This systematic review synthesizes research on echo chambers and filter bubbles to explore the reasons behind dissent regarding their existence, antecedents, and effects. It provides a taxonomy of conceptualizations and operationalizations, analyzing how measurement approaches and contextual factors influence outcomes. The review of 129 studies identifies variations in...
Human mobility and other social activity patterns influence various aspects of society such as urban planning, traffic predictions, crisis resilience, and epidemic prevention. The behaviour of individuals, like their communication frequencies and movements, are shaped by societal and socio-economic factors. In addition, the differences in the geolocation of people as well as...
Much work has been done to understand complex crowd dynamics and self-organizing behaviors in high-density crowd situations. But most approaches for modelling pedestrian dynamics in emergencies require complex computations, making it difficult to capture multiple individual behaviors within a single model. This paper describes an agent-based model (ABM) that incorporates Bayesian...
Analyzing policymaking using archival evidence is common in qualitative studies, but doing so using text-as-data methods is challenging because commonly used techniques favor the identification of policy areas rather than actions. This article employs natural language processing to evaluate how UK governments describe their actions, using the full sample of official (command...
Due to their increasing popularity, researchers and health professionals are actively utilizing social media networks as valuable tools to recognize linguistic patterns associated with mental health. In this research, our aim was to better understand to what extent the Beck Depression Inventory (BDI) could undergo automated screening based on users’ social media feeds. To this...
The rise of social media has led to new studies on collective attention in specific events such as elections and sports. In the context of collective attention, phenomena such as rapid increases in the number of posts and the sentiment of the content have been extensively studied. However, microscopic details, like who is participating and the specific words used in posts, are...
Hansard, or the official verbatim transcripts of parliamentary debates, contains rich information for analysing discourse and political activities on a wide range of policy issues. A fundamental task in political text analysis is to predict whether a speaker takes on a positive or negative view about a debate topic. Unlike social media data, which has received extensive attention...
Evaluating bias in Large Language Models (LLMs) has become a pivotal issue in current Artificial Intelligence (AI) research due to their significant impact on societal dynamics. Recognizing political bias in LLMs is particularly important as they approach performative prediction, influencing societal behavior and political events, such as the upcoming European Parliament...
This paper investigates the dissemination of COVID-19 misinformation on Twitter within the context of the Norwegian media landscape, characterized by high levels of trust in the media, yet experiencing an increasing influence of alternative news sources. Using a semi-supervised self-training approach for text classification, a dataset of 426,262 tweets is analyzed, identifying...
Global crises, such as wars or the COVID-19 pandemic, underscore the need for real-time economic monitoring. Traditional economic indicators often fall short, prompting the exploration of alternative data sources, including online and social media content. This study examines the relationship between media sentiment in press articles and traditional economic indicators: the...
This study examines U.S. public sentiment toward solar energy from 2013 to 2022 by analyzing 8 million social media posts using RoBERTa, a transformer-based deep learning algorithm. While sentiment has been generally positive, it has declined since 2016, driven by increasing negativity. The analysis reveals significant and widening regional disparities, with Republican-leaning...
The article examines news trajectories used by individuals to access mainstream journalistic media in Germany and German-speaking Switzerland during the first peak of COVID-19 in Europe. It discusses the role of individual characteristics related to sociodemographics, political, and media attitudes in predisposition towards specific modes of news access. For this aim, it combines...
Teams comprised of exceptional individuals are often thought to excel in performance, but the reality is that even such teams can face challenges in group environments. Problems like excessive coordination and declining motivation can undermine a team’s productivity. This study seeks to improve team cooperation through task allocation while addressing individual needs. However...
The Covid-19 pandemic brought unprecedented changes to business ownership in the UK which affects a generation of entrepreneurs and their employees. Nonetheless, the impact remains poorly understood. This is because research on capital accumulation has typically lacked high-quality, individualized, population-level data. We overcome these barriers to examine who benefits from...
Incels (involuntary celibates) are part of a broader misogynistic culture known as the manosphere. Some communities within the manosphere, including incels, promote gender-based violence through misogynistic rhetoric and ideology. Incels are men who struggle to form romantic relationships and thus seek solace in online forums to find a sense of purpose and community. The...
The growth of social media recently has made individual investors more reliant on online media for information. This trend significantly affects investor behavior and information disparity. For instance, social media can lead to the phenomenon of “meme stocks,
The current global cyber governance model is dominated by Western liberal norms and multi-stakeholder values. Dissatisfied with the status quo, some developing countries like China embrace another governance concept called cyber sovereignty, which advocates more state control. Meanwhile, AI development further enlarges cyberspace’s national security threats, but an international...
Substance use poses a significant public health challenge worldwide, including in Finland. This study seeks to predict patterns of substance use, aiming to identify the driving factors behind these trends using artificial intelligence techniques. This research utilizes data from the 2022 Finnish National Drug Survey, comprising 3,857 participants, to develop predictive models...
In financial markets, the sentiment expressed in news articles plays a pivotal role in interpreting and forecasting market trends, which also holds true for the task of financial news summarization (FNS). Leveraging AI models to analyze social science data, this paper employs financial sentiment to improve FNS effectiveness by introducing a novel method that combines the...
This paper studies the performance of open-source Large Language Models (LLMs) in text classification tasks typical for political science research. By examining tasks like stance, topic, and relevance classification, we aim to guide scholars in making informed decisions about their use of LLMs for text analysis and to establish a baseline performance benchmark that demonstrates...
This article presents a comparative analysis of the potential of two large language model (LLM)-based chatbots—ChatGPT and Bing Chat (recently rebranded to Microsoft Copilot)—to detect veracity of political information. We use AI auditing methodology to investigate how chatbots evaluate true, false, and borderline statements on five topics: COVID-19, Russian aggression against...
In this article, we examine the ideological trajectories of Facebook groups discussing climate change issues in Sweden, with a particular focus on groups expressing climate change denial beliefs. Using textual data spanning nine years, we construct an ideological space through text embeddings and apply trajectory analysis to map the ideological movement of these groups, exploring...
Previous studies have found stable individual differences in decision-making under escalation situations. Conventionally, the differences have been attributed to dispositional factors. In this paper, we offer multi-level adaptive learning as an alternative, positing that stable individual differences can develop (a) from an equal starting point at which there are no individual...
A network analysis approach, complemented by machine learning (ML) techniques, is applied to analyse the factors influencing Bilateral Investment Treaties (BITs) at the country level. Using the Electronic Database of Investment Treaties, BITs with health safeguards from 167 countries were charted, resulting in 534 connections with countries as nodes and their BITs as edges...
Artificially intelligent technologies have become a common topic in our everyday discussions where arguments about the subject can take different forms from cognitive reasoning to emotional expressions. Utilizing persuasion theories and research on the appeal of content characteristics as the theoretical approach to examine affective–cognitive language, we investigated social...