bluuebunny/crossref_metadata_2025 huggingface dataset:
The Persuasive Power of Large Language Models
Authors: Breum Simon Martin , Egdal Daniel Vædele , Gram Mortensen Victor , Møller Anders Giovanni , Aiello Luca Maria
Published: May 2024

The increasing capability of Large Language Models to act as human-like social agents raises two important questions in the area of opinion dynamics. First, whether these agents can generate effective arguments that could be injected into the online discourse to steer the public opinion. Second, whether artificial agents can interact with each other to reproduce dynamics of persuasion typical of human social systems, opening up opportunities for studying synthetic social systems as faithful proxies for opinion dynamics in human populations. To address these questions, we designed a synthetic persuasion dialogue scenario on the topic of climate change, where a 'convincer' agent generates a persuasive argument for a 'skeptic' agent, who subsequently assesses whether the argument changed its internal opinion state. Different types of arguments were generated to incorporate different linguistic dimensions underpinning psycho-linguistic theories of opinion change. We then asked human judges to evaluate the persuasiveness of machine-generated arguments. Arguments that included factual knowledge, markers of trust, expressions of support, and conveyed status were deemed most effective according to both humans and agents, with humans reporting a marked preference for knowledge-based arguments. Our experimental framework lays the groundwork for future in-silico studies of opinion dynamics, and our findings suggest that artificial agents have the potential of playing an important role in collective processes of opinion formation in online social media.
Making the Pick: Understanding Professional Editor Comment Curation in Online News
Authors: He Yupeng , Gu Yimeng , Shekhar Ravi , Castro Ignacio , Tyson Gareth
Published: May 2024

Online comments within news articles are a key way people share opinions. Discovering insightful comments can, however, be challenging for readers. A solution to this problem is using comment curation, whereby professional editors select the highest quality comments manually --- referred to as ''editor-picks''. This paper studies the growing use of professional editor-curation for user-generated comments. We focus on the New York Times as a case study, using a dataset covering 80k articles. We study the characteristics of editor-pick comments, highlighting how editor criteria vary across news sections (e.g. sports, entertainment). We find that editor-pick comments tend to be longer, more relevant to the article, positive in sentiment, and contain low toxicity. Our analysis further reveals that editors within different news sections exhibit differing criteria when they perform comment selection. Thus, we finally propose a set of models that can automatically identify good candidate editor-picks. Our ultimate goal is to reduce editor and journalistic workload, increasing productivity and the quality of curated comments.
CPL-NoViD: Context-Aware Prompt-Based Learning for Norm Violation Detection in Online Communities
Authors: He Zihao , May Jonathan , Lerman Kristina
Published: May 2024

Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions. Existing machine learning approaches often struggle to adapt to the diverse rules and interpretations across different communities due to the inherent challenges of fine-tuning models for such context-specific tasks. In this paper, we introduce Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a novel method that employs prompt-based learning to detect norm violations across various types of rules. CPL-NoViD outperforms the baseline by incorporating context through natural language prompts and demonstrates improved performance across different rule types. Significantly, it not only excels in cross-rule-type and cross-community norm violation detection but also exhibits adaptability in few-shot learning scenarios. Most notably, it establishes a new state-of-the-art in norm violation detection, surpassing existing benchmarks. Our work highlights the potential of prompt-based learning for context-sensitive norm violation detection and paves the way for future research on more adaptable, context-aware models to better support online community moderators.
A Multi-modal Prompt Learning Framework for Early Detection of Fake News
Authors: Hu Weiqi , Wang Ye , Jia Yan , Liao Qing , Zhou Bin
Published: May 2024

