A scoping review on the use of natural language processing in research on political polarization: trends and research prospects

As part of the “text-as-data” movement, Natural Language Processing (NLP) provides a computational way to examine political polarization. We conducted a methodological scoping review of studies published since 2010 ( n = 154) to clarify how NLP research has conceptualized and measured political polarization, and to characterize the degree of integration of the two different research paradigms that meet in this research area. We identified biases toward US context (59%), Twitter data (43%) and machine learning approach (33%). Research covers different layers of the political public sphere (politicians, experts, media, or the lay public), however, very few studies involved more than one layer. Results indicate that only a few studies made use of domain knowledge and a high proportion of the studies were not interdisciplinary. Those studies that made efforts to interpret the results demonstrated that the characteristics of political texts depend not only on the political position of their authors, but also on other often-overlooked factors. Ignoring these factors may lead to overly optimistic performance measures. Also, spurious results may be obtained when causal relations are inferred from textual data. Our paper provides arguments for the integration of explanatory and predictive modeling paradigms, and for a more interdisciplinary approach to polarization research

4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595

100 key questions to guide hydropeaking research

As the share of renewable energy grows worldwide, flexible energy production from peak-operating hydropower and the phenomenon of hydropeaking have received increasing attention. In this study, we collected open research questions from 220 experts in river science, practice, and policy across the globe using an online survey available in six languages related to hydropeaking. We used a systematic method of determining expert consensus (Delphi method) to identify 100 high-priority questions related to the following thematic fields: (a) hydrology, (b) physico-chemical properties of water, (c) river morphology and sediment dynamics, (d) ecology and biology, (e) socio-economic topics, (f) energy markets, (g) policy and regulation, and (h) management and mitigation measures. The consensus list of high-priority questions shall inform and guide researchers in focusing their efforts to foster a better science-policy interface, thereby improving the sustainability of peak-operating hydropower in a variety of settings. We find that there is already a strong understanding of the ecological impact of hydropeaking and efficient mitigation techniques to support sustainable hydropower. Yet, a disconnect remains in its policy and management implementation.publishedVersio

Disentangled learning of stance and aspect topics for vaccine attitude detection in social media

Building models to detect vaccine attitudes on social media is challenging because of the composite, often intricate aspects involved, and the limited availability of annotated data. Existing approaches have relied heavily on supervised training that requires abundant annotations and pre-defined aspect categories. Instead, with the aim of leveraging the large amount of unannotated data now available on vaccination, we propose a novel semi-supervised approach for vaccine attitude detection, called VADet. A variational autoencoding architecture based on language models is employed to learn from unlabelled data the topical information of the domain. Then, the model is fine-tuned with a few manually annotated examples of user attitudes. We validate the effectiveness of VADet on our annotated data and also on an existing vaccination corpus annotated with opinions on vaccines. Our results show that VADet is able to learn disentangled stance and aspect topics, and outperforms existing aspect-based sentiment analysis models on both stance detection and tweet clustering

Mapping (Dis-)Information Flow about the MH17 Plane Crash

Digital media enables not only fast sharing of information, but also disinformation. One prominent case of an event leading to circulation of disinformation on social media is the MH17 plane crash. Studies analysing the spread of information about this event on Twitter have focused on small, manually annotated datasets, or used proxys for data annotation. In this work, we examine to what extent text classifiers can be used to label data for subsequent content analysis, in particular we focus on predicting pro-Russian and pro-Ukrainian Twitter content related to the MH17 plane crash. Even though we find that a neural classifier improves over a hashtag based baseline, labeling pro-Russian and pro-Ukrainian content with high precision remains a challenging problem. We provide an error analysis underlining the difficulty of the task and identify factors that might help improve classification in future work. Finally, we show how the classifier can facilitate the annotation task for human annotators

