ORCID Profile
0000-0003-0381-669X
Current Organisation
University of Technology Sydney
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Communication technology and digital media studies | Communications and media policy | Media and communication law | Communication and media studies |
Publisher: Springer International Publishing
Date: 2021
Publisher: arXiv
Date: 2021
Publisher: arXiv
Date: 2022
Publisher: Public Library of Science (PLoS)
Date: 04-2021
DOI: 10.1371/JOURNAL.PCBI.1008830
Abstract: 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 benefits of both statistical and mechanistic models to recreate and forecast disease transmission beyond just malaria outbreak scenarios. These methods have been successfully used in numerous applications such as social media and earthquake modelling, but are not yet widespread in epidemiology. By using domain-specific knowledge, we can both recreate transmission curves for malaria in China and Eswatini and disentangle the proportion of cases which are imported from those that are community based.
Publisher: IEEE
Date: 10-12-2020
Publisher: IGI Global
Date: 2011
DOI: 10.4018/978-1-60960-625-1.CH003
Abstract: This chapter addresses the issue of topic extraction from text corpora for ontology learning. The first part provides an overview of some of the most significant solutions present today in the literature. These solutions deal mainly with the inferior layers of the Ontology Learning Layer Cake. They are related to the challenges of the Terms and Synonyms layers. The second part shows how these pieces can be bound together into an integrated system for extracting meaningful topics. While the extracted topics are not proper concepts as yet, they constitute a convincing approach towards concept building and therefore ontology learning. This chapter concludes by discussing the research undertaken for filling the gap between topics and concepts as well as perspectives that emerge today in the area of topic extraction.
Publisher: arXiv
Date: 2018
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: ACM
Date: 08-02-2016
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Publisher: Elsevier BV
Date: 2020
DOI: 10.2139/SSRN.3681132
Publisher: arXiv
Date: 2019
Publisher: ACM
Date: 30-04-2023
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-05-2017
DOI: 10.1609/ICWSM.V11I1.14865
Abstract: Modeling the popularity dynamics of an online item is an important open problem in 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 online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.
Publisher: IEEE
Date: 11-2012
Publisher: IEEE
Date: 20-09-2020
Publisher: Springer Science and Business Media LLC
Date: 26-12-2015
Publisher: Wiley
Date: 04-08-2021
DOI: 10.1111/NPH.17612
Abstract: Cotton fibre is the most important source for natural textiles. The secondary cell walls (SCWs) of mature cotton fibres contain the highest proportion of cellulose content ( 90%) in any plant. The onset and progression of SCW cellulose synthesis need to be tightly controlled to balance fibre elongation and cell wall deposition. However, regulatory mechanisms that control cellulose synthesis during cotton fibre growth remain elusive. Here, we conducted genetic and functional analyses demonstrating that the R2R3‐MYB GhMYB7 controls cotton fibre cellulose synthesis. Overexpression of GhMYB7 in cotton sped up SCW cellulose biosynthesis in fibre cells, and led to shorter fibres with thicker walls. By contrast, RNA interference (RNAi) silencing of GhMYB7 delayed fibre SCW cellulose synthesis and resulted in elongated fibres with thinner walls. Furthermore, we demonstrated that GhMYB7 regulated cotton fibre SCW cellulose synthases by directly binding to three distinct cis ‐elements in the respective GhCesA4 , GhCesA7 and GhCesA8 promoters. We found that this regulatory mechanism of cellulose synthesis was ‘hi‐jacked’ also by other GhMYBs. Together, our findings uncover a hitherto‐unknown mechanism that cotton fibre employs to regulate SCW cellulose synthesis. Our results also provide a strategy for genetic improvement of SCW thickness of cotton fibre.
Publisher: Research Square Platform LLC
Date: 03-11-2022
DOI: 10.21203/RS.3.RS-2120305/V1
Abstract: Social influence pervades our everyday lives and lays the foundation for complex social phenomena, such as the spread of misinformation and the polarization of communities. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted, public health measures are observed, or immunization c aigns meet their targets. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect appears between psychology approaches, which are generally performed and tested in controlled lab experiments, and the quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues (such as an ongoing pandemic) the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work begins to bridge this gap and presents three contributions towards modeling and empirically quantifying online influence. The first contribution is a data-driven Generalized Influence Model that incorporates two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the influence-capital distribution. The second contribution is a framework to empirically rank users' social influence using a human-in-the-loop method combined with crowdsourced pairwise influence comparisons. We build a human-labeled ground truth, calibrate our generalized influence model and perform a large-scale evaluation of influence. We find that our generalized model outperforms the current state-of-the-art approaches and corrects the inherent biases introduced by the widely used follower count. As the third contribution, we apply the influence model to discussions around COVID-19. We quantify users' influence and content veracity, tabulating it against their professions. We find that executives, media, and the military are more influential than pandemic-related experts such as life scientists and healthcare professionals. Worryingly, by leveraging existing COVID-19 misinformation datasets, we show that some of the most influential occupations also spread the most misinformation. These findings raise questions about the effectiveness of information dissemination by experts in situations of crisis.
