Search Results (39)

The rapid expansion of digital communication platforms has created new spaces for antisocial, manipulative, and emotionally detached behaviors. While psychopathy has been extensively studied in clinical and forensic settings, its digital manifestation, referred to as cyberpsychopathy, remains conceptually underdefined. This integrative review aimed to synthesize empirical research exploring psychopathy and aversive personality traits in online contexts to identify key conceptual domains and propose a preliminary definition. A systematic search across five databases yielded 35 peer-reviewed studies meeting the inclusion criteria. Using a biopsychosocial framework and thematic synthesis, six interrelated domains were identified: online behaviors (e.g., trolling and deception), online environments (e.g., anonymity and reward mechanisms), sociodemographic factors (e.g., age and gender), personality traits (e.g., psychopathy and narcissism), psychological factors (e.g., emotion dysregulation and low self-esteem), and motivations (e.g., dominance and emotional compensation). These domains interact to shape how psychopathic tendencies manifest online. Most studies were of moderate-to-high methodological quality, though variability limited direct comparisons. We propose cyberpsychopathy as a multidimensional construct representing the expression of aversive traits facilitated by digital affordances and psychological vulnerabilities. This review provides a foundational framework for understanding cyberpsychopathy and underscores the need for empirical validation and the development of assessment tools suited to digital behavior in both clinical and forensic settings. Full article

Trolling behavior is not simply a result of ‘bad actors’, an individual trait, or a linguistic phenomenon, but emerges from complex contagious social dynamics. This study uses formal concepts from information theory and complexity science to study it as such. The data comprised over 13 million Reddit comments, which were classified as troll or non-troll messages using the BERT model, fine-tuned with a human coding set. We derive the unique, minimally complex, and maximally predictive model from statistical mechanics, i.e., ε-machines and transducers, and can distinguish which aspects of trolling behaviors are both self-motivated and socially induced. While the vast majority of self-driven dynamics are like flipping a coin (86.3%), when social contagion is considered, most users (95.6%) show complex hidden multiple-state patterns. Within this complexity, trolling follows predictable transitions, with, for example, a 76% probability of remaining in a trolling state once it is reached. We find that replying to a trolling comment significantly increases the likelihood of switching to a trolling state or staying in it (72%). Besides being a showcase for the use of information-theoretic measures from dynamic systems theory to conceptualize human dynamics, our findings suggest that users and platform designers should go beyond calling out and removing trolls, but foster and design environments that discourage the dynamics leading to the emergence of trolling behavior. Full article

This study investigates the moderating effect of language attitude on the relationships among social media addiction, social pain, and internet trolling among college students. A sample of 891 students from various colleges and universities completed validated measures assessing their levels of social media addiction, social pain, internet trolling, and language attitude. Using a latent variable approach within a multigroup structural equation modeling (SEM) framework, participants were divided into three groups (high, medium, and low language attitude) based on their language attitude scores. The SEM analysis revealed distinct patterns across groups. In the high language attitude group, both social media addiction and social pain significantly predicted internet trolling, with standardized regression coefficients of 0.564 and 0.728, respectively. In the medium language attitude group, the predictive effects remained significant; however, the magnitude of the coefficients decreased markedly (0.264 for social media addiction and 0.562 for social pain). In contrast, in the low language attitude group, neither social media addiction nor social pain emerged as significant predictors of internet trolling. Interestingly, the covariance between social media addiction and social pain remained consistent across the three groups, suggesting a stable interrelationship irrespective of language attitude level. These findings imply that language attitude plays a crucial moderating role in the interplay among social media addiction, social pain, and internet trolling. Specifically, higher levels of language attitude appear to amplify the effects of social media addiction and social pain on internet trolling behavior, while lower levels attenuate these associations. The results underscore the importance of considering individual differences in language attitudes when developing intervention strategies aimed at mitigating problematic online behaviors among college students. Full article

