Unveiling the drivers of active participation in social media discourse

The emergence of new public forums in the form of online social media has introduced unprecedented challenges to public discourse, including polarization, misinformation, and the rise of echo chambers. Existing research has extensively examined these topics by focusing on the active actions performed by users, without accounting for the share of individuals who consume content without actively interacting with it. In contrast, this study incorporates passive consumption data to investigate the prevalence of active participation in online discourse. We introduce a metric to quantify the share of active engagement and analyze over 17 million pieces of content linked to a polarized Twitter debate to understand its relationship with several features of online environments, such as echo chambers, coordinated behavior, political bias, and source reliability. Our findings reveal a significant proportion of users who consume content without active interactions, underscoring the importance of considering also passive consumption proxies in the analysis of online debates. Furthermore, we found that increased active participation is primarily correlated with the presence of multimedia content and unreliable news sources, rather than with the ideological stance of the content producer, suggesting that active engagement is independent of echo chambers. Our work highlights the significance of passive consumption proxies for quantifying active engagement, which influences platform feed algorithms and, consequently, the development of online discussions. Moreover, it highlights the factors that may encourage active participation, which can be utilized to design more effective communication campaigns

The healthcare community has a responsibility to highlight the ongoing destruction in Gaza

A month before Mahmoud Abu Nujaila, a doctor who worked with Médecins Sans Frontières at Gaza's Al-Awda hospital, was killed by an Israeli airstrike along with his colleagues, he wrote on a hospital whiteboard: “Whoever stays until the end will tell the story. We did what we could. Remember us.” 1 Doctors working in Gaza have made urgent pleas, saying that they feel abandoned by the world amid renewed Israeli airstrikes.2 More recently, the United Nations reported that several paramedics and rescue workers were killed and buried in a mass grave by Israeli forces in southern Gaza.3 Over 18 months of relentless attacks, thousands have died,4 and the healthcare infrastructure has been almost entirely dismantled.56 Access to vital medical supplies has been systematically restricted, and basic necessities for life—clean water, food, sanitation, and energy—have also been destroyed

Investor Action on Health: A Review

EXECUTIVE SUMMARY 1. Using a system-level approach, we review the different mechanisms through which investors can contribute to improved population health outcomes. Specifically, we highlight how these investor action mechanisms – including corporate engagement, environmental, social and governance (ESG) ratings, board oversight, and policy engagement – have been used by institutional investors (i.e., asset owners, asset managers) to advance 15 priority health issues, ranging from food safety and alcohol harm to air pollution and worker health. 2. We categorize the 15 priority health issues according to their maturity from the perspective of the investment community. Investors are motivated to use the full spectrum of mechanisms to address mature issues (e.g., human rights, tobacco smoking) and these are actively incorporated into investment decisions and stewardship activities, as there is general consensus about the financial materiality of these issues. Progressing issues (e.g., nutrition, access to medicines) are growing in significance within the investment community and involve a diverse yet underused array of mechanisms. Emerging issues (e.g., digital well-being, access to quality housing) have only recently begun to attract some attention from investors who are beginning to recognize potential financial risks associated with these issues. 3. We propose that investors need to recognize the maturity of the issue when deciding which mechanisms to use. By matching the right mechanism to the maturity of the issue, investors are more likely to further advance the relevance of the issue in the broader investment community. Drawing on lessons from other ESG issues, including climate change and diversity, equity and inclusion, we develop a framework that can be applied to investor action on health-related issues. Using this framework allows investors to understand which actions are most likely to be relevant for each issue given its stage of maturity. 4. The complexities associated with population health and the financial system provide challenges and opportunities for investors. We detail five different challenges relating to investor action on health: 1. Issue scope: The meaningful differences between different types of health-related issues means that investor action needs to be designed to fit with the characteristics of each issue. 2. Defining impact: The goals associated with investor action on health will ideally be measurable and attributable to investors’ efforts. 3. Impact time lags: Many of the desired impacts of investor action on health will take time to be implemented so investors will need to identify realistic timeframes and key milestones for different types of outcomes and impacts. 4. Demonstrating financial materiality: Existing financial materiality assessment frameworks place varying emphasis on health-related issues so motivated investors may need to play an educational role to raise the profile of less mature issues. 5. Considering system-level effects: Although the investment system is complex, investors can identify key leverage points in the system to unlock wider support for their efforts. Despite the barriers posed by these challenges, we highlight that investors have opportunities to carefully design their actions to increase their effectiveness when seeking to positively contribute to population health

Byron’s Manfred (1817) and Tragedy in the ‘Mental Theatre’

This journal article presents a new interpretation of Byron’s work by setting out Byron’s approach to tragedy. Byron was a prolific tragedian, completing six tragic plays, but he insisted that his plays were closet dramas, to be experienced in the reader’s ‘Mental Theatre’, and not to be performed. While Byron’s attitude has often been dismissed by critics, this article takes his insistence on the reading of his plays as the starting point for understanding what Byron believed tragic drama should achieve and what it is for. Reading Byron’s preference for reading in the context of contemporary theatrical practices, the article contends that Byron’s preference for tragedy-as-read is rooted in his belief in the power of the imagination, and presents a vision of tragedy as something individual and private. It then provides a new reading of Byron’s play Manfred (1817) which both exemplifies and complicates this idea, and develops his vision of tragedy further. The article therefore offers a fresh way of approaching the genre of closet plays and the work of a key Romantic writer, and provides a new approach to tragedy which contributes to broader critical discussions about how tragedy is experienced and theorised

"Why is the Right Obsessed with Epic Poetry?"

