Celia Chen, Alex Leitch, William Jordan Conway
et al.
The quality of a user's social media experience is determined both by the content they see and by the quality of the conversation and interaction around it. In this paper, we look at replies to tweets from mainstream media outlets and official government agencies and assess if they are good faith, engaging honestly and constructively with the original post, or bad faith, attacking the author or derailing the conversation. We assess automated approaches that may help in making this determination and then show that within our dataset of replies to mainstream media outlets and government agencies, bad faith interactions constitute 68.3% of all replies we studied, suggesting potential concerns about the quality of discourse in these specific conversational contexts. This is particularly true from verified accounts, where 91.7% of replies were bad faith. Given that verified accounts are algorithmically amplified, we discuss the implications of our work for understanding the user experience on social media.
Yasmine Bedoui, Zein Kallas, Adrià Menéndez i Molist
et al.
Pricing is an essential element that significantly impacts the supply chain mechanisms. The primary objective of this study is to explore the transmission of producer-to-consumer prices in the Spanish fresh tomato industry. Employing the Threshold Vector Autoregressive model, and subsequently utilizing the Generalized Impulse Response Function, we investigated the nonlinear price adjustments that occur in response to positive and negative shocks affecting both tomato prices of consumers and producers. The findings show a clear pattern of distinct reactions between segments in response to shocks. Specifically, the speed and intensity of consumer price responses to producer price shocks appear to surpass those observed when producer prices respond to consumer price shocks. Furthermore, it is evident from the current research that the behavior of producers has evolved from earlier studies that utilized outdated information, suggesting a more competitive approach. The research identifies a new trend in producer behavior within the supply chain. By analyzing tomato price fluctuations, it advances current knowledge and provides essential market insights to support informed decision-making.
The development of Braille recognition technology is intrinsically linked to the educational rights of individuals with visual impairments. The key challenges in natural scene Braille detection include three core trade-offs: difficulty extracting small-target features under complex background interference, a balance between model accuracy and real-time performance, and generalization across diverse scenes. To address these issues, this paper proposes an improved YOLOv11 algorithm that integrates a lightweight gating mechanism and subspace attention. By reconstructing the C3k2 module into a hybrid structure containing Gated Bottleneck Convolutions (GBC), the algorithm effectively captures weak Braille dot matrix features. A super-lightweight subspace attention module (ULSAM) enhances the attention to Braille regions, while the SDIoU loss function optimizes bounding box regression accuracy. Experimental results on a natural scene Braille dataset show that the algorithm achieves a Precision of 0.9420 and a Recall of 0.9514 with only 2.374 M parameters. Compared to the base YOLOv11, this algorithm improves the combined detection performance (Precision: 0.9420, Recall: 0.9514) by 3.2% and reduces computational complexity by 6.3% (with only 2.374 M parameters). Ablation experiments validate the synergistic effect of each module: the GBC structure reduces the model parameter count by 8.1% to maintain lightweight properties, and the ULSAM effectively lowers the missed detection rate of ultra-small Braille targets. This study provides core algorithmic support for portable Braille assistive devices, advancing the technical realization of equal information access for individuals with visual impairments.
Social media have been fundamental in the daily lives of millions of people, but they have raised concerns about content moderation policies, the management of personal data, and their commercial exploitation. The acquisition of Twitter (now X) by Elon Musk in 2022 generated concerns among Twitter users regarding changes in the platform's direction, prompting a migration campaign by some user groups to the federated network Mastodon. This study reviews the onboarding of users to this decentralised platform between 2016 and 2022 and analyses the migration of 19,000 users who identified themselves as supporters of the platform switch. The results show that the migration campaign was a reactive response to Elon Musk's acquisition of Twitter and was led by a group of highly active academics, scientists, and journalists. However, a complete transition was not realised, as users preferred to straddle their presence on both platforms. Mastodon's decentralisation made it difficult to exactly replicate Twitter's communities, resulting in a partial loss of these users' social capital and greater fragmentation of these user communities, which highlights the intrinsic differences between both platforms.