Antisocial behavior (ASB), including trolling and aggression, undermines constructive discourse and escalates during periods of societal stress, such as the COVID-19 pandemic. This study aimed to examine ASB on social media during the COVID-19 pandemic by leveraging a novel annotated dataset and state-of-the-art transformer models for detection and classification of ASB categories. Specifically, this study examined ASB within a gold-standard corpus of tweets collected from Ghana during a 21-day lockdown. Each tweet was meticulously annotated into ASB categories or non-ASB, enabling a comprehensive analysis of online behaviors. We employed three state-of-the-art transformer-based language models (BERT, RoBERTa, and ELECTRA) and compared their performance against traditional machine learning models. The results demonstrate that the transformer-based approaches substantially outperformed the baseline models, achieving a high detection accuracy across both binary and multiclass classification tasks. RoBERTa excelled in binary ASB detection, attaining a 95.59% accuracy and an F1-score of 94.99%, while BERT led in multiclass classification, with a 94.38% accuracy and an F1-score of 93.92%. Trolling emerged as the most prevalent ASB type, reflecting the polarizing nature of online interactions during the lockdown. This study highlights the potential of transformer-based models in detecting diverse online behaviors and emphasizes the societal implications of ASB during crises. The findings provide a foundation for enhancing moderation tools and fostering healthier online environments. Full article

The mixing of hydrothermal vent fluids with deep ocean water and near-vent pelagic matter results in particle populations with a complex composition consisting of hydrothermally derived, rock-forming, and biogenic particles. This study is the first investigation of deep sediment trap material collected at the Jan Mayen hydrothermal vent field area at 71° N and 6° W of the southernmost Mohns Ridge in the Norwegian–Greenland Sea. This area is characterized by high magmatic activity, axial volcanic ridges, and mafic-hosted volcanogenic massive sulfide deposits. Data on sinking particle fluxes from two hydrothermal settings, the Troll Wall and Soria Moria vent fields, located about 4 km apart, are discussed in the article. In particular, the study emphasize the differences between two hydrothermal settings from each other that demonstrate the geodiversity of hydrothermal processes within the relatively shallow Jan Mayen hydrothermal vent field area affected by the Iceland and Jan Mayen hotspots. The fluxes of sinking hydrothermally derived particles (barite, gypsum, non-crystalline Fe-Si oxyhydroxides, and Fe, Zn, and Cu sulfides) obtained at the Jan Mayen hydrothermal vents made it possible to elucidate the characteristic features of their buoyancy plumes and compare them with similar data reported for other submarine hydrothermal systems. In terms of the composition of the deep-sea hydrothermal particles from buoyant plumes, the studied vent fields are most similar to the Menez Gwen and Lucky Strike vent fields affected by the Azores hotspot. The supply of hydrothermally derived matter is accompanied by normal pelagic/hemipelagic sedimentation, which is dominated by biogenic particles, especially in the upper water layers. Full article

Humour and antisocial behaviour on the internet are under-researched. Online spaces have opened a gateway for new ways to express unrestrained humour (e.g., dark humour) and ways to behave antisocially (e.g., online trolling). The tendencies and motivations of those engaging with such humour and behaviour are yet to be clearly established and understood. The present study aimed to fill this gap by exploring the interplay between dark humour, online trolling, and dark personality traits. Participants (N = 160) completed an online survey consisting of trait scales to assess the Dark Tetrad, dark humour, and online trolling, as well as two online trolling tasks (enjoyment and ability) and two dark humour meme tasks (enjoyment and ability). The results confirmed relationships between the Dark Tetrad and the dark humour trait, and several Dark Tetrad traits were related to the enjoyment of and ability to produce dark humour. Furthermore, dark humour and online trolling were closely related. The findings also revealed that online trolls did not enjoy being trolled but did enjoy trolling, and this ability to troll is underpinned by sadism. These findings illustrate the potential dark psychological motivations for using dark humour, demonstrate that online trolling is infused with darker forms of humour, and provide deeper insights into online trolls. Full article

