Marian-Andrei Rizoiu

Marian-Andrei Rizoiu
University of Technology Sydney | UTS · Faculty of Engineering and Information Technology

PhD

About

78
Publications
18,999
Reads
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756
Citations
Additional affiliations
May 2014 - March 2016
National ICT Australia Ltd
Position
  • Researcher
September 2013 - May 2014
Université Lumiere Lyon 2
Position
  • PostDoc Position
October 2009 - June 2013
Université Lumiere Lyon 2
Position
  • PhD Student

Publications

Publications (78)
Preprint
Full-text available
This work introduces a novel multivariate temporal point process, the Partial Mean Behavior Poisson (PMBP) process, which can be leveraged to fit the multivariate Hawkes process to partially interval-censored data consisting of a mix of event timestamps on a subset of dimensions and interval-censored event counts on the complementary dimensions. Fi...
Preprint
Social influence pervades our everyday lives and lays the foundation for complex social phenomena. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect appears between psychology appr...
Preprint
Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considerable manual effort from researchers and may be overly focused and narrowed to certain online groups. In this work, we propose a complete solution to accelerate qualitative...
Article
Full-text available
Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because new technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displace...
Article
Full-text available
Ever since the web began, the number of websites has been growing exponentially. These websites cover an ever-increasing range of online services that fill a variety of social and economic functions across a growing range of industries. Yet the networked nature of the web, combined with the economics of preferential attachment, increasing returns a...
Preprint
Full-text available
Hawkes processes are a popular means of modeling the event times of self-exciting phenomena, such as earthquake strikes or tweets on a topical subject. Classically, these models are fit to historical event time data via likelihood maximization. However, in many scenarios, the exact times of historical events are not recorded for either privacy (e.g...
Chapter
This paper studies the dynamics of opinion formation and polarization in social media. We investigate whether users’ stance concerning contentious subjects is influenced by the online discussions they are exposed to and interactions with users supporting different stances. We set up a series of predictive exercises based on machine learning models....
Article
Full-text available
Developing new methods for modelling infectious diseases outbreaks is important for monitoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are well founded mathematical methods that enable us to combine the bene...
Article
In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science a...
Preprint
Full-text available
This paper studies the dynamics of opinion formation and polarization in social media. We investigate whether the stance of users with respect to contentious subjects is influenced by the online discussions that they are exposed to, and by the interactions with users supporting different stances. We set up a series of predictive exercises, in which...
Preprint
Full-text available
The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications. Social scientists and practitioners who model online discourse as a proxy for real-world behavior, often curate large social media datasets. A lack of available tooling...
Preprint
Full-text available
Job security can never be taken for granted, especially in times of rapid, widespread and unexpected social and economic change. These changes can force workers to transition to new jobs. This may be because technologies emerge or production is moved abroad. Perhaps it is a global crisis, such as COVID-19, which shutters industries and displaces la...
Preprint
It is not news that our mobile phones contain a wealth of private information about us, and that is why we try to keep them secure. But even the traces of how we communicate can also tell quite a bit about us. In this work, we start from the calling and texting history of 200 students enrolled in the Netsense study, and we link it to the type of re...
Preprint
In Australia and beyond, journalism is reportedly an industry in crisis, a crisis exacerbated by COVID-19. However, the evidence revealing the crisis is often anecdotal or limited in scope. In this unprecedented longitudinal research, we draw on data from the Australian journalism jobs market from January 2012 until March 2020. Using Data Science a...
Preprint
Full-text available
Developing new methods for modelling infectious diseases outbreaks is important for mon- itoring transmission and developing policy. In this paper we propose using semi-mechanistic Hawkes Processes for modelling malaria transmission in near-elimination settings. Hawkes Processes are mathematical methods that enable us to combine the benefits of bot...
Preprint
Full-text available
Traffic flow prediction, particularly in areas that experience highly dynamic flows such as motorways, is a major issue faced in traffic management. Due to increasingly large volumes of data sets being generated every minute, deep learning methods have been used extensively in the latest years for both short and long term prediction. However, such...
Preprint
Full-text available
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however, this is a challenging task due to inter-dependencies of traffic flow both in time an...
Preprint
Full-text available
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a disconnect between the practitioners of online social media analysis - usually social, political and communication scientists - and the accessibility to tools capable of handli...