Gaspard Abel, Argyris Kalogeratos, Jean-Pierre Nadal
et al.
The emergence of online social platforms, such as social networks and social media, has drastically affected the way people apprehend the information flows to which they are exposed. In such platforms, various information cascades spreading among users is the main force creating complex dynamics of opinion formation, each user being characterized by their own behavior adoption mechanism. Moreover, the spread of multiple pieces of information or beliefs in a networked population is rarely uncorrelated. In this paper, we introduce the Mixture of Interacting Cascades (MIC), a model of marked multidimensional Hawkes processes with the capacity to model jointly non-trivial interaction between cascades and users. We emphasize on the interplay between information cascades and user activity, and use a mixture of temporal point processes to build a coupled user/cascade point process model. Experiments on synthetic and real data highlight the benefits of this approach and demonstrate that MIC achieves superior performance to existing methods in modeling the spread of information cascades. Finally, we demonstrate how MIC can provide, through its learned parameters, insightful bi-layered visualizations of real social network activity data.
The spread of toxic content on online platforms presents complex challenges that call for both theoretical insight and practical tools to test intervention strategies. In this novel research paper, we introduce a simulation-based framework that extends the classical SEIZ (Susceptible-Exposed-Infected-Skeptic) epidemic model to capture the dynamics of toxic message propagation. Our simulator incorporates active moderation mechanisms through two distinct variants: a basic moderator, which implements uniform, non-personalized interventions, and smart moderator, which leverages user-specific psychological profiles based on Dark Triad traits to apply personalized, threshold-driven moderation. By varying parameter configurations, the simulator allows for systematic exploration of how different moderation strategies influence user state transitions over time. Simulation results demonstrate that while generic interventions can curb toxicity under certain conditions, profile-aware moderation proves significantly more effective in limiting both the spread and persistence of toxic behavior. This simulation framework offers a flexible and extensible tool for studying and designing adaptive moderation strategies in complex online social systems.
Distrust of public serving institutions and anti-establishment views are on the rise (especially in the U.S.). As people turn to social media for information, it is imperative to understand whether and how social media environments may be contributing to distrust of institutions. In social media, content creators, influencers, and other opinion leaders often position themselves as having expertise and authority on a range of topics from health to politics, and in many cases devalue and dismiss institutional expertise to build a following and increase their own visibility. However, the extent to which this content appears and whether such content increases engagement is unclear. This study analyzes the prevalence of anti-establishment sentiment (AES) on the social media platform TikTok. Despite its popularity as a source of information, TikTok remains relatively understudied and may provide important insights into how people form attitudes towards institutions. We employ a computational approach to label TikTok posts as containing AES or not across topical domains where content creators tend to frame themselves as experts: finance and wellness. As a comparison, we also consider the topic of conspiracy theories, where AES is expected to be common. We find that AES is most prevalent in conspiracy theory content, and relatively rare in content related to the other two topics. However, we find that engagement patterns with such content varies by area, and that there may be platform incentives for users to post content that expresses anti-establishment sentiment.
In recent years, the essential roles of digital payment have gradually emerged. However, current research on digital payment adoption models rarely incorporates the outcomes of digital payment, and it also gives less consideration to rural residents. Considering these two issues as a research gap, this article establishes a unified digital payment use and credit availability model by extending the UTAUT2 framework in two aspects and applying it to China’s rural residents. The first extension is to add credit availability as an outcome variable of digital payment use. The second is to add two factors important to farmers, perceived riskiness and innovativeness, as constructs. Structural equation modeling is employed to analyze data collected from nearly 500 Chinese rural residents. The results show that almost 90% of rural residents have used digital payment. However, only a low proportion use it for many purposes or frequently. Rural residents’ digital payment use can increase credit availability. Performance expectancy, effort expectancy, social influence, price value, and innovativeness significantly and positively affect digital payment intention and behavior, thereby indirectly improving credit availability. However, the perceived risk does not influence digital payment adoption, possibly due to effective protective behaviors. In addition to the indirect effect, the results show that innovativeness also has a direct impact on credit availability. JEL Classification : M15, G21.
