activity
20152022
most cited#ArsonEmergency and Australia's "Black Summer": Polarisation and misinformation on social media

20 citations · 51 across the 12 of their papers we have counts for

collaborators

12 papers

cs.SI2022★ 1 cited

Digital Information Seeking and Sharing Behavior During the First Wave of the COVID-19 Pandemic

Mehak Fatima, Aimal Rextin, Mehwish Nasim +1

People turn to search engines and social media to seek information during population-level events, such as during civil unrest, disease outbreaks, fires, or flood. They also tend t…

cs.SI2022

Are we always in strife? A longitudinal study of the echo chamber effect in the Australian Twittersphere

Mehwish Nasim, Derek Weber, Tobin South +4

Contrary to expectations that the increased connectivity offered by the internet and particularly Online Social Networks (OSNs) would result in broad consensus on contentious issue…

cs.SI2022★ 12 cited

Promoting and countering misinformation during Australia's 2019-2020 bushfires: A case study of polarisation

Derek Weber, Lucia Falzon, Lewis Mitchell +1

During Australia's unprecedented bushfires in 2019-2020, misinformation blaming arson resurfaced on Twitter using #ArsonEmergency. The extent to which bots were responsible for dis…

cs.CR2021

Systematic Literature Review on Cyber Situational Awareness Visualizations

Liuyue Jiang, Asangi Jayatilaka, Mehwish Nasim +3

The dynamics of cyber threats are increasingly complex, making it more challenging than ever for organizations to obtain in-depth insights into their cyber security status. Therefo…

cs.SI2021★ 14 cited

Exploring the effect of streamed social media data variations on social network analysis

Derek Weber, Mehwish Nasim, Lewis Mitchell +1

To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications…

cs.SI2020

A method to evaluate the reliability of social media data for social network analysis

Derek Weber, Mehwish Nasim, Lewis Mitchell +1

To study the effects of Online Social Network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications…

cs.SI2020★ 20 cited

#ArsonEmergency and Australia's "Black Summer": Polarisation and misinformation on social media

Derek Weber, Mehwish Nasim, Lucia Falzon +1

During the summer of 2019-20, while Australia suffered unprecedented bushfires across the country, false narratives regarding arson and limited backburning spread quickly on Twitte…

cs.CR2019

Gathering Cyber Threat Intelligence from Twitter Using Novelty Classification

Ba Dung Le, Guanhua Wang, Mehwish Nasim +1

Preventing organizations from Cyber exploits needs timely intelligence about Cyber vulnerabilities and attacks, referred as threats. Cyber threat intelligence can be extracted from…

cs.SI2018

Analysing Emergent Users' Text Messages Data and Exploring its Benefits

Anas Bilal, Aimal Rextin, Ahmad Kakakhail +1

While users in the developed world can choose to adopt the technology that suits their needs, the emergent users cannot afford this luxury, hence, they adapt themselves to the tech…

cs.CY2018

Pachinko Prediction: A Bayesian method for event prediction from social media data

Jonathan Tuke, Andrew Nguyen, Mehwish Nasim +4

The combination of large open data sources with machine learning approaches presents a potentially powerful way to predict events such as protest or social unrest. However, account…

cs.SI2018★ 1 cited

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

Mehwish Nasim, Andrew Nguyen, Nick Lothian +2

Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing r…

cs.SI2015★ 3 cited

On Temporal Regularity in Social Interactions: Predicting Mobile Phone Calls

Mehwish Nasim, Aimal Rextin, Numair Khan +1

In this paper we predict outgoing mobile phone calls using a machine learning approach. We analyze to which extent the activity of mobile phone users is predictable. The premise is…