Collective attention in online social networks

Social media is an ever-present tool in modern society, and its widespread usage positions it as a valuable source of insights into society at large. The study of collective attention in particular is one application that benefits from the scale of social media data. In this thesis we will investigate how collective attention manifests on social media and how it can be understood. We approach this challenge from several perspectives across network and data science. We first focus on a period of increased media attention to climate change to see how robust the previously observed polarised structures are under a collective attention event. Our experiments will show that while the level of engagement with the climate change debate increases, there is little disruption to the existing polarised structure in the communication network. Understanding the climate media debate requires addressing a methodological concern about the most effective method for weighting bipartite network projections with respect to the accuracy of community detection. We test seven weighting schemes on constructed networks with known community structure and then use the preferred methodology we identify to study collective attention in the climate change debate on Twitter. Following on from this, we will investigate how collective attention changes over the course of a single event over a longer period, namely the COVID-19 pandemic. We measure how the disruption to in-person social interactions as a consequence of attempts to limit the spread of COVID-19 in England and Wales have affected social interaction patterns as they appear on Twitter. Using a dataset of tweets with location tags, we will see how the spatial attention to locations and collective attention to discussion topics are affected by social distancing and population movement restrictions in different stages of the pandemic. Finally we present a new analysis framework for collective attention events that allows direct comparisons across different time and volume scales, such as those seen in the climate change and COVID-19 experiments. We demonstrate that this approach performs better than traditional approaches that rely on binning the timeseries at certain resolutions and comment on the mechanistic properties highlighted by our new methodology.Engineering and Physical Sciences Research Council (EPSRC

Social media mining to investigate the impacts of the COVID-19 pandemic

The COVID-19 pandemic created a global crisis with devastating social and economic impacts. Firstly, public health measures for COVID-19, such as social distancing affected how we work and study. Secondly, this crisis caused mobility restrictions and shutdowns that impacted our economy. In this thesis, we aim to obtain a better understanding of how these socio-economic impacts have affected people. We, therefore, choose one problem from each of these two areas of impact for further study. The social distancing mandates shifted working environments and education online. Due to cheating being more prevalent in online education, serious issues may arise during the pandemic when classes and examinations are online. In order to understand these issues and their impacts on college students, we ask: how do college students feel about online cheating? Fuel consumption and carbon emissions declined due to mobility restrictions and economic shutdowns. As a result, air quality improved. Economic shutdowns, however, impacted countries' ability to fight climate change. We are interested in understanding how people's perspectives have changed due to both the positive and negative effects of the pandemic on climate change. To do so, we ask: What is the public's attitude towards climate action during the COVID-19 recovery and beyond? We answer these questions by analyzing discussions on Twitter and Reddit social media platforms. These online social media platforms are considered essential tools for reflecting and forecasting public opinion on a wide range of topics. Therefore, we answer our questions by mining text messages that were posted during the COVID-19 crisis. We begin by collecting the necessary posts and comments. We then prepare the documents for text mining by using standard pre-processing techniques. As a result, we are able to construct an understanding of the discussions by using these methods. While investigating the discussions about academic dishonesty on Reddit, we found more discussions related to cheating in 2020 than in 2019. The topics have expanded from plagiarism in programming assignments to online assessments in general. Topic modelling of the Fall 2020 discussions revealed three concerns raised by students: that cheating inflates grades and forces instructors to increase the difficulty of assessments; that witnessing cheating go unpunished is demotivating; and that academic integrity policies are not always communicated clearly. Investigating the discussions about the climate change and the pandemic on Twitter revealed that most tweets support climate action and point out lessons learned during the pandemic response that can shape future climate policy, although skeptics continue to have a presence. Additionally, concerns arise in the context of climate action during the pandemic, such as mitigating the risk of COVID-19 transmission on public transit

Discovering and Mitigating Social Data Bias

abstract: Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago. Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This means that the results of many studies are limited by the quality of data they collect. The finding that social media data is biased inspires the main challenge addressed by this thesis. I introduce three sets of methodologies to correct for bias. First, I design methods to deal with data collection bias. I offer a methodology which can find bias within a social media dataset. This methodology works by comparing the collected data with other sources to find bias in a stream. The dissertation also outlines a data collection strategy which minimizes the amount of bias that will appear in a given dataset. It introduces a crawling strategy which mitigates the amount of bias in the resulting dataset. Second, I introduce a methodology to identify bots and shills within a social media dataset. This directly addresses the concern that the users of a social media site are not representative. Applying these methodologies allows the population under study on a social media site to better match that of the real world. Finally, the dissertation discusses perceptual biases, explains how they affect analysis, and introduces computational approaches to mitigate them. The results of the dissertation allow for the discovery and removal of different levels of bias within a social media dataset. This has important implications for social media mining, namely that the behavioral patterns and insights extracted from social media will be more representative of the populations under study.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Trends in European Climate Change Perception: Where the Effects of Climate Change go unnoticed