Within the dynamic realm of social media, the proliferation of harmful content can significantly influence user engagement and emotional health. This study presents an in-depth analysis that bridges diverse domains, from examining the aftereffects of personal online attacks to the intricacies of online trolling. By leveraging an AI-driven framework, we systematically implemented high-precision attack detection, psycholinguistic feature extraction, and sentiment analysis algorithms, each tailored to the unique linguistic contexts found within user-generated content on platforms like Reddit. Our dataset, which spans a comprehensive spectrum of social media interactions, underwent rigorous analysis employing classical statistical methods, Bayesian estimation, and model-theoretic analysis. This multi-pronged methodological approach allowed us to chart the complex emotional responses of users subjected to online negativity, covering a spectrum from harassment and cyberbullying to subtle forms of trolling. Empirical results from our study reveal a clear dose–response effect; personal attacks are quantifiably linked to declines in user activity, with our data indicating a 5% reduction after 1–2 attacks, 15% after 3–5 attacks, and 25% after 6–10 attacks, demonstrating the significant deterring effect of such negative encounters. Moreover, sentiment analysis unveiled the intricate emotional reactions users have to these interactions, further emphasizing the potential for AI-driven methodologies to promote more inclusive and supportive digital communities. This research underscores the critical need for interdisciplinary approaches in understanding social media’s complex dynamics and sheds light on significant insights relevant to the development of regulation policies, the formation of community guidelines, and the creation of AI tools tailored to detect and counteract harmful content. The goal is to mitigate the impact of such content on user emotions and ensure the healthy engagement of users in online spaces. Full article

Among emergent climate-sensitive infectious diseases, some mosquito-vectored arbovirus infections have epidemiological, social, and economic effects. Dengue virus (DENV), West Nile virus (WNV), and Chikungunya virus (CHIKV) disease, previously common only in the tropics, currently pose a major risk to global health and are expected to expand dramatically in the near future if adequate containment measures are not implemented. The lack of safe and effective vaccines is critical as it seems likely that emerging mosquito-vectored arbovirus infections will be con-trolled only when effective and safe vaccines against each of these infections become available. This paper discusses the clinical characteristics of DENV, WNV, and CHIKV infections and the state of development of vaccines against these viruses. An ideal vaccine should be able to evoke with a single administration a prompt activation of B and T cells, adequate concentrations of protecting/neutralizing antibodies, and the creation of a strong immune memory capable of triggering an effective secondary antibody response after new infection with a wild-type and/or mutated infectious agent. Moreover, the vaccine should be well tolerated, safe, easily administrated, cost-effective, and widely available throughout the world. However, the development of vaccines against emerging mosquito-vectored arbovirus diseases is far from being satisfactory, and it seems likely that it will take many years before effective and safe vaccines for all these infections are made available worldwide. Full article

In this study, we have identified a Twitter network of bad actors mostly affiliated with Iraqi militias that are closely connected to the federal Iraqi government. Using disinformation and threats of legal action, these users often target journalists and news organizations that are critical of them. Three datasets were collected totaling about 16,000 tweets by using 6 Arabic hashtags. We found three major themes: public shaming and personal attacks; legal threats and misinformation accusations; and glorifying Shiite heroism and promoting conspiracies. These bad actors also created a coordinated attack against journalists, news organizations, and human rights activists and even the UN representative in Iraq, Jeanine Plasschaert, falsely accusing her of fabricating the 2021 federal election results. Full article