Article
The Hawkes process (HP) has been widely applied to modeling self-exciting events including neuron spikes, earthquakes and tweets. To avoid designing parametric triggering kernel and to be able to quantify the prediction confidence, the non-parametric Bayesian HP has been proposed. However, the inference of such models suffers from unscalability or...
Preprint
This research develops a Machine Learning approach able to predict labor shortages for occupations. We compile a unique dataset that incorporates both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018. This includes data from 1.3 million job advertisements (ads) and 20 official labor force measures. We use these data as...
Conference Paper
Full-text available
A comprehensive understanding of data bias is the cornerstone of mitigating biases in social media research. This paper presents in-depth measurements of the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing two complete tweet streams, we show that Twitter rate li...
Preprint
Full-text available
A comprehensive understanding of data bias is the cornerstone of mitigating biases in social media research. This paper presents in-depth measurements of the effects of Twitter data sampling across different timescales and different subjects (entities, networks, and cascades). By constructing two complete tweet streams, we show that Twitter rate li...
Preprint
Ever since the web began, the number of websites has been growing exponentially. These websites cover an ever-increasing range of online services that fill a variety of social and economic functions across a growing range of industries. Yet the networked nature of the web, combined with the economics of preferential attachment, increasing returns a...
Preprint
Hawkes processes have been successfully applied to understand online information diffusion and popularity of online items. Most prior work concentrate on individually modeling successful diffusion cascades, while discarding smaller cascades which, however, account for a majority proportion of the available data. In this work, we propose a set of to...
Preprint
In this work, we develop a new approximation method to solve the analytically intractable Bayesian inference for Gaussian process models with factorizable Gaussian likelihoods and single-output latent functions. Our method -- dubbed QP -- is similar to the expectation propagation (EP), however it minimizes the $L^2$ Wasserstein distance instead of...
Article
Full-text available
Work is thought to be more enjoyable and beneficial to individuals and society when there is congruence between one’s personality and one’s occupation. We provide large-scale evidence that occupations have distinctive psychological profiles, which can successfully be predicted from linguistic information unobtrusively collected through social media...
Article
Full-text available
Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about th...
Preprint
Full-text available
This research develops a data-driven method to generate sets of highly similar skills based on a set of seed skills using online job advertisements (ads) data. This provides researchers with a novel method to adaptively select occupations based on granular skills data. We apply this adaptive skills similarity technique to a dataset of over 6.7 mill...
Preprint
Full-text available
Epidemic models and self-exciting processes are two types of models used to describe information diffusion. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the two model classes via three new mathematical c...
Conference Paper
Full-text available
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and...
Conference Paper
Full-text available
Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about th...
Preprint
Full-text available
Online videos have shown tremendous increase in Internet traffic. Most video hosting sites implement recommender systems, which connect the videos into a directed network and conceptually act as a source of pathways for users to navigate. At present, little is known about how human attention is allocated over such large-scale networks, and about th...
Conference Paper
Full-text available
In this paper, we develop an efficient non-parametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it provides flexible Hawkes kernels and quantifies their uncertainty. Our method is based on the cluster representation of Hawkes processes. Utilizing the stationarity of the...
Preprint
Full-text available
Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and...
Article
Full-text available
In the digital era, individuals are increasingly profiled and grouped based on the traces that they leave behind in online social networks such as Twitter and Facebook. In this paper, we develop and evaluate a novel text analysis approach for studying user identity and social roles by redefining identity as a sequence of timestamped items (e.g., tw...
Preprint
Full-text available
In today's society more and more people are connected to the Internet, and its information and communication technologies have become an essential part of our everyday life. Unfortunately, the flip side of this increased connectivity to social media and other online contents is cyber-bullying and -hatred, among other harmful and anti-social behavio...
Preprint
Full-text available
Predicting traffic incident duration is a major challenge for many traffic centres around the world. Most research studies focus on predicting the incident duration on motorways rather than arterial roads, due to a high network complexity and lack of data. In this paper we propose a bi-level framework for predicting the accident duration on arteria...
Preprint
Full-text available