Publisher: Proceedings of the National Academy of Sciences
Date: 11-12-2019
Abstract: Work is thought to be more enjoyable and beneficial to in iduals 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. Based on 128,279 Twitter users representing 3,513 occupations, we automatically assess user personalities and visually map the personality profiles of different professions. Similar occupations cluster together, pointing to specific sets of jobs that one might be well suited for. Observations that contradict existing classifications may point to emerging occupations relevant to the 21st century workplace. Findings illustrate how social media can be used to match people to their ideal occupation.
Publisher: Springer Science and Business Media LLC
Date: 24-02-2013
Publisher: arXiv
Date: 2020
Publisher: World Scientific Pub Co Pte Lt
Date: 08-2014
DOI: 10.1142/S0218213014600136
Abstract: In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant in time are considered as dissimilar. We also propose a method to enforce the segmentation contiguity, by introducing, in the objective function, a penalty term inspired from the Normal Distribution Function. We combine the two propositions into a novel time-driven constrained clustering algorithm, called TDCK-Means, which creates a partition of coherent clusters, both in the multidimensional space and in the temporal space. This algorithm uses soft semi-supervised constraints, to encourage adjacent observations belonging to the same entity to be assigned to the same cluster. We apply our algorithm to a Political Studies dataset in order to detect typical evolution phases. We adapt the Shannon entropy in order to measure the entity contiguity, and we show that our proposition consistently improves temporal cohesion of clusters, without any significant loss in the multidimensional variance.
Publisher: Association for Computing Machinery and Morgan & Claypool
Date: 19-12-2018
Publisher: IEEE
Date: 12-2019
Publisher: arXiv
Date: 2022
Publisher: Proceedings of the National Academy of Sciences
Date: 14-08-2023
Abstract: In 2022, the European Union introduced the Digital Services Act (DSA), a new legislation to report and moderate harmful content from online social networks. Trusted flaggers are mandated to identify harmful content, which platforms must remove within a set delay (currently 24 h). Here, we analyze the likely effectiveness of EU-mandated mechanisms for regulating highly viral online content with short half-lives. We deploy self-exciting point processes to determine the relationship between the regulated moderation delay and the likely harm reduction achieved. We find that harm reduction is achievable for the most harmful content, even for fast-paced platforms such as Twitter. Our method estimates moderation effectiveness for a given platform and provides a rule of thumb for selecting content for investigation and flagging, managing flaggers’ workload.
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2019
Abstract: 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 Hawkes process, we efficiently s le random branching structures and thus, we split the Hawkes process into clusters of Poisson processes. We derive two algorithms --- a block Gibbs s ler and a maximum a posteriori estimator based on expectation maximization --- and we show that our methods have a linear time complexity, both theoretically and empirically. On synthetic data, we show our methods to be able to infer flexible Hawkes triggering kernels. 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. We also observe that on diffusions related to online videos, the learned kernels reflect the perceived longevity for different content types such as music or pets videos.
Publisher: arXiv
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: ACM Press
Date: 2018
Publisher: International World Wide Web Conferences Steering Committee
Date: 03-04-2017
Publisher: arXiv
Date: 2021
Publisher: arXiv
Date: 2019
Publisher: arXiv
Date: 2020
Publisher: arXiv
Date: 2017
Publisher: Public Library of Science (PLoS)
Date: 04-08-2021
DOI: 10.1371/JOURNAL.PONE.0254722
Abstract: 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 displaces labor en masse . Regardless of the impetus, people are faced with the challenge of moving between jobs to find new work. Successful transitions typically occur when workers leverage their existing skills in the new occupation. Here, we propose a novel method to measure the similarity between occupations using their underlying skills. We then build a recommender system for identifying optimal transition pathways between occupations using job advertisements (ads) data and a longitudinal household survey. Our results show that not only can we accurately predict occupational transitions (Accuracy = 76%), but we account for the asymmetric difficulties of moving between jobs (it is easier to move in one direction than the other). We also build an early warning indicator for new technology adoption (showcasing Artificial Intelligence), a major driver of rising job transitions. By using real-time data, our systems can respond to labor demand shifts as they occur (such as those caused by COVID-19). They can be leveraged by policy-makers, educators, and job seekers who are forced to confront the often distressing challenges of finding new jobs.