This article discusses the results of a fluid inclusion studies in barite collected at the Jan Mayen vent field area (Troll Wall and Perle and Bruse) and Loki’s Castle vent field on the Mohns Ridge segment of the Arctic Mid-Ocean Ridge. Three mafic-hosted volcanogenic massive sulfide deposits were examined within the active vent fields that adequately correspond to the geological settings of ultraslow-spreading ridges and P–T conditions. Hydrothermal sediments were investigated to determine the temperature and salinity of the fluids responsible for barite precipitation. The hydrothermal origin of the barite was confirmed by its morphology. Fluid inclusions are two-phase and homogenize into the liquid phase on heating at temperatures below 287 °C. The salt concentration in fluids trapped in inclusions is 2.6–4.4 wt.% NaCl eq. The crystallization temperatures varied from 276 °C to 119 °C and from 307 °C to 223 °C for the Jan Mayen and Loki’s Castle vent fields, respectively. The data obtained allowed us to confirm evidence of fluid phase separation in the hydrothermal systems and to expand our knowledge of the temperature and salinity of mineral fluids previously known from recent direct measurements during the cruises within the G.O. Sars research vessel. The fluid inclusions data obtained from barites emphasize the fluid features characteristic of volcanogenic massive sulfide deposits, the similarities and differences among the studied hydrothermal sites and allow comparisons with similar products from other active hydrothermal systems. Full article

The main objective of this study is to assess moderation effects of online behaviors between personality traits and addiction to Internet. To this end, four instruments were validated for Portuguese version through confirmatory factor analysis and exploratory factor analysis (Study 1) Multiple regression analysis was applied to examine the personality predictors of specific online behaviors while controlling for gender and age; and moderation effects were assessed (Study 2). Results showed good psychometric properties for the four validated scales. Machiavellianism is positively associated with all the dimensions of this study. Psychopathy is positively associated with total Cyberstalking, Cyberstalking Control, Flaming and Trolling. Narcissism is positively associated with all the dimensions, except Online Harassment and Flaming. Machiavellianism is positively associated with Addiction to Internet through Cyberstalking, Flaming and Trolling. Psychopathy is positively associated with Addiction to Internet through Cyberstalking Control and Flaming. Narcissism is also positively associated with Addiction to Internet through Cyberstalking and Trolling. This study demonstrates that dimensions of the dark triad of personality play an important role in Internet addiction through online behaviors. The results of this study have theoretical and practical implications: on the one hand, they reinforces the findings of other studies showing that dimensions of the dark personality triad play an important role in Internet and social network addition, contributing to the literature; and, on the other hand, on a practical level, they allow to conduct awareness campaigns in communities, schools, and work to understand that one can be exposed to unpleasant situations due to behaviors that some people with personality traits of Machiavellianism, narcissism and/or psychopathy that may cause problems affecting the mental, emotional and psychological health of others. Full article

Phishing is an online threat where an attacker impersonates an authentic and trustworthy organization to obtain sensitive information from a victim. One example of such is trolling, which has long been considered a problem. However, recent advances in phishing detection, such as machine learning-based methods, have assisted in combatting these attacks. Therefore, this paper develops and compares four models for investigating the efficiency of using machine learning to detect phishing domains. It also compares the most accurate model of the four with existing solutions in the literature. These models were developed using artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and random forest (RF) techniques. Moreover, the uniform resource locator’s (URL’s) UCI phishing domains dataset is used as a benchmark to evaluate the models. Our findings show that the model based on the random forest technique is the most accurate of the other four techniques and outperforms other solutions in the literature. Full article

Background: This study presents a graph-based, macro-scale, polarity-based, echo chamber detection approach for Twitter. Echo chambers are a concern as they can spread misinformation, and reinforce harmful stereotypes and biases in social networks. Methods: This study recorded the German-language Twitter stream over two months, recording about 6.7M accounts and their 75.5M interactions (33M retweets). This study focuses on retweet interaction patterns in the German-speaking Twitter stream and found that the greedy modularity maximization and HITS metric are the most effective methods for identifying echo chambers. Results: The purely structural detection approach identified an echo chamber (red community, 66K accounts) focused on a few topics with a triad of anti-Covid, right-wing populism and pro-Russian positions (very likely reinforced by Kremlin-orchestrated troll accounts). In contrast, a blue community (113K accounts) was much more heterogeneous and showed “normal” communication interaction patterns. Conclusions: The study highlights the effects of echo chambers as they can make political discourse dysfunctional and foster polarization in open societies. The presented results contribute to identifying problematic interaction patterns in social networks often involved in the spread of disinformation by problematic actors. It is important to note that not the content but only the interaction patterns would be used as a decision criterion, thus avoiding problematic content censorship. Full article

