Related papers: Opening up echo chambers via optimal content recom…
This paper addresses the problem of designing recommendation systems for social networks and e-commerce platforms from a control-theoretic perspective. We treat the design of recommendation systems as a state-feedback infinite-horizon…
Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. Recent studies reveal the existence of…
The Friedkin-Johnsen (FJ) model is a popular opinion dynamics model that explains the disagreement that can occur even among closely interacting individuals. Cluster consensus is a special type of disagreement, where agents in a network…
In a stylized voting model, we establish that increasing the share of critical thinkers -- individuals who are aware of the ambivalent nature of a certain issue -- in the population increases the efficiency of surveys (elections) but might…
Social media is often criticized for amplifying toxic discourse and discouraging constructive conversations. But designing social media platforms to promote better conversations is inherently challenging. This paper asks whether simulating…
Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and (largely ad-hoc) hybrid systems. We propose a unified…
Social media has a misinformation problem, and counterspeech -- fighting bad speech with more speech -- has been an ineffective solution. Here, we argue that bridging-based ranking -- an algorithmic approach to promoting content favored by…
We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the…
Recommender systems constitute the core engine of most social network platforms nowadays, aiming to maximize user satisfaction along with other key business objectives. Twitter is no exception. Despite the fact that Twitter data has been…
In online platforms, recommender systems are responsible for directing users to relevant contents. In order to enhance the users' engagement, recommender systems adapt their output to the reactions of the users, who are in turn affected by…
Network-based people recommendation algorithms are widely employed on the Web to suggest new connections in social media or professional platforms. While such recommendations bring people together, the feedback loop between the algorithms…
Twitter, one of the biggest and most popular microblogging Websites, has evolved into a powerful communication platform which allows millions of active users to generate huge volume of microposts and queries on a daily basis. To accommodate…
Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds…
We propose a graph contextualization method, pairGraphText, to study political engagement on Facebook during the 2012 French presidential election. It is a spectral algorithm that contextualizes graph data with text data for online…
Political polarisation on structured discussion platforms such as Reddit differs fundamentally from that on broadcast platforms such as Twitter/X, yet most prior work targets the latter. We present an end-to-end framework for measuring and…
Public discourse and opinions stem from multiple social groups. Each group has beliefs about a topic (such as vaccination, abortion, gay marriage, etc.), and opinions are exchanged and blended to produce consensus. A particular measure of…
For many years, achievements and discoveries made by scientists are made aware through research papers published in appropriate journals or conferences. Often, established scientists and especially newbies are caught up in the dilemma of…
ECHO (Evaluation of Chat, Human behavior, and Outcomes) is an open research platform designed to support reproducible, mixed-method studies of human interaction with both conversational AI systems and Web search engines. It enables…
Social media platforms shape public discourse through two fundamental design choices that naturally co-occur in any field investigation: platform architecture, which defines what types of actors exist and how they interact, and…
Recommender systems require their recommendation algorithms to be accurate, scalable and should handle very sparse training data which keep changing over time. Inspired by ant colony optimization, we propose a novel collaborative filtering…