Publisher: SAGE Publications
Date: 23-02-2021
Abstract: 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 and Machine Learning techniques, we analyse two distinct data sets: job advertisements (ads) data comprising 3698 journalist job ads from a corpus of over 8 million Australian job ads and official employment data from the Australian Bureau of Statistics. Having matched and analysed both sources, we address both the demand for and supply of journalists in Australia over this critical period. The data show that the crisis is real, but there are also surprises. Counter-intuitively, the number of journalism job ads in Australia rose from 2012 until 2016, before falling into decline. Less surprisingly, for the entire period studied the figures reveal extreme volatility, characterised by large and erratic fluctuations. The data also clearly show that COVID-19 has significantly worsened the crisis. 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’ skills.
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 03-04-2020
Abstract: 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 slow convergence. In this paper, we aim to solve both problems. Specifically, first, we propose a new non-parametric Bayesian HP in which the triggering kernel is modeled as a squared sparse Gaussian process. Then, we propose a novel variational inference schema for model optimization. We employ the branching structure of the HP so that maximization of evidence lower bound (ELBO) is tractable by the expectation-maximization algorithm. We propose a tighter ELBO which improves the fitting performance. Further, we accelerate the novel variational inference schema to linear time complexity by leveraging the stationarity of the triggering kernel. Different from prior acceleration methods, ours enjoys higher efficiency. Finally, we exploit synthetic data and two large social media datasets to evaluate our method. We show that our approach outperforms state-of-the-art non-parametric frequentist and Bayesian methods. We validate the efficiency of our accelerated variational inference schema and practical utility of our tighter ELBO for model selection. We observe that the tighter ELBO exceeds the common one in model selection.
Publisher: Elsevier BV
Date: 2023
DOI: 10.2139/SSRN.4359859
Publisher: arXiv
Date: 2022
Publisher: arXiv
Date: 2018
Publisher: ACM
Date: 19-10-2020
Publisher: arXiv
Date: 2022
Publisher: IEEE
Date: 10-2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Springer Science and Business Media LLC
Date: 07-2019
Publisher: arXiv
Date: 2017
Publisher: Springer Science and Business Media LLC
Date: 17-10-2023
Publisher: Public Library of Science (PLoS)
Date: 28-04-2021
DOI: 10.1371/JOURNAL.PONE.0249993
Abstract: 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 and global trade, suggest that over the long run a small number of competitive giants are likely to dominate each functional market segment, such as search, retail and social media. Here we perform a large scale longitudinal study to quantify the distribution of attention given in the online environment to competing organisations. In two large online social media datasets, containing more than 10 billion posts and spanning more than a decade, we tally the volume of external links posted towards the organisations’ main domain name as a proxy for the online attention they receive. We also use the Common Crawl dataset—which contains the linkage patterns between more than a billion different websites—to study the patterns of link concentration over the past three years across the entire web. Lastly, we showcase the linking between economic, financial and market data by exploring the relationships between online attention on social media and the growth in enterprise value in the electric carmaker Tesla. Our analysis shows that despite the fact that we observe consistent growth in all the macro indicators—the total amount of online attention, in the number of organisations with an online presence, and in the functions they perform—we also observe that a smaller number of organisations account for an ever-increasing proportion of total user attention, usually with one large player dominating each function. These results highlight how evolution of the online economy involves innovation, ersity, and then competitive dominance.
Publisher: Springer Berlin Heidelberg
Date: 2011
Publisher: Informa UK Limited
Date: 29-04-2023
Publisher: Research Square Platform LLC
Date: 07-03-2023
DOI: 10.21203/RS.3.RS-2590597/V1
Abstract: 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. Startups are a vital source of social, scientific and economic innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors as well as the team’s size. The effects of founders’ personalities on the success of new ventures are mainly unknown. Here we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global s le of startups (n=26,781). We found that the Big 5 personality traits of startup founders across 30 dimensions significantly differed from that of the population at large. We can train a classifier to distinguish founders from employees with 82.5% accuracy. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). However, we do not find one “Founder-type” personality instead, six different personality types appear, with startups founded by a “Hipster, Hacker and Hustler” being twice as likely to succeed. Our results also demonstrate the benefits of larger, personality- erse teams in startups, which has the potential to be extended through further research into other team settings within business, government and research.
Publisher: arXiv
Date: 2015
Location: Romania
Start Date: 04-2023
End Date: 04-2024
Amount: $215,000.00
Funder: Australian Research Council
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