Abhijnan Chakraborty

TL;DR: Wang et al. as mentioned in this paper proposed clickbait detection and personalized blocking approaches to detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines.

TL;DR: In this article, the authors investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other, and propose FairRec algorithm to guarantee at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One Good (EF1) fairness for every customer.

TL;DR: This paper analyzes job assignments of a major taxi company and observes that there is significant inequality in the driver income distribution, and proposes a novel framework to think about fairness in the matching mechanisms of ride hailing platforms.

TL;DR: A novel scalable methodology to accurately infer the biases of thousands of news sources on social media sites like Facebook and Twitter and shows how biases in a news source’s audience demographics can be used to infer more fine-grained biases of the source, such as social vs. economic vs. nationalistic conservatism.

TL;DR: This work attempts to automatically detect clickbait detection and then builds a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines, and offers each reader an option to block clickbaits she doesn't want to see.