History of scholarship and learning. The humanities, Social Sciences
Online interviewing has allowed cost-effective data collection and access to diverse, geographically dispersed populations. However, these benefits come with significant risks to research integrity, particularly in terms of attracting potential scammer participants—individuals who volunteer to take part in research by creating a false identity that fits the eligibility criteria for a research study. This paper explores our experiences in conducting online interviews for a qualitative health research study involving expectant parents with a rare genetic condition. We outline the strategies we implemented to deter fraudulent interest and participation at different stages of recruitment and data collection and share our critical reflections on how we managed these challenges. This paper highlights how Artificial Intelligence (AI)-assisted software may help scammers to evade detection, representing a new external threat to the validity with online qualitative research. We provide practical recommendations for safeguarding qualitative research in the digital age, along with a checklist for the design stage, informed by recent technological advancements and lessons learned from our study.
Background: Formal and informal social support networks are crucial for mental well-being, providing a sense of personal security and safety, especially during times of crisis. Aims: The aim of this study is to examine women’s perceptions of their own experiences alongside their perceptions of their partners’ experiences in the relationship between social support and the sense of danger within intimate relationships, based solely on women’s self-reports. It compares couples experiencing high-intensity conflict (particularly related to separation) with couples from the general population, and explores how the distribution of social support, whether received by the woman, the man, both, or neither is associated with feelings of danger. Methods: The sample comprised 165 women from two subsamples: 70 women from the general population and 95 women engaged in high-intensity intimate conflict, who were undergoing separation proceedings. Data were collected via self-report questionnaires, which included the Multidimensional Scale of Perceived Social Support and a custom questionnaire for Sense of Danger from the Partner. No direct data were collected from male partners; women provided both their own reports and their perceptions of their partner’s experiences. Repeated measures analysis was performed to examine the sense of danger as perceived for both themselves and their partners. Results: The analysis shows that the higher the level of social support, the weaker the sense of danger reported by women for themselves and for their partners. It also reveals that the sense of danger (both self-reported and attributed to the partner) is stronger among couples engaged in high-intensity conflict than among those in the general population, and that women report a stronger sense of danger for themselves than they attribute to their men. Importantly, when social support is provided to only one partner, it is associated with a higher sense of danger in the other partner. Conclusions: Social support has been associated with lower reported feelings of danger in intimate relationships. However, when support is given to only one partner, it may increase the other partner’s sense of danger. These findings highlight the need for balanced support for both partners in order to reduce tension and promote a greater sense of safety during times of conflict.
Constructed wetlands for effluent treatment (CW-ET) play a vital role in the degradation of pollutants, the purification of water, and the improvement of freshwater ecosystems. However, conventional designs often lack a methodical approach for quantifying the efficacy of these wetlands. In this context, numerical simulations aid in optimizing vegetation selection and placement in these systems, thereby enhancing their overall efficiency. In this study, the MIKE21 hydrodynamic (HD) module was coupled with the advection–dispersion (AD) module to simulate the Yingtai CW-ET in Hai’an. Accordingly, key parameters involved in effective water purification were calibrated and the system’s performance in treating effluent from wastewater treatment facilities was evaluated. The findings demonstrated significant removal efficiencies for chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and ammonia (NH3-N), with average rates of 51.14%, 43.14%, 63.82%, and 54.38%, respectively. In addition, the simulations exhibited high accuracy, with hydrodynamic predictions deviating by under 5% and water quality approximations by under 15%. The treated water quality met the requirements for Class IV surface water standards. Utilizing numerical simulations offers valuable insights for the design and performance evaluation of future constructed wetlands.
