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

Trolling Twitter

Political polarization is a defining feature of the contemporary American political landscape. While there is little doubt that elite polarization levels have risen dramatically in recent decades, there is some debate over the existence of a corresponding rise in mass polarization. Recent scholarship on mass polarization has cited evidence related to citizens’ positions on public policy issues, party sorting, and geographic polarization; however, questions remain as to the nature and extent of mass polarization in online spaces. Specifically, more needs to be known regarding how expressions of elite polarization influence the formation of polarized communities within social media. This dissertation examines the question: Does elite polarization contribute to mass polarization in social media? This question is approached in three stages. First, this dissertation tests whether or not a causal link between elite and mass polarization strengthens with temporal proximity to highly politicized and potentially polarizing events over the span of the 2016 Republican presidential primary. Second, this dissertation examines the instant effects of elite polarization by examining a minute-by-minute live stream of reactions on Twitter during the first 2016 presidential debate. Third, this dissertation tests a contemporary theory which claims a presidential candidate’s patterns of speech sows the seeds of mass polarization in the form of resentment, fear, or incivility. This dissertation also employs the use of network analysis tools to measure the extent to which polarized communities form on social media in response to elite cues. The nature of such causal relationships provides insight into the influence polarizing messages by elites may have on mass polarization while taking into consideration the unique characteristics of the social media communications environment. In doing so, this dissertation offers a blueprint for future researchers who seek to better understand how networked technologies shape human interactions

To what extent do university students in Saudi Arabia find a social media tool (Twitter) useful in their respective learning environments?

This research examines and evaluates students’ perceptions of the effectiveness of social media (Twitter) in their learning environment. The investigation was conducted in a university located in Saudi Arabia. Twitter was integrated into the learning environment to precisely examine the potential of social media in education. The study attempts to address the research topic from several aspects: examining the challenges that students face during the integration, evaluating and discovering possible pitfalls associated with social media and education, evaluating the positive capacity of Twitter in learning and as a pedagogical tool, and investigating students’ educational engagement through the social media platform. A mixed-methods approach was conducted for data collection, including quantitative (online questionnaires) and qualitative (face-to-face interviews) methods. The obtained data were analysed quantitatively using descriptive and inferential statistics analysis, including exploratory factor analysis using the Statistical Package for Social Sciences (SPSS), and qualitatively through thematic analysis. The analysis of students’ perspectives revealed that they had positive Twitter experiences, and they expressed that utilising Twitter can facilitate and improve their educational activities, including knowledge sharing, communication, interaction and collaboration, questioning, and finding answers. The analysis also revealed that they believed that Twitter is a supportive tool that ‘often’ increases students’ engagement in educational activities, such as being involved in discussions and asking questions. In relation to some of the students’ preferences, it was revealed that there were few concerns related to privacy. In addition, interviews revealed there was a small amount of non-academic interaction via Twitter as well as difficulties in accessing the platform, including the internet services. The study highlights the process of integrating Twitter in learning, as this could increase its positive impact; the process includes the provision of a general induction, an explanation of the purpose, and how students might achieve their goal. The study is important as it provides an overall picture of the use of social media in higher education. It also assists in the development of integrating social media, particularly Twitter, in general academic practice or in the learning environment

Determining Political Inclination in Tweets Using Transfer Learning

Last few years have seen tremendous development in neural language modeling for transfer learning and downstream applications. In this research, I used Howard and Ruder’s Universal Language Model Fine Tuning (ULMFiT) pipeline to develop a classifier that can determine whether a tweet is politically left leaning or right leaning by likening the content to tweets posted by @TheDemocrats or @GOP accounts on Twitter. We achieved 87.7% accuracy in predicting political ideological inclination