Information spreads quickly through social media platforms, especially fake news with negative or even malicious intentions. In recent years, psychological studies have found that explicit reminders of fake news would diminish its consequence. Therefore, it is crucial to identify their authenticity at an early stage to avoid serious consequences. However, existing methods for fake news detection either utilize auxiliary information including users’ profiles and related events propagation networks or require sufficient and high-quality training data, which is not suitable for early fake news detection in real. An increasing number of social media news not only involves natural language content but also visual content such as images and videos, which give us a new view of fake news detection at an early stage by multi-modal data. In this paper, we propose a Multi-modal Prompt Learning framework (MPL) based on the multi-modal pre-trained model CLIP for early detection of fake news. A learnable prompt module is developed to adaptively and efficiently generate prompt representations to boost the semantic context. MPL can be implemented in supervised or few-shot settings. Extensive experiments show that the proposed MPL obtains substantial performance and efficiency improvement for the early-stage fake news detection task. The results demonstrate that MPL performs considerably well compared to both the state-ofthe-art supervised multi-modal models and the latest promptbased few-shot multi-modal models. Especially, the high recall of fake news and the high precision of real news that MPL achieved compared to other baselines verify that it will better approach one of the motivations that providing early notification of “maybe real” or “maybe fake” with the release of the news.
The Geography of Information Diffusion in Online Discourse on Europe and Migration
Authors: Leonardelli Elisa , Tonelli Sara
Published: May 2024

The online diffusion of information related to Europe and migration has been little investigated from an external point of view. However, this is a very relevant topic, especially if users have had no direct contact with Europe and its perception depends solely on information retrieved online.
In this work we analyse the information circulating online about Europe and migration after retrieving a large amount of data from social media (Twitter), to gain new insights into topics, magnitude, and dynamics of their diffusion. We combine retweets and hashtags network analysis with geolocation of users, linking thus data to geography and allowing analysis from an “outside Europe” perspective, with a special focus on Africa. We also introduce a novel approach based on cross-lingual quotes, i.e. when content in a language is commented and retweeted in another language, assuming these interactions are a proxy for connections between very distant communities.
Results show how the majority of online discussions occurs at a national level, especially when discussing migration. Language (English) is pivotal for information to become transnational and reach far. Transnational information flow is strongly unbalanced, with content mainly produced in Europe and amplified outside. Conversely Europe-based accounts tend to be self-referential when they discuss migration-related topics.
Football is the most exported topic from Europe worldwide. Moreover, important nodes in the communities discussing migration-related topics include accounts of official institutions and international agencies, together with journalists, news, commentators and activists.
Auditing Algorithmic Explanations of Social Media Feeds: A Case Study of TikTok Video Explanations
Authors: Mousavi Sepehr , Gummadi Krishna P. , Zannettou Savvas
Published: May 2024

In recent years, user feeds on social media platforms have shifted from simple, chronologically ordered content posted by their network connections (i.e., friends) to opaque, algorithmically ordered and curated content. This shift has led to regulations that require platforms to offer end users greater transparency and control over their algorithmic recommendation-based feeds. In response, social media platforms such as TikTok have recently started explaining why specific videos are recommended to end users. However, we still lack a good understanding of how these explanations are generated and whether they offer the desired transparency to end users. In this work, we audit explanations provided on short-format videos on TikTok. We collect a large dataset of short-format videos and explanations provided by TikTok (when available) using automated sockpuppet accounts. Then, we systematically characterize the explanations, focusing on their accuracy and comprehensiveness. For our assessments, we compare the provided explanations with video metadata and the behavior of our sockpuppet accounts. Our analysis shows that some generic (non-personalized) reasons are always included in explanations (e.g., "This video is popular in your country"), while at the same time, we find that a large number of provided explanations are incompatible with the behavior of our sockpuppet accounts; (e.g., an account that made zero comments on the platform, was presented with the explanation "You commented on similar videos" in 34% of all recommended videos.) Overall, our audit of TikTok video explanations highlights the need for more accurate, fine-grained, and useful explanations for the end users. We will make our code and dataset available to assist the research community.
The Manifestation of Affective Polarization on Social Media: A Cross-Platform Supervised Machine Learning Approach
Authors: Overgaard Christian Staal Bruun , Lukito Josephine , Soorholtz Kaiya
Published: May 2024

