Showing papers by "Augustin Chaintreau published in 2018"

Algorithmic Glass Ceiling in Social Networks: The effects of social recommendations on network diversity

Abstract: As social recommendations such as friend suggestions and people to follow become increasingly popular and influential on the growth of social media, we find that prominent social recommendation algorithms can exacerbate the under-representation of certain demographic groups at the top of the social hierarchy. To study this imbalance in online equal opportunities, we leverage new Instagram data and offer for the first time an analysis that studies the effect of gender, homophily and growth dynamics under social recommendations. Our mathematical analysis demonstrates the existence of an algorithmic glass ceiling that exhibits all the properties of the metaphorical social barrier that hinders groups like women or people of color from attaining equal representation. What raises concern is that our proof shows that under fixed minority and homophily parameters the algorithmic effect is systematically larger than the glass ceiling generated by the spontaneous growth of social networks. We discuss ways to address this concern in future design.

Throughput Scalability Analysis of Fork-Join Queueing Networks

Abstract: Parallel and distributed processing systems have expanded in size as technology advances in cloud computing and big data analytics. A critical issue concerns throughput scalability: whether through...

Dynamics and Prediction of Clicks on News from Twitter

Abstract: Social networks are a major gateway to access news content. It is estimated that a third of all web visits originate on social media, and about half of users rely on those to keep up-to-date with world events. Strangely, no model has been proposed and validated to study how to reproduce and interpolate clicks created by social media. Here we study news posted on Twitter, leveraging public information as well as private data from a popular online publisher. We propose and validate a simple two-step model of information diffusion that can be easily interpreted and applied using only public information to determine current and future clicks.

PopFactor: Live-Streamer Behavior and Popularity

Abstract: Live video-streaming platforms such as Twitch enable top content creators to reap significant profits and influence. To that effect, various behavioral norms are recommended to new entrants and those seeking to increase their popularity and success. Chiefly among them are to simply put in the effort and promote on social media outlets such as Twitter, Instagram, and the like. But does following these behaviors indeed have a relationship with eventual popularity? In this paper, we collect a corpus of Twitch streamer popularity measures --- spanning social and financial measures --- and their behavior data on Twitch and third party platform. We also compile a set of community-defined behavioral norms. We then perform temporal analysis to identify the increased predictive value that a streamer's future behavior contributes to predicting future popularity. At the population level, we find that behavioral information improves the prediction of relative growth that exceeds the median streamer. At the individual level, we find that although it is difficult to quickly become successful in absolute terms, streamers that put in considerable effort are more successful than the rest, and that creating social media accounts to promote oneself is effective irrespective of when the accounts are created. Ultimately, we find that studying the popularity and success of content creators in the long term is a promising and rich research area.

How long does it take for all users in a social network to choose their communities

Abstract: We consider a community formation problem in social networks, where the users are either friends or enemies. The users are partitioned into conflict-free groups (i.e., independent sets in the conflict graph $G^- =(V,E)$ that represents the enmities between users). The dynamics goes on as long as there exists any set of at most k users, k being any fixed parameter, that can change their current groups in the partition simultaneously, in such a way that they all strictly increase their utilities (number of friends i.e., the cardinality of their respective groups minus one). Previously, the best-known upper-bounds on the maximum time of convergence were $O(|V|\alpha(G^-))$ for k $\leq 2$ and $O(|V|^3) for k=3$, with $\alpha(G^-)$ being the independence number of $G^-$. Our first contribution in this paper consists in reinterpreting the initial problem as the study of a dominance ordering over the vectors of integer partitions. With this approach, we obtain for $k \leq 2$ the tight upper-bound $O(|V| \min\{ \alpha(G^-)$, $\sqrt{|V|} \})$ and, when $G^-$ is the empty graph, the exact value of order $\frac{(2|V|)^{3/2}}{3}$. The time of convergence, for any fixed k \geq 4, was conjectured to be polynomial. In this paper we disprove this. Specifically, we prove that for any k \geq 4, the maximum time of convergence is an $\Omega(|V|^{\Theta(\log{|V|})})$.

Information Market for Web Browsing: Design, Usability and Incremental Adoption

Abstract: Browsing privacy solutions face an uphill battle to deployment. Many operate counter to the economic objectives of popular online services (e.g., by completely blocking ads) and do not provide enough incentive for users who may be subject to performance degradation for deploying them. In this study, we take a step towards realizing a system for online privacy that is mutually beneficial to users and online advertisers: an information market. This system not only maintains economic viability for online services, but also provides users with financial compensation to encourage them to participate. We prototype and evaluate an information market that provides privacy and revenue to users while preserving and sometimes improving their Web performance. We evaluate feasibility of the market via a one month field study with 63 users and find that users are indeed willing to sell their browsing information. We also use Web traces of millions of users to drive a simulation study to evaluate the system at scale. We find that the system can indeed be profitable to both users and online advertisers.