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Online platforms have a wealth of data, run countless experiments and use industrial-scale algorithms to optimize user experience. Despite this, many users seem to regret the time they spend on these platforms. One possible explanation is…
There has been considerable interest in modelling the spread of information on X (formerly Twitter) using machine learning models. Here, we consider the problem of predicting the reposting of new information, i.e., when a user propagates…
Over the last ten years, a sharp rise in the number of dating apps has broadened the spectrum of how one can get in contact with new acquaintances. A common feature of such apps is a swipe enabling a user to decide whether to like or…
In this work, we ask two questions: 1. Can we predict the type of community interested in a news article using only features from the article content? and 2. How well do these models generalize over time? To answer these questions, we…
Fashion is a multi-billion dollar industry with social and economic implications worldwide. To gain popularity, brands want to be represented by the top popular models. As new faces are selected using stringent (and often criticized)…
We introduce a model for predicting page-view dynamics of promoted content. The regularity of the content promotion process on Wikipedia provides excellent experimental conditions which favour detailed modelling. We show that the popularity…
For many users on social networks, one of the goals when broadcasting content is to reach a large audience. The probability of receiving reactions to a message differs for each user and depends on various factors, such as location, daily…
Several factors influence the popularity of content on social media, including the what, when, and who of a post. Of these factors, the what and when of content are easiest to customize in order to maximize viewership and reach. Further,…
Social media platforms are extensively used for sharing personal emotions, daily activities, and various life events, keeping people updated with the latest happenings. From the moment a user creates an account, they continually expand…
In this paper, we propose a method of ranking recently created Twitter accounts according to their prospective popularity. Early detection of new promising accounts is useful for trend prediction, viral marketing, user recommendation, and…
The massive amounts of data that social media generates has facilitated the study of online human behavior on a scale unimaginable a few years ago. At the same time, the much discussed apparent randomness with which people interact online…
Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive…
Choice decisions made by users of online applications can suffer from biases due to the users' level of engagement. For instance, low engagement users may make random choices with no concern for the quality of items offered. This biased…
Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user…
Large-scale data resulting from users online interactions provide the ultimate source of information to study emergent social phenomena on the Web. From individual actions of users to observable collective behaviors, different mechanisms…
Increasingly people form opinions based on information they consume on online social media. As a result, it is crucial to understand what type of content attracts people's attention on social media and drive discussions. In this paper we…
This paper describes a novel diffusion model, DyDiff-VAE, for information diffusion prediction on social media. Given the initial content and a sequence of forwarding users, DyDiff-VAE aims to estimate the propagation likelihood for other…
Studying temporal dynamics of topics in social media is very useful to understand online user behaviors. Most of the existing work on this subject usually monitors the global trends, ignoring variation among communities. Since users from…
We introduce an original mathematical model to analyse the diffusion of posts within a generic online social platform. The main novelty is that each user is not simply considered as a node on the social graph, but is further equipped with…
User activities can influence their subsequent interactions with a post, generating interest in the user. Typically, users interact with posts from friends by commenting and using reaction emojis, reflecting their level of interest on…