An article on three promninent members of the American right-wing political sphere - Elon Musk, Jordan Peterson, and Peter Thiel - exploring their various allusions to epic literature and arguing that these are the bearers of an ambitious new political vision for the United States

Theoretical investigation of functionalized diamond-like carbon with COOH, OH and NH2: a comprehensive DFT-D study

In this study, the functionalization of diamond-like carbon (DLC) with carboxyl (COOH), hydroxyl (OH) and amine (NH₂) groups was investigated to understand its impact on the structural, electronic and nonlinear optical (NLO) properties. Dispersion-corrected density functional theory (DFT-D) calculations using the B3LYP-D3(BJ) exchange–correlation functional were performed in all calculations. The results indicated that functionalization with these groups enhanced the reactivity of the DLC surface. Molecular reactivity descriptors revealed that COOH − DLC exhibited the highest softness (S = 0.25 eV), significant electrophilicity (ω = 2.55 eV) and a reduced energy gap (∆Eg = 3.97 eV). Time-dependent DFT (TD-DFT) analysis showed that COOH − DLC achieved the maximum absorption wavelength among the systems investigated. Additionally, functionalization improved the NLO properties, including increased polarity, with COOH − DLC displaying the highest first hyperpolarizability value. Natural bond orbital (NBO) analysis indicated significant orbital delocalization between the functional groups and the pristine DLC surface. Quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses, based on the reduced density gradient (RDG), provided a detailed characterization of interactions, highlighting the presence of van der Waals forces

XGBoost Model for Predicting Property Prices in the UK Real Estate Market

Abstract. This study contributes to the enhancement of the predictive models and mitigating regression problem, using data science machine learning approach. It critically evaluates studies that have been done within the predictive valuation, forecasting models, using the current technological trends in machine learning, through the proposed framework: Sustainable Feature Machine Learning Agile Framework (SFMLAF). SFMLAF suggests that our proposed model based on XGBoost demonstrated better accuracy based on these results; utilizing the following validation metrics: XGBoost RMSE: 0.444, MAPE: 1.94%, MAE: 0.234. The required datasets were sourced online from the UK government property sold dataset, under the Open Government licence v.3.0, focusing on four UK cities with the following data observations: London: 658,337, Peterborough: 44,635, Leeds: 102,984, and Manchester: 154,626. In conclusion, the proposed predictive model aims to deliver practical benefits for real estate professionals, homebuyers and sellers by enhancing the accuracy and reliability of property valuation in the UK market

Categorical color perception shown in a cross-lingual comparison of visual search

Categorical perception (CP) for colors entails that hues within a category look more similar than would be predicted by their perceptual distance. We examined color CP in both a UK and a remote population (Himba) for newly acquired and long-established color terms. Previously, the Himba language used the same color term for blue and green but now they have labels that match the English terms. However, they still have no color terms for the purple areas of color space. Hence, we were able to investigate a color category boundary that exists in the Himba language but not in English as well as a boundary that is the same for both. CP was demonstrated for both populations in a visual search task for one different hue among 12 otherwise similar hues; a task that eliminated concerns of label matching. CP was found at the color-category boundaries that are specific to each language. Alternative explanations of our data are discussed and, in particular, that it is the task-dependent use of categorical rather than non-categorical (perceptual) color networks which produces CP. It is suggested that categorical networks for colors are bilaterally represented and are the default choice in a suprathreshold similarity judgment

Performance of Higher-Order Networks in Reconstructing Sequential Paths: from Micro to Macro Scale

Activities such as the movement of passengers and goods, the transfer of physical or digital assets, web navigation and even successive passes in football, result in timestamped paths through a physical or virtual network. The need to analyse such paths has produced a new modelling paradigm in the form of higher-order networks which are able to capture temporal and topological characteristics of sequential data. This has been complemented by sequence mining approaches, a key example being sequential motifs measuring the prevalence of recurrent subsequences. Previous work on higher-order networks has focused on how to identify the optimal order for a path dataset, where the order can be thought of as the number of steps of memory encoded in the model. In this paper, we build on these approaches to consider which orders are necessary to reproduce different path characteristics, from path lengths to counts of sequential motifs, viewing paths generated from different higher-order models as null models which capture features of the data up to a certain order, and randomise otherwise. Furthermore, we provide an important extension to motif counting, whereby cases with self-loops, starting nodes, and ending nodes of paths are taken into consideration. Conducting a thorough analysis using path lengths and sequential motifs on a diverse range of path datasets, we show that our approach can shed light on precisely where models of different order overperform or underperform, and what this may imply about the original path data