Ali Khodabandeh Yalabadi, Mehdi Yazdani-Jahromi, Sina Abdidizaji
et al.
The rapid and widespread dissemination of misinformation through social networks is a growing concern in today's digital age. This study focused on modeling fake news diffusion, discovering the spreading dynamics, and designing control strategies. A common approach for modeling the misinformation dynamics is SIR-based models. Our approach is an extension of a model called 'SBFC' which is a SIR-based model. This model has three states, Susceptible, Believer, and Fact-Checker. The dynamics and transition between states are based on neighbors' beliefs, hoax credibility, spreading rate, probability of verifying the news, and probability of forgetting the current state. Our contribution is to push this model to real social networks by considering different classes of agents with their characteristics. We proposed two main strategies for confronting misinformation diffusion. First, we can educate a minor class, like scholars or influencers, to improve their ability to verify the news or remember their state longer. The second strategy is adding fact-checker bots to the network to spread the facts and influence their neighbors' states. Our result shows that both of these approaches can effectively control the misinformation spread.
Ali Raza, Khaled Mohamed Elhadi, Muhammad Abid
et al.
Waste tyre rubber has become an environmental and health concern that needs to be sustainably managed to avoid fire hazards and save natural resources. This research work aims to study the structural behavior of glass fiber reinforced polymer (glass-FRP) reinforced rubberized concrete (GRC) compressive elements under monotonic axial compression loads. Nine GRC circular compressive elements with different axial and crosswise reinforcement ratios were fabricated. All the elements were 300 mm in diameter and 1200 mm in height. A 3D nonlinear finite element equation (FEM) was suggested for the GRC compressive elements using a commercial package ABAQUS. A parametric study has been done to examine the effect of various parameters of GRC elements. The test outcomes revealed that the ductility of GRC elements ameliorated with the lessening in the spaces of glass-FRP ties. The addition of rubberized concrete improved the ductility of GRC elements. The damage to GRC elements occurred due to the vertical cracking along the height of the elements. The estimates of FEM were in close agreement with the test outcomes. The suggested empirical equation depending on the 600 test elements, which considered the lateral confinement effect of FRP ties, presented higher accuracy than previous equations.
Dambar Bahadur Khadka, Tikaram Pahadi, Sunil Aryal
et al.
Proteases are large group of highly demanded enzymes having huge application in food and pharmaceutical industries. Numerous sources, including plants, microorganisms, and animals, can be used to obtain protease. Due to its affordability and safety consideration, fermented foods have recently attracted more attention as a source of microbial protease. The present study aimed to extract protease from kinema, partially purify the extracted protease following dialysis after precipitation with ammonium sulfate, and determine general characteristics of protease. The kinema having highest proteolysis activity after three days of control fermentation (Temperature 30±2 °C, RH 66 ± 2%) was taken for the study. About 2.45 fold of purification with overall recovery of 63.21% was achieved after precipitation with ammonium sulfate at 30–70% saturation level followed by dialysis of crude extracted protease. The dialysed kinema protease had specific activity of 7.90 U/mg. The enzyme remained actively functional across a wider pH (5–9) and temperature (40-60 °C) range. SDS-PAGE and Zymogram confirmed the presence of three major active bands respectively of 29.04 kDa, 36.09 kDa and 46.35 kDa in the kinema protease extract. The enzyme kinetics data on casein, fitted to Mechaelis Mentens’ plots showed the protease had Vmax of 1.001 U/ml with corresponding Km value of 0.825 mg/ml. Metal ions such as iron, mercury and aluminium showed the inhibition effect whereas presence of sodium, zinc, and calcium shows the activation effect on protease performance. The enzyme was active over various natural substrates; showing maximal activity on casein, and subsequent to bovine serum albumin, gelatin, hemoglobin and whey protein respectively. Furthermore, molecular weight distribution of the protease extract and activity inhibition with ethylenediaminetetraacetic acid and phenylmethylsulfonyl fluoride, suggesting the protease from kinema could be a metal dependent serine protease or mixture of them.
