Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the longrun, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed.In this work, we attempt to automatically 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. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.2. newsroom.fb.com/news/2014/08/news-feed-fyi-click-baiting 3. thenextweb.com/facebook/2016/04/21/facebook-might-finally-killclickbait-new-algorithm-tweaks/

We investigate the problem of fair recommendation in the context of two-sided online platforms, comprising customers on one side and producers on the other. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reveals that such customer-centric design may lead to unfair distribution of exposure among the producers, which may adversely impact their well-being. On the other hand, a producer-centric design might become unfair to the customers. Thus, we consider fairness issues that span both customers and producers. Our approach involves a novel mapping of the fair recommendation problem to a constrained version of the problem of fairly allocating indivisible goods. Our proposed FairRec algorithm guarantees at least Maximin Share (MMS) of exposure for most of the producers and Envy-Free up to One item (EF1) fairness for every customer. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in the overall recommendation quality. CCS CONCEPTS• Information systems → Recommender systems.

Ride hailing platforms, such as Uber, Lyft, Ola or DiDi, have traditionally focused on the satisfaction of the passengers, or on boosting successful business transactions. However, recent studies provide a multitude of reasons to worry about the drivers in the ride hailing ecosystem. The concerns range from bad working conditions and worker manipulation to discrimination against minorities. With the sharing economy ecosystem growing, more and more drivers financially depend on online platforms and their algorithms to secure a living. It is pertinent to ask what a fair distribution of income on such platforms is and what power and means the platform has in shaping these distributions.In this paper, we analyze job assignments of a major taxi company and observe that there is significant inequality in the driver income distribution. We propose a novel framework to think about fairness in the matching mechanisms of ride hailing platforms. Specifically, our notion of fairness relies on the idea that, spread over time, all drivers should receive benefits proportional to the amount of time they are active in the platform. We postulate that by not requiring every match to be fair, but rather distributing fairness over time, we can achieve better overall benefit for the drivers and the passengers. We experiment with various optimization problems and heuristics to explore the means of achieving two-sided fairness, and investigate their caveats and side-effects. Overall, our work takes the first step towards rethinking fairness in ride hailing platforms with an additional emphasis on the well-being of drivers.

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To minimize battery drain due to background communication in cellular-connected devices such as smartphones, the duration for which the cellular radio is kept active should be minimized. This, in turn, calls for scheduling the background communication so as to maximize the throughput. It has been recognized in prior work that a key determinant of throughput is the wireless link quality. However, as we show here, another key factor is the load in the cell, arising from the communication of other nodes. Unlike link quality, the only way, thus far, for a cellular client to obtain a measure of load has been to perform active probing, which defeats the goal of minimizing the active duration of the radio.In this paper, we address the above dilemma by making the following contributions. First, we show experimentally that to obtain good throughput, considering link quality alone is insufficient, and that cellular load must also be factored in. Second, we present a novel technique called LoadSense for a cellular client to obtain a measure of the cellular load, locally and passively, that allows the client to determine the ideal times for communication when available throughput to the client is likely to be high. Finally, we present the Peek-n-Sneak protocol, which enables a cellular client to "peek" into the channel and "sneak" in with its background communication when the conditions are suitable. When multiple clients in a cell perform Peen-n-Sneak, it enables them to coordinate their communications, implicitly and in an entirely distributed manner, akin to CSMA in wireless LANs, helping improve throughput (and reduce energy drain) for all. Our experimental evaluation shows overall device energy savings of 20-60% even when Peek-n-Sneak is deployed incrementally.