Simon D. Angus

Michael Kitchener, Nandini Anantharama, Simon D. Angus et al.

This paper proposes a new method to predict individual political ideology from digital footprints on one of the world's largest online discussion forum. We compiled a unique data set from the online discussion forum reddit that contains information on the political ideology of around 91,000 users as well as records of their comment frequency and the comments' text corpus in over 190,000 different subforums of interest. Applying a set of statistical learning approaches, we show that information about activity in non-political discussion forums alone, can very accurately predict a user's political ideology. Depending on the model, we are able to predict the economic dimension of ideology with an accuracy of up to 90.63% and the social dimension with and accuracy of up to 82.02%. In comparison, using the textual features from actual comments does not improve predictive accuracy. Our paper highlights the importance of revealed digital behaviour to complement stated preferences from digital communication when analysing human preferences and behaviour using online data.

Songhai Fan, Simon Angus, Tim Dwyer et al.

Exponential growth in the quantity of digital news, social media, and other textual sources makes it difficult for humans to keep up with rapidly evolving narratives about world events. Various visualisation techniques have been touted to help people to understand such discourse by exposing relationships between texts (such as news articles) as topics and themes evolve over time. Arguably, the understandability of such visualisations hinges on the assumption that people will be able to easily interpret the relationships in such visual network structures. To test this assumption, we begin by defining an abstract model of time-dependent text visualisation based on directed graph structures. From this model we distill motifs that capture the set of possible ways that texts can be linked across changes in time. We also develop a controlled synthetic text generation methodology that leverages the power of modern LLMs to create fictional, yet structured sets of time-dependent texts that fit each of our patterns. Therefore, we create a clean user study environment (n=30) for participants to identify patterns that best represent a given set of synthetic articles. We find that it is a challenging task for the user to identify and recover the predefined motif. We analyse qualitative data to map an unexpectedly rich variety of user rationales when divergences from expected interpretation occur. A deeper analysis also points to unexpected complexities inherent in the formation of synthetic datasets with LLMs that undermine the study control in some cases. Furthermore, analysis of individual decision-making in our study hints at a future where text discourse visualisation may need to dispense with a one-size-fits-all approach and, instead, should be more adaptable to the specific user who is exploring the visualisation in front of them.

Simon D Angus, Lachlan O'Neill

Detecting issue framing in text - how different perspectives approach the same topic - is valuable for social science and policy analysis, yet challenging for automated methods due to subtle linguistic differences. We introduce `paired completion', a novel approach using LLM next-token log probabilities to detect contrasting frames using minimal examples. Through extensive evaluation across synthetic datasets and a human-labeled corpus, we demonstrate that paired completion is a cost-efficient, low-bias alternative to both prompt-based and embedding-based methods, offering a scalable solution for analyzing issue framing in large text collections, especially suited to low-resource settings.

Lachlan O'Neill, Simon Angus, Satya Borgohain et al.

As the discipline has evolved, research in machine learning has been focused more and more on creating more powerful neural networks, without regard for the interpretability of these networks. Such "black-box models" yield state-of-the-art results, but we cannot understand why they make a particular decision or prediction. Sometimes this is acceptable, but often it is not. We propose a novel architecture, Regression Networks, which combines the power of neural networks with the understandability of regression analysis. While some methods for combining these exist in the literature, our architecture generalizes these approaches by taking interactions into account, offering the power of a dense neural network without forsaking interpretability. We demonstrate that the models exceed the state-of-the-art performance of interpretable models on several benchmark datasets, matching the power of a dense neural network. Finally, we discuss how these techniques can be generalized to other neural architectures, such as convolutional and recurrent neural networks.

Klaus Ackermann, Simon D. Angus, Paul A. Raschky

Alternative data is increasingly adapted to predict human and economic behaviour. This paper introduces a new type of alternative data by re-conceptualising the internet as a data-driven insights platform at global scale. Using data from a unique internet activity and location dataset drawn from over 1.5 trillion observations of end-user internet connections, we construct a functional dataset covering over 1,600 cities during a 7 year period with temporal resolution of just 15min. To predict accurate temporal patterns of sleep and work activity from this data-set, we develop a new technique, Segmented Functional Classification Analysis (SFCA), and compare its performance to a wide array of linear, functional, and classification methods. To confirm the wider applicability of SFCA, in a second application we predict sleep and work activity using SFCA from US city-wide electricity demand functional data. Across both problems, SFCA is shown to out-perform current methods.

Klaus Ackermann, Simon D Angus, Paul A Raschky

With the large-scale penetration of the internet, for the first time, humanity has become linked by a single, open, communications platform. Harnessing this fact, we report insights arising from a unified internet activity and location dataset of an unparalleled scope and accuracy drawn from over a trillion (1.5$\times 10^{12}$) observations of end-user internet connections, with temporal resolution of just 15min over 2006-2012. We first apply this dataset to the expansion of the internet itself over 1,647 urban agglomerations globally. We find that unique IP per capita counts reach saturation at approximately one IP per three people, and take, on average, 16.1 years to achieve; eclipsing the estimated 100- and 60- year saturation times for steam-power and electrification respectively. Next, we use intra-diurnal internet activity features to up-scale traditional over-night sleep observations, producing the first global estimate of over-night sleep duration in 645 cities over 7 years. We find statistically significant variation between continental, national and regional sleep durations including some evidence of global sleep duration convergence. Finally, we estimate the relationship between internet concentration and economic outcomes in 411 OECD regions and find that the internet's expansion is associated with negative or positive productivity gains, depending strongly on sectoral considerations. To our knowledge, our study is the first of its kind to use online/offline activity of the entire internet to infer social science insights, demonstrating the unparalleled potential of the internet as a social data-science platform.