Machine and manufacturing migrant workers in the new era are side by side cooperation and interaction of the new labor force form. Based on the dynamic survey of China’s floating population in 2011 and the data collected by the International Federation of Robotics, the Bartik instrument variable method is used to analyze the impact of industrial robots on the employment quality of the floating population in manufacturing industry at the city level. As the city scale expands, industrial robots have an inverted U-shaped effect on the employment quality of manufacturing migrant workers. Industrial robots have a positive U-shaped influence on the number of hours that migrant workers in manufacturing work, with an inflexion point of 1.3721 units per 10,000 workers. The influence of industrial robots on migrant workers’ working conditions in the manufacturing sector was U-shaped, and 1.668 units per 10,000 workers marked the tipping point. Nevertheless, industrial robots have an inverse influence on the occupation stability of migrant workers in the manufacturing industry. Precisely, the installation density of industrial robots in the manufacturing industry has a detrimental impact on the occupational stability of migrant employees. Industrial robots are negatively associated with the working conditions of migrant workers employed in manufacturing. There were detrimental effects on the employment quality of manufacturing migrant workers in cities with higher and lower population densities. In the end, for every manufacturing farmer using an industrial robot, the likelihood of being miserable and almost happy went up by 2.64 percent and 5.59 percent, respectively, while the likelihood of being happy went down by 7.62 percent.
Recommender Systems (RS) currently represent a fundamental tool in online services, especially with the advent of Online Social Networks (OSN). In this case, users generate huge amounts of contents and they can be quickly overloaded by useless information. At the same time, social media represent an important source of information to characterize contents and users' interests. RS can exploit this information to further personalize suggestions and improve the recommendation process. In this paper we present a survey of Recommender Systems designed and implemented for Online and Mobile Social Networks, highlighting how the use of social context information improves the recommendation task, and how standard algorithms must be enhanced and optimized to run in a fully distributed environment, as opportunistic networks. We describe advantages and drawbacks of these systems in terms of algorithms, target domains, evaluation metrics and performance evaluations. Eventually, we present some open research challenges in this area.
A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This paper presents a differential game approach to the opinion formation problem in social networks to investigate the evolution of opinions as a result of a Nash equilibrium. The opinion of each individual is described by a differential equation, which is the continuous-time Hegselmann-Krause model for opinion dynamics with a time delay in input. The objective of each individual is to seek optimal strategies for her own opinion evolution by minimizing an individual cost function. Two differential game problems emerge, one for a population that is not stubborn and another for a population that is stubborn. The open-loop Nash equilibrium actions and their associated opinion trajectories are derived for both differential games using Pontryagin's principle. Additionally, the receding horizon control scheme is used to practice feedback strategies where the information flow is restricted by fixed and complete social graphs as well as the second neighborhood concept. The game strategies were executed on the well-known Zachary's Karate Club social network. The resulting opinion trajectories associated with the game strategies showed consensus, polarization, and disagreement in final opinions.
Based on the Internet Finance development index developed by Peking University in investigating 31 provinces, we use social network analysis to investigate the spatial correlation and influencing factors of the Internet Finance of China’s provinces. The research shows that the spatial correlation of Internet Finance of China’s provinces has significant characteristics of a regional gradient. The correlation among their correlation is moderate, more closely related, smooth, or weak. All provinces gather to form four plates; they play their respective functional advantages and have different statuses, functions, and roles. The differences between provinces in industrial structure, degree of marketization, infrastructure, degree of informatization, and geographical distance significantly affect the spatial correlation of Internet finance development in China, and the degree of influence decreases in turn. Our results might positively affect policymakers in promoting the coordinated development of regional Internet finance in China.
History of scholarship and learning. The humanities, Social Sciences