The Hawkes process has been widely applied to modeling self-exciting events, including neuron spikes, earthquakes and tweets. To avoid designing parametric kernel functions and to be able to quantify the prediction confidence, non-parametric Bayesian Hawkes processes have been proposed. However the inference of such models suffers from unscalabilit...
Preprint
Full-text available
Online trolling has raised serious concerns about manipulating public opinion and exacerbating political divides among social media users. In this work, we analyse the role and behaviour of Russian trolls on Twitter through the lens of the social theory inspired by Tarde's ancient theory of monadology and its further development in Actor-Network Th...
Preprint
Full-text available
In this paper, we develop a non-parametric Bayesian estimation of Hawkes process kernel functions. Our method is based on the cluster representation of Hawkes processes. We sample random branching structures, and thus split the Hawkes process into clusters of Poisson processes, where the intensity function of each of these processes is the nonparam...
Conference Paper
Full-text available
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. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in...
Conference Paper
Full-text available
What makes content go viral Which videos become popular and why others don't Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, accessi...
Conference Paper
Full-text available
Among the statistical tools for online information diffusion modeling, both epidemic models and Hawkes point processes are popular choices. The former originate from epidemiology, and consider information as a viral contagion which spreads into a population of online users. The latter have roots in geophysics and finance, view individual actions as...
Article
Full-text available
Understanding and predicting the popularity of online items is an important open problem in social media analysis. Considerable progress has been made recently in data-driven predictions, and in linking popularity to external promotions. However, the existing methods typically focus on a single source of external influence, whereas for many types o...
Article
Full-text available
Serious concerns have been raised about the role of 'socialbots' in manipulating public opinion and influencing the outcome of elections by retweeting partisan content to increase its reach. Here we analyze the role and influence of socialbots on Twitter by determining how they contribute to retweet diffusions. We collect a large dataset of tweets...
Article
What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly in the context of online media. A range of models have been recently proposed to explain and predict popularity; however, there is a short supply of practical tools, acces...
Chapter
Full-text available
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and the key concepts in point processes. We then introduce the Hawkes process, its event intensity function, as well as schemes for event sim...
Chapter
Full-text available
This chapter provides an accessible introduction for point processes, and especially Hawkes processes, for modeling discrete, inter-dependent events over continuous time. We start by reviewing the definitions and key concepts in point processes. We then introduce the Hawkes process and its event intensity function, as well as schemes for event simu...
Conference Paper
Full-text available
Modeling the popularity dynamics of an online item is an important open problem in social media analysis and computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote on...
Conference Paper
Full-text available
Modeling and predicting the popularity of online content is a significant problem for the practice of information dissemination, advertising, and consumption. Recent work analyzing massive datasets advances our understanding of popularity, but one major gap remains: To precisely quantify the relationship between the popularity of an online item and...
Conference Paper
Full-text available
Predicting popularity, or the total volume of information outbreaks, is an important subproblem for understanding collective behavior in networks. Each of the two main types of recent approaches to the problem, feature-driven and generative models, have desired qualities and clear limitations. This paper bridges the gap between these solutions with...
Article
Full-text available
We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in an economical dataset) can be inferred from low-level descriptive features. Such high-level regulari...
Article
Full-text available
We study what drives the popularity of cultural items, by computationally quantifying a video's potential to become viral. We propose to model popularity lifecycles as the endogenous response to exogenous stimuli, dubbed Hawkes intensity process. This model can explain the complex, multi-phase, longitudinal popularity of a Youtube video. Moreover,...
Article
Full-text available
One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally describe images usually capture little information about the semantics of the images. In this paper, we tackle this difficulty by enriching the semantic content of the image...
Article
Full-text available
The cumulative effect of collective online participation has an important and adverse impact on individual privacy. As an online system evolves over time, new digital traces of individual behavior may uncover previously hidden statistical links between an individual's past actions and her private traits. To quantify this effect, we analyze the evol...
Article
Full-text available
We present CommentWatcher, an open source tool aimed at 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. The underlying social network...