16 citations · 40 across the 7 of their papers we have counts for
7 papers · 1 filter
SMERC: Social media event response clustering using textual and temporal information
Peter Mathews, Caitlin Gray, Lewis Mitchell +2
Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a pro…
The one comparing narrative social network extraction techniques
Michelle Edwards, Lewis Mitchell, Jonathan Tuke +1
Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consu…
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…
Enhancing keyword correlation for event detection in social networks using SVD and k-means: Twitter case study
Ahmad Hany Hossny, Terry Moschou, Grant Osborne +2
Extracting textual features from tweets is a challenging process due to the noisy nature of the content and the weak signal of most of the words used. In this paper, we propose usi…
Generating Connected Random Graphs
Caitlin Gray, Lewis Mitchell, Matthew Roughan
Sampling random graphs is essential in many applications, and often algorithms use Markov chain Monte Carlo methods to sample uniformly from the space of graphs. However, often the…
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…