SNS Retrieval Based on User Profile Estimation Using Transfer Learning from Web Search

by Daisuke Kataoka, Keishi Tajima


In this paper, we propose a method of retrieving posts on social networking services (SNSs) by specifying a pair of queries: a topic query and an entity query. A topic query specifies the topic of the posts to retrieve (e.g., “iPhone”) and an entity query specifies the type of users who posted them (e.g., “students”). In the existing search systems for SNS posts, we can specify topics of posts by keywords, but we cannot specify types of users. Even if we include keywords specifying types of users in a query, such keywords are not usually included in tweets or user profile data. In our method, we estimate types of users by learning vocabulary whose appearance is correlated with specific types of users. We learn it from the datasets obtained through Web search. We retrieve Web documents through the search with a keyword specifying the type of users (e.g., “student”), and we also retrieve Web documents by using a keyword specifying its opposite (e.g., “adult”). We regard the documents retrieved by these queries as positive and negative examples of documents describing the target type, and we train a model for recognizing users of the given type. We recognize users of the target type by inputting their posts and their profile data into the model. We use Web documents instead of SNS posts for training the model because the Web has more documents describing types of people.

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BibTex entry


microblog; profile estimation; transfer learning
Published in Proc. of IEEE/WIC/ACM WI, pp.278-285, Santiago, Chile, 2018

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