Climate change threatens global impacts in a variety of domains that must be limited by adaptation and mitigation measures. The successful implementation of such policies can strongly benefit from the general public’s cooperation motivated by their own risk perceptions. Public participation can be promoted by tailoring policies to the populations they affect, which in turn results in the need for a deeper understanding of how different communities interact with the issue of climate change. Social media platforms such as the microblogging service Twitter have opened unprecedented opportunities for research on public perception in recent years, offering a continuous stream of user-generated data. Simultaneously, they represent a crucial discursive space in which members of the public develop and discuss their opinions and concerns about climate change. Subsequently, this thesis gains insight into the characteristics of public reactions to individual climate change effects and processes by investing corresponding corpora of tweets spanning a decade. For seven western European countries, the spatial, temporal, and thematic reaction patterns are determined with a further assessment of the drivers behind each finding. Tweets are collected, classified, georeferenced, and clustered using a selection of Geographic Information Retrieval as well as Natural Language Processing methods before being analysed regarding thematic trends in their content, spatial distributions and influences of environmental factors, as well temporal distributions and impacts of real-world events. The findings illustrate diverse climate change perceptions that vary across spatial, temporal, and thematic dimensions. Communities tend to focus more on issues relevant to their local or national environment, leading populations to develop a certain degree of specialisation for these aspects of climate change. This typically coincides with a substantially more domestic discourse on the subject and a decrease in interest for corresponding international events. In a similar sense, the tangibility of an event drives the magnitude of reactions. However, while more tangible events are more frequently recognised and discussed, less tangible events tend to be more frequently attributed to climate change as the public shifts their focus from immediate impacts on the personal scale to impacts on the global scale. Additionally, traditional news media are shown to retain a high level of control over science communication and the climate change discourse on Twitter, likely influencing the public’s perspective on global warming. Individual real-world events such as major climate conferences and scientific releases only occasionally elicit strong public reactions when they are topically related to an event type, whereas global protests can lead to significant discussion across various event types. Inversely, global crises such as the COVID-19 pandemic significantly reduce public concern about climate change processes

Modelling reef fish connectivity: Investigating the biological mechanisms that influence connectivity patterns

Biophysical dispersal models (BDMs) — a hydrodynamic model coupled with a biological model — lend themselves to inferring potential connectivity patterns, as experimental studies using current methods are inherently difficult over extended spatial and temporal scales. This thesis explored the biological processes that affect the connectivity patterns of ichthyoplankton using four related data chapters. The first data chapter, a meta-analysis of connectivity studies using BDMs, investigated both trends and consequences of modelling choices on derived connectivity patterns. The results of this meta-analysis provide a useful framework on parameter choice for future consideration of connectivity studies. The second data chapter is an experiment measuring the ontogenetic vertical migration of reef fish off the coast of south-eastern Australia. The ichthyoplankton sampled demonstrated deeper migration with both increasing ontogenetic stage and length. The third data chapter is a theoretical modelling chapter that investigated the effect of different swimming and migration behaviours and differences in the parameterisation and implementation of vertical migration in a BDM. The fourth data chapter synthesised the results from the previous three data chapters and explored the predicted connectivity patterns of an endemic and threatened Australian reef fish, the black cod (Epinephelus daemelii) using a BDM. E. daemelii larvae showed strong connections to both the natal and proximate regions. Within the context of the current marine protected area (MPA) network of NSW, strong settlement regions had only moderate or no no-take areas. The results of this thesis increase our understanding of the influence of behaviour on the dispersal patterns of marine larvae along the east Australian coast