Job Posting Birmingham

Below are the links to job adverts, job descriptions and application forms for the following posts at the University of Birmingham:

Research Fellow in Machine Learning in the Space Inferential Models

Research Fellow – Turing –

Should you have any queries about the roles, please email the following:

WSTnet Student Profile: Simon Jonsson

Simon is completing his second year at the Web Science Institute (WSI) at Southampton where he is working towards a PhD in Web Science on “Increasing engagement and learning performance in educational apps”.

We spoke to Simon about his interest in enhanced learning techniques and how he hopes to contribute towards improved learning experiences at a time when many are relying so heavily on remote and on-line learning approaches. 

Q. Simon tell us about your research topic

A. Well the research is concerned with the design of approaches/elements which increase both the enjoyment and efficiency of the e-learning experience through the creation of a state of “flow”. When we are in state of flow we are typically less distracted, more receptive to the content/material and typically report enjoying the experience more than when not in flow.

Q. Are you looking to make learning more enagaging/enjoyable through gamification?

A. Well thats just the point. Gamification appears to be partly distracting: drawing attention to another aspect of the experience rather than focussing on the experience itself so users are enjoying the gamerather than enjoying the learning. This can be reflected in the speed and depth of learning and ties the sucess of the learning to the sucess of the gamified elements.

Q. How are you testing your approach?

A. Whilst the final approach can be expanded to many factors and combinations I have started in the pilot phase with a simple A/B matrix in which four groups are exposed (or not) to a learning design feature giving rise to AB, A’B, AB’, A’B’ (A and B, NOT A and B, A and NOT B, NOT A and NOT B).

We then test the participants for the length and regularity of engagement and adminisiter tests around the learning and retention. In this way we have a simple model to evaluate the impact of a single feature or feature combination on engagement and performance.

The first phase has used local volunteers whilst the main study will involve releasing the app to the Google Play store to recuit a much larger number of participants.

Q. Have any of the results or insights surprised you so far?

A. One thing which did surprise me was the existence of a large body of education/learning theory which does not seem to be used or implemented in practice. For example, spaced learning was a theory put forward by Ebbinghaus in 1885 and which even today is not always implemented in learning apps.

Q. How will your research continue?

A. Once we have results from a wider Google Play experiment (based on Spanish Language learning) we will review applying any insights to other areas of learning and app design.


Q.  Have any aspects of Web Science been useful here?

A. The most significant aspect of Web Science has been the interdisciplinarity – the opportunity to work with aspects of education coming from a background in Maths and Psychology. That interdisciplinarity is so important.

Good luck with the rest of your research and thanks for taking part.

WSTnet Student Profile: Amir Javed

Amir, thanks for agreeing to be interviewed. Can you tell us where you are based and what your main research interests are..
I’m based at Cardiff University and my focus is on a particular type of cyber attack called “drive-by downloads” which are typically combined with social media posts on platforms like Twitter
How are these different from typical viruses or other attacks?
A Drive-By download involves one or more malicious scripts which execute without  requiring the user to specifically download or click a suspicious object – the act of visiting the URL is enough to infect the host machine.
How does the social media element play out here?
Social media platforms often host/distribute click-bait in the form of a message which provokes interest and/or an emotional reaction in the user and encourages them to follow a (typically shortened and hence unrecognisable) URL to respond to it.
So what angle is your research taking on this?
Rather than attempting to look at the vast range of topic/ideas that might prompt a user to follow click bait we are looking at the types of stimulus like Events (e.g. Sports matches) which have a specific date/time around which the click-bait and URLs may be focused. If we can work to specific events as a focus we may be able to analyse patterns of (social) attack discovering which users and sites are involved, how these are structured in terms of topics and social vectors and work to dampen the scale of the retweet network which is generated and ultimately predict where attacks may happen and find ways to inoculate against them.
What has your research shown so far?
We analysed tweets from several events and  categorised them as malicious or benign and within the malicious group the type of emotion (we discovered eight) that the tweets were trying to elicit to get a click-through or retweet. We found that fear-provoking tweets were most likely to be retweeted and persisted longer than other emotions.
We then analysed the effect of the different drive-by download scripts on the machine state of a test machine in order to subject these to a machine learning process. We were able to identify activities/patterns that the scripts attempted to execute on visiting the infected site and attempted to match/recognise these patterns within a short window as the script starts to execute. Success here would facilitate developing a “kill-switch” protocol that could potentially save the machine/network from infection. Our current model is identifying malicious URLs about 86% of the time which is very promising.
Where are you going next with the work?
We are keen to build a better profile of the influential users, the common topics, the infected sites (though these shift) and to be able to create an efficient and scalable method to scan for attacks/attackers using various factors (e.g. tweets from users created only hours/minutes before) such that we can weaken/disrupt the scale of the attack and ultimately inoculate users through an efficient combination of blacklisting and real-time detection processes.
How useful has the Web Science perspective been on this work?
Traditionally Cybersecurity has focused on machine impacts and technical networks but whilst the idea of the social exploit is far from new, social media enables social attacks and trust exploits on a scale we’ve never seen before and so understanding how social networks function and how they can be managed/influenced for better security is vital.
Where would you like to see Web Science go next as a discipline?
With a growing war between hackers and cybersecurity specialists there is not only a need to understand each specific attack in terms of machine learning/pattern matching but also to understand the broader social process of deliberate deception (feinting) in order to avoid detection. How do we filter for “noise”, fake data and other methods designed to fool automated detection and make our model resilient against such noise.
Amir has submitted his Thesis at Cardiff University and is shortly to be appointed a lecturer at Cardiff
Here are link to two of Amir’s related papers

Meet the Students: Ipek Baris

Welcome to our new series on PhD student profiles. In this series, we’ll be visiting PhD students across the globe and showcasing their unique research in Web Science.

In our profile, we’d like you to meet Ipek Baris – a first year PhD student at WeST University of Koblenz-Landau. Ipek’s research is sponsored by the Co-Inform project of the European Union. The project aims to research and develop tools and methodologies for combating online misinformation.

We asked Ipek how she was developing the topic further. She explained that “my research focuses on understanding misinformation diffusion by investigating user interactions in online media. I aim to use indicators of misinformation as features of neural network based architectures for early detection of misinformation, and seek to know whether the answers for these indicators differ among different cultures, nations and topics such as politics or health”.

Ipek continues “as initial work, I investigated these indicators in an English rumour dataset and participated in RumourEval 2019 with a neural network-based architecture, which was ranked 2nd on the task of assessing the veracity of rumour”. You can read more about this work in an upcoming paper titled “Convolving ELMo Against Rumours” in the proceedings of SemEval 2019, an international workshop on semantic evaluation.

“I chose Web Science at WeST for pursuing my PhD on misinformation because Web Science has close interactions with computer science and other disciplines, such as social science, political science and more”, she says. “At WeST, I have the opportunity to work for my PhD with researchers from different backgrounds and disciplines. This opportunity helps me to progress faster and to be more productive in my research”.

Ipek looks forward to developing her research further at WeST, and hopes to publish more research about combating misinformation online.