19 research outputs found
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
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 long
run, 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.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
To help their users to discover important items at a particular time, major
websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K
recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most
Viewed News Stories), which rely on crowdsourced popularity signals to select
the items. However, different sections of a crowd may have different
preferences, and there is a large silent majority who do not explicitly express
their opinion. Also, the crowd often consists of actors like bots, spammers, or
people running orchestrated campaigns. Recommendation algorithms today largely
do not consider such nuances, hence are vulnerable to strategic manipulation by
small but hyper-active user groups.
To fairly aggregate the preferences of all users while recommending top-K
items, we borrow ideas from prior research on social choice theory, and
identify a voting mechanism called Single Transferable Vote (STV) as having
many of the fairness properties we desire in top-K item (s)elections. We
develop an innovative mechanism to attribute preferences of silent majority
which also make STV completely operational. We show the generalizability of our
approach by implementing it on two different real-world datasets. Through
extensive experimentation and comparison with state-of-the-art techniques, we
show that our proposed approach provides maximum user satisfaction, and cuts
down drastically on items disliked by most but hyper-actively promoted by a few
users.Comment: In the proceedings of the Conference on Fairness, Accountability, and
Transparency (FAT* '19). Please cite the conference versio
Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations
Major online platforms today can be thought of as two-sided markets with
producers and customers of goods and services. There have been concerns that
over-emphasis on customer satisfaction by the platforms may affect the
well-being of the producers. To counter such issues, few recent works have
attempted to incorporate fairness for the producers. However, these studies
have overlooked an important issue in such platforms -- to supposedly improve
customer utility, the underlying algorithms are frequently updated, causing
abrupt changes in the exposure of producers. In this work, we focus on the
fairness issues arising out of such frequent updates, and argue for incremental
updates of the platform algorithms so that the producers have enough time to
adjust (both logistically and mentally) to the change. However, naive
incremental updates may become unfair to the customers. Thus focusing on
recommendations deployed on two-sided platforms, we formulate an ILP based
online optimization to deploy changes incrementally in n steps, where we can
ensure smooth transition of the exposure of items while guaranteeing a minimum
utility for every customer. Evaluations over multiple real world datasets show
that our proposed mechanism for platform updates can be efficient and fair to
both the producers and the customers in two-sided platforms.Comment: To Appear In the Proceedings of 34th AAAI Conference on Artificial
Intelligence (AAAI), New York, USA, Feb 202
Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations
International audienceTo help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowd-sourced popularity signals to select the items. However, diferent sections of a crowd may have diferent preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Trans-ferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two diferent real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users
White, Man, and Highly Followed: Gender and Race Inequalities in Twitter
Social media is considered a democratic space in which people connect and
interact with each other regardless of their gender, race, or any other
demographic factor. Despite numerous efforts that explore demographic factors
in social media, it is still unclear whether social media perpetuates old
inequalities from the offline world. In this paper, we attempt to identify
gender and race of Twitter users located in U.S. using advanced image
processing algorithms from Face++. Then, we investigate how different
demographic groups (i.e. male/female, Asian/Black/White) connect with other. We
quantify to what extent one group follow and interact with each other and the
extent to which these connections and interactions reflect in inequalities in
Twitter. Our analysis shows that users identified as White and male tend to
attain higher positions in Twitter, in terms of the number of followers and
number of times in user's lists. We hope our effort can stimulate the
development of new theories of demographic information in the online space.Comment: In Proceedings of the IEEE/WIC/ACM International Conference on Web
Intelligence (WI'17). Leipzig, Germany. August 201
Coordinating Cellular Background Transfers using LoadSense
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
Dissemination Biases of Social Media Channels: On the Topical Coverage of Socially Shared News
In a marked departure from traditional offline media, where all subscribers of a particular news media source (e.g., New York Times) used to get the same news stories through printed newspapers, online news media presents multiple options for the readers to consume news. For example, the subscribers of a media source can get news directly from the news website, or from what their peers share over social media sites like Facebook and Twitter. It is, however, unclear whether there are any differences in the news disseminated on these different online channels. In this work, we analyze data from a popular online news media site (nytimes.com), and show that each of these different channels tends to highlight some types of stories more than other stories. We believe that consumers of online news as well as media organizations need to be aware of such differences in various online news dissemination channels