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Found 34 papers, 12 papers with code
What Drives Online Popularity: Author, Content or Sharers? Estimating Spread Dynamics with Bayesian Mixture Hawkes
In this work, we propose the Bayesian Mixture Hawkes (BMH) model to jointly learn the influence of source, content and spread.
Misinformation is not about Bad Facts: An Analysis of the Production and Consumption of Fringe Content
What if misinformation is not an information problem at all?
The Innovation-to-Occupations Ontology: Linking Business Transformation Initiatives to Occupations and Skills
The fast adoption of new technologies forces companies to continuously adapt their operations making it harder to predict workforce requirements.
Enhancing Biomedical Text Summarization and Question-Answering: On the Utility of Domain-Specific Pre-Training
Biomedical summarization requires large datasets to train for text generation.
Informing Innovation Management: Linking Leading R&D Firms and Emerging Technologies
Understanding the relationship between emerging technology and research and development has long been of interest to companies, policy makers and researchers.
The Science of Startups: The Impact of Founder Personalities on Company Success
Startup companies solve many of today's most complex and challenging scientific, technical and social problems, such as the decarbonisation of the economy, air pollution, and the development of novel life-saving vaccines.
Being Automated or Not? Risk Identification of Occupations with Graph Neural Networks
In this paper, we study the automation risk of occupations by performing a classification task between automated and non-automated occupations.
Detect Hate Speech in Unseen Domains using Multi-Task Learning: A Case Study of Political Public Figures
We simulate evaluation on new previously unseen datasets by adopting a leave-one-out scheme in which we omit a target dataset from training and jointly train on the other datasets.
Opinion Market Model: Stemming Far-Right Opinion Spread using Positive Interventions
To evaluate the effectiveness of positive interventions, we introduce the Opinion Market Model (OMM), a two-tier online opinion ecosystem model that considers both inter-opinion interactions and the role of positive interventions.
Linking Across Data Granularity: Fitting Multivariate Hawkes Processes to Partially Interval-Censored Data
The multivariate Hawkes process (MHP) is widely used for analyzing data streams that interact with each other, where events generate new events within their own dimension (via self-excitation) or across different dimensions (via cross-excitation).
Interval-censored Hawkes processes
We propose the multi-impulse exogenous function - for when the exogenous events are observed as event time - and the latent homogeneous Poisson process exogenous function - for when the exogenous events are presented as interval-censored volumes.
Skill-driven Recommendations for Job Transition Pathways
Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work.
Layoffs, Inequity and COVID-19: A Longitudinal Study of the Journalism Jobs Crisis in Australia from 2012 to 2020
We then tease out more granular findings, including: that there are now more women than men journalists in Australia, but that gender inequity is worsening, with women journalists getting younger and worse-paid just as men journalists are, on average, getting older and better-paid; that, despite the crisis besetting the industry, the demand for journalism skills has increased; and that, perhaps concerningly, the skills sought by journalism job ads increasingly include social media and generalist communications.
Graph modelling approaches for motorway traffic flow prediction
Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management.
Traffic congestion anomaly detection and prediction using deep learning
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
A Toolkit for Analyzing and Visualizing Online Users via Reshare Cascade Modeling
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior.
Social and Information Networks Computers and Society
Variation across Scales: Measurement Fidelity under Twitter Data Sampling
This paper presents in-depth measurements on the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades).
Quantile Propagation for Wasserstein-Approximate Gaussian Processes
We show that QP matches quantile functions rather than moments as in EP and has the same mean update but a smaller variance update than EP, thereby alleviating EP's tendency to over-estimate posterior variances.
Estimating Attention Flow in Online Video Networks
In this paper, we first construct the Vevo network -- a YouTube video network with 60, 740 music videos interconnected by the recommendation links, and we collect their associated viewing dynamics.
Motorway Traffic Flow Prediction using Advanced Deep Learning
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling.
Transfer Learning for Hate Speech Detection in Social Media
This paper uses a transfer learning technique to leverage two independent datasets jointly and builds a single representation of hate speech.
Social and Information Networks Computers and Society
Arterial incident duration prediction using a bi-level framework of extreme gradient-tree boosting
Predicting traffic incident duration is a major challenge for many traffic centres around the world.
Variational Inference for Sparse Gaussian Process Modulated Hawkes Process
We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection.
Efficient Non-parametric Bayesian Hawkes Processes
On two large-scale Twitter diffusion datasets, we show that our methods outperform the current state-of-the-art in goodness-of-fit and that the time complexity is linear in the size of the dataset.
Modeling Popularity in Asynchronous Social Media Streams with Recurrent Neural Networks
We find that results depend on the type of content being promoted: superusers are more successful in promoting Howto and Gaming videos, whereas the cohort of regular users are more influential for Activism videos.
#DebateNight: The Role and Influence of Socialbots on Twitter During the 1st 2016 U.S. Presidential Debate
We collect a large dataset of tweets during the 1st U. S. Presidential Debate in 2016 (#DebateNight) and we analyze its 1. 5 million users from three perspectives: user influence, political behavior (partisanship and engagement) and botness.
Social and Information Networks
Beyond Views: Measuring and Predicting Engagement in Online Videos
The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance.
Social and Information Networks Human-Computer Interaction
A Tutorial on Hawkes Processes for Events in Social Media
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time.
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption.
Social and Information Networks
How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions
In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension.
Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering
Evolutionary clustering aims at capturing the temporal evolution of clusters.
Unsupervised Feature Construction for Improving Data Representation and Semantics
We seek to construct, in an unsupervised way, new features that are more appropriate for describing a given dataset and, at the same time, comprehensible for a human user.
Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context
We apply our proposition to the task of content-based image classification and we show that semantically enriching the image representation yields higher classification performances than the baseline representation.
CommentWatcher: An Open Source Web-based platform for analyzing discussions on web forums
Constructed as a web platform, CommentWatcher features automatic mass fetching of user posts from forum on multiple sites, extracting topics, visualizing the topics as an expression cloud and exploring their temporal evolution.
