Occupation inference through detection and classification of biographical activities

Filatova, Elena; Prager, John

doi:10.1016/j.datak.2012.04.001

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Another line is to understand the political orientation of users from the congressional transcripts as well as microblogs [9], [10], [27], [28], [31], [40]. Other previously explored user attributes include geographical location [8], [14], regional origin [28], [28], [40], occupation [17], ethnicity [27], sentiment [26], [30], [34], etc.…”

Section: Related Workmentioning

confidence: 99%

“…In this work, we consider six types of user attributes including three biographic attributes-age, gender, relationship, and three personalization attribute-occupation, interest, emotional orientation. The attribute values are defined manually based on a comprehensive study of Google+ data and a survey of previous work on user attribute inference [17], [28], [40]. Table I presents the meaning of user attribute values.…”

Section: Problem Definitionmentioning

confidence: 99%

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Relational User Attribute Inference in Social Media

Fang

Sang

et al. 2015

IEEE Trans. Multimedia

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Nowadays, more and more people are engaged in social media to generate multimedia information, i.e., creating text and photo profiles and posting multimedia messages. Such multimodal social networking activities reveal multiple user attributes such as age, gender, and personal interest. Inferring user attributes is important for user profiling, retrieval, and personalization. Existing work is devoted to inferring user attributes independently and ignores the dependency relations between attributes. In this work, we investigate the problem of relational user attribute inference by exploring the relations between user attributes and extracting both lexical and visual features from online user-generated content. We systematically study six types of user attributes: gender, age, relationship, occupation, interest, and emotional orientation. In view of methodology, we propose a relational latent SVM (LSVM) model to combine a rich set of user features, attribute inference, and attribute relations in a unified framework. In the model, one attribute is selected as the target attribute and others are selected as the auxiliary attributes to assist the target attribute inference. The model infers user attributes and attribute relations simultaneously. Extensive experiments conducted on a collected dataset from Google+ with full attribute annotations demonstrate the effectiveness of the proposed approach in user attribute inference and attribute-based user retrieval.Index Terms-Attribute relation, latent SVM (LSVM), user attribute inference.

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