social network structure and communication behaviour of Twitter users. In regards to a user’s gender, they found that neither a user’s network structure (such as their follower-following ratio, follower frequency or following frequency) or their communication behaviour (response frequency, retweet frequency, tweet frequency) were helpful in its inference. They experimented with a number of predictive models looking specifically at text posts, seeing if they could infer gender exclusively from the content and style of a tweet’s author. They report results of 68.7% accuracy in regards to inferring gender using an ngram model alone that utilizes several million ngram features. Accuracy increases to 72.3% when this model is combined with a sociolinguistic-based features model that examines the lexical choice and other linguistic features such as the use of emoticons, use of internet slang, etc.). It should be noted that their dataset was assembled by finding twitter users with social network connections to unambiguously gendered organizations such as fraternities and sororities.

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