The use of digital twins for the development of Autonomous Maritime Surface Vessels (AMSVs) has enormous potential to resolve the increasing need for water-based navigation and safety at the sea. Aiming at the problem of lack of broad and integrated digital twin implementations with live data along with the absence of a digital twin-driven framework for AMSV design and development, an application framework for the development of a fully autonomous vessel using an integrated digital twin in a 3D simulation environment has been presented. Our framework has 4 layers which ensure that simulation and real-world vessel and the environment are as close as possible. Åboat, an in-house, experimental research platform for maritime automation and autonomous surface vessel applications, equipped with two trolling electric motors, cameras, LiDARs, IMU and GPS has been used as the case study to provide a proof of concept. Åboat, its sensors, and the environment have been replicated in a commercial, 3D simulation environment, AILiveSim. Using the proposed application framework, we develop obstacle detection and path planning systems based on machine learning which leverage live data from a 3D simulation environment to mirror the complex dynamics of the real world. Exploiting the proposed application framework, the rewards across training episodes of a Deep Reinforcement Learning model are evaluated for live simulated data in AILiveSim. Full article

Eutrophication process and phytoplankton primary productivity have intensified in continental aquatic ecosystems because of climate change. As a consequence, the proliferation of potentially toxic cyanobacteria is increasing in frequency, magnitude, and duration. For water sources used in public supply, this growth represents an ecological risk to ecosystems and human health. From October 2021 to February 2022, integrated samples of surface water were obtained from 11 reservoirs in São Paulo State, Brazil (Jaguari, Jacarei, Atibainha, Paiva Castro, Rio Grande, Guarapiranga, Barra Bonita, Bariri, Broa, Salto Grande, and Itupararanga). Limnological variables were obtained using the Troll 500 probe, in addition to depth, turbidity (Tur), chlorophyll a (Chla), and phycocyanin (Phy) concentrations (Turner C3 probe). In the laboratory, chlorophyll-a concentrations (ChlaABS) were analyzed. Phytoplankton biovolume (Utermöhl method) was estimated. The concentrations of microcystins (MCs) and saxitoxins (STXs) were analyzed with Beacon kits, in ELISA microplate reader. For the studied reservoirs, the Secchi disc water transparency ranged from 0.6 to 2.3 m. The average values of water temperature, electrical conductivity, pH, and dissolved oxygen were, respectively, 24.8 °C, 162.9 µS/cm, and 8.4 and 9.5 mg/L. For Tur, Chla, Phy, and ChlaABS, ranged from 1.86 to 24.6 NTU, 3.3 to 105.1 µg/L, 12.4 to 445.2 µg/L, and 4.2 to 84.9 µg/L, respectively. Cyanobacteria was the more representative phytoplankton class in biovolume, from 0.07 to 51.7 mm³/L. STXs and MCs were found in most sampled stations. For STXs it ranged from 0.016 µg/L to 0.308 µg/L, and for MCs in some stations it was higher than 200 µg/L. According to the World Health Organization and Brazilian legislation, in the 11 studied reservoirs, the concentrations of saxitoxins are within the maximum allowed limits (3 µg/L), while for microcystins the concentrations are for most reservoirs above the maximum allowed value (1 µg/L). Considering the analyzed information in relation to water quality and the cyanobacterial community, we verify that most of these environments present a worrying water quality, which can represent a risk for public health. Full article