Publication

Discovery of Topical Authorities in Instagram

WWW 2016


Abstract

Instagram has more than 400 million monthly active accounts who share more than 80 million pictures and videos daily. This large volume of user-generated content is the application’s notable strength, but also makes the problem of finding the authoritative users for a given topic challenging. Discovering topical authorities can be useful for providing relevant recommendations to the users. In addition, it can aid in building a catalog of topics and top topical authorities in order to engage new users, and hence provide a solution to the cold-start problem.

In this paper, we present a novel approach that we call the Authority Learning Framework (ALF) to find topical authorities in Instagram. ALF is based on the self-described interests of the follower base of popular accounts. We infer regular users’ interests from their self-reported biographies that are publicly available and use Wikipedia pages to ground these interests as fine-grained, disambiguated concepts. We propose a generalized label propagation algorithm to propagate the interests over the follower graph to the popular accounts. We show that even if biography-based interests are sparse at an individual user level they provide strong signals to infer the topical authorities and let us obtain a high precision authority list per topic. Our experiments demonstrate that ALF performs significantly better at user recommendation task compared to fine-tuned and competitive methods, via controlled experiments, in-the-wild tests, and over an expert-curated list of topical authorities.

Related Publications

All Publications

Open Source Evolutionary Structured Optimization

Jeremy Rapin, Pauline Bennet, Emmanuel Centeno, Daniel Haziza, Antoine Moreau, Olivier Teytaud

Evolutionary Computation Software Systems Workshop at ​GECCO - July 9, 2020

Adherence to suicide reporting guidelines by news shared on a social networking platform

Steven A. Sumner, Moira Burke, Farshad Kooti

PNAS - July 6, 2020

A Counterfactual Framework for Seller-Side A/B Testing on Marketplaces

Viet Ha-Thuc, Avishek Dutta, Ren Mao, Matthew Wood, Yunli Liu

ACM SIGIR - July 25, 2020

Finding the Best k in Core Decomposition: A Time and Space Optimal Solution

Deming Chu, Fan Zhang, Xuemin Lin, Wenjie Zhang, Ying Zhang, Yinglong Xia, Chenyi Zhang

ICDE - April 20, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy