Characterizing polarization in online vaccine discourse - A large-scale study, 2022
Vaccine hesitancy is currently recognized by the WHO as a major threat to global health. Recently... more Vaccine hesitancy is currently recognized by the WHO as a major threat to global health. Recently, especially during the COVID-19 pandemic, there has been a growing interest in the role of social media in the propagation of false information and fringe narratives regarding vaccination. Using a sample of approximately 60 billion tweets, we conduct a large-scale analysis of the vaccine discourse on Twitter. We use methods from deep learning and transfer learning to estimate the vaccine sentiments expressed in tweets, then categorize individual-level user attitude towards vaccines. Drawing on an interaction graph representing mutual interactions between users, we analyze the interplay between vaccine stances, interaction network, and the information sources shared by users in vaccine-related contexts. We find that strongly anti-vaccine users frequently share content from sources of a commercial nature; typically sources which sell alternative health products for profit. An interesting aspect of this finding is that concerns regarding commercial conflicts of interests are often cited as one of the major factors in vaccine hesitancy. Further, we show that the debate is highly polarized, in the sense that users with similar stances on vaccination interact preferentially with one another. Extending this insight, we provide evidence of an epistemic echo chamber effect, where users are exposed to highly dissimilar sources of vaccine information, depending the vaccination stance of their contacts. Our findings highlight the importance of understanding and addressing vaccine mis-and dis-information in the context in which they are disseminated in social networks.
Quantum Computing with Majorana Fermions Coupled to Quantum Dots
The topic of this bachelor’s thesis is the application of Majorana fermions in quantum computers.... more The topic of this bachelor’s thesis is the application of Majorana fermions in quantum computers. We initially discuss important concepts of quantum information and quantum computation and later derive the non-abelian statistics of Majoranas realized as vortices in p-wave superconductors. We consider in details the effects leading to these statistics, including flux quantization and the Aharonov-Bohm effect. We also consider a model for physical implementation of quantum bits using Majoranas coupled to quantum dots, which allows the construction of certain quantum gates. Based on our discussion of quantum information theory, we identify the weaknesses of the model, and examine in detail the possibility of improving it. Monika Kovacic 15/1-90 Bjarke Mønsted 22/3-87
The scope of the research presented in this thesis is fairly broad, touching upon areas like soci... more The scope of the research presented in this thesis is fairly broad, touching upon areas like social network analysis, information and behavioral contagion models, and social data science. As the world becomes increasingly digitalized, it becomes crucial to better understand interactions in the digital sphere. The proliferation of online communication allows interaction between people with more diverse backgrounds more varied areas of knowledge than before. However while increased digitalization carries the potential for an explosion of diversity in communication, that is far from the only conceivable consequence. The same diversity offers anyone online a multitude of different communities and sources of information. This carries the risk of individuals choosing disproportionately often to connect to individuals that are similar to themselves, and to consume exclusively information which supports their preexisting convictions. Fortunately fields such as network science, computational...
Vaccination rates are decreasing in many areas of the world, and outbreaks of preventable disease... more Vaccination rates are decreasing in many areas of the world, and outbreaks of preventable diseases tend to follow in areas with particular low rates. Much research has been devoted to improving our understanding of the motivations behind vaccination decisions and the effects of various types of information offered to skeptics, no large-scale study of the structure of online vaccination discourse have been conducted. Here, we offer an approach to quantitatively study the vaccine discourse in an online system, exemplified by Twitter. We use train a deep neural network to predict tweet vaccine sentiments, surpassing state-of-the-art performance, attaining two-class accuracy of $90.4\%$, and a three-class F1 of $0.762$. We identify profiles which consistently produce strongly anti- and pro-vaccine content. We find that strongly anti-vaccine profiles primarily post links to Youtube, and commercial sites that make money on selling alternative health products, representing a conflict of in...
With news pushed to smart phones in real time and social media reactions spreading across the glo... more With news pushed to smart phones in real time and social media reactions spreading across the globe in seconds, the public discussion can appear accelerated and temporally fragmented. In longitudinal datasets across various domains, covering multiple decades, we find increasing gradients and shortened periods in the trajectories of how cultural items receive collective attention. Is this the inevitable conclusion of the way information is disseminated and consumed? Our findings support this hypothesis. Using a simple mathematical model of topics competing for finite collective attention, we are able to explain the empirical data remarkably well. Our modeling suggests that the accelerating ups and downs of popular content are driven by increasing production and consumption of content, resulting in a more rapid exhaustion of limited attention resources. In the interplay with competition for novelty, this causes growing turnover rates and individual topics receiving shorter intervals o...
Basic personality traits are believed to be expressed in, and predictable from, smart phone data.... more Basic personality traits are believed to be expressed in, and predictable from, smart phone data. We investigate the extent of this predictability using data (n = 636) from the Copenhagen Network Study, which to our knowledge is the most extensive study concerning smartphone usage and personality traits. Based on phone usage patterns, earlier studies have reported surprisingly high predictability of all Big Five personality traits. We predict personality trait tertiles (low, medum, high) from a set of behavioral variables extracted from the data, and find that only extraversion can be predicted significantly better (35.6%) than by a null model. Finally, we show that the higher predictabilities in the literature are likely due to overfitting on small datasets.
It has recently become possible to study the dynamics of information diffusion in techno-social s... more It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.
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