This project explores how affective polarization, hostility towards people’s political adversaries, manifests on social media. Whereas prior attempts have relied on sentiment analysis and bag-of-word approaches, we use supervised machine learning to capture the nuances of affective polarization in text on social media. Specifically, we fine-tune BERT to build a classifier that identifies expressions of affective polarization in posts shared on Facebook or Twitter during the first six months of the COVID-19 pandemic (n = 8,603,695). Focusing on this context allows us to study how affective polarization evolved on social media as the COVID-19 issue went from unfamiliar to highly political. We explore the temporal dynamics of affective polarization on Facebook and Twitter using ARIMA models and an outlier analysis of the first few months of the pandemic. Further, we examine the interplay between affective polarization and virality across the two platforms. The findings have important implications for those seeking to (1) capture affective polarization in text, and (2) understand how affective polarization manifests on social media. These implications are discussed.
Party Prediction for Twitter
Authors: Pelrine Kellin , Imouza Anne , Yang Zachary , Tian Jacob-Junqi , Lévy Sacha , Desrosiers-Brisebois Gabrielle , Feizi Aarash , Amadoro Cécile , Blais André , Godbout Jean-François , Rabbany Reihaneh
Published: May 2024

A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their political affiliation. The accuracy of this model can change the conclusions of a downstream analysis significantly, yet the choice between different models seems to be made arbitrarily. In this paper, we provide a comprehensive survey and an empirical comparison of the current party prediction practices and propose several new approaches which are competitive with or outperform state-of-the-art methods, yet require less computational resources. Party prediction models rely on the content generated by the users (e.g., tweet texts), the relations they have (e.g., who they follow), or their activities and interactions (e.g., which tweets they like). We examine all of these and compare their signal strength for the party prediction task. This paper lets the practitioner select from a wide range of data types that all give strong performance. Finally, we conduct extensive experiments on different aspects of these methods, such as data collection speed and transfer capabilities, which can provide further insights for both applied and methodological research.
Where Did the President Visit Last Week? Detecting Celebrity Trips from News Articles
Authors: Peng Kai , Zhang Ying , Ling Shuai , Ke Zhaoru , Zhang Haipeng
Published: May 2024

Celebrities’ whereabouts are of pervasive importance. For instance, where politicians go, how often they visit, and who they meet, come with profound geopolitical and economic implications. Although news articles contain travel information of celebrities, it is not possible to perform large-scale and network-wise analysis due to the lack of automatic itinerary detection tools. To design such tools, we have to overcome difficulties from the heterogeneity among news articles: 1) One single article can be noisy, with irrelevant people and locations, especially when the articles are long. 2) Though it may be helpful if we consider multiple articles together to determine a particular trip, the key semantics are still scattered across different articles intertwined with various noises, making it hard to aggregate them effectively. 3) Over 20% of the articles refer to celebrity trips indirectly, instead of using the exact celebrity names or location names, leading to large portions of trips escaping regular detecting algorithms. We model text content across articles related to each candidate location as a graph to better associate essential information and cancel out the noises. Besides, we design a special pooling layer based on attention mechanism and node similarity, reducing irrelevant information from longer articles. To make up the missing information resulted from indirect mentions, we construct knowledge sub-graphs for named entities (person, organization, facility, etc.). Specifically, we dynamically update embeddings of event entities like the G7 summit from news descriptions since the properties (date and location) of the event change each time, which is not captured by pre-trained event representations. The proposed CeleTrip jointly trains these modules, which outperforms all baseline models and achieves 82.53% in the F1 metric. By open-sourcing the first tool and a carefully curated dataset for such a task, we hope to facilitate relevant research in celebrity itinerary mining as well as the social and political analysis built upon the extracted trips.
Community Notes vs. Snoping: How the Crowd Selects Fact-Checking Targets on Social Media
Authors: Pilarski Moritz , Solovev Kirill Olegovich , Pröllochs Nicolas
Published: May 2024

Deploying links to professional fact-checking websites (so-called “snoping”) is a common misinformation intervention technique that can be used by social media users to refute misleading claims made by others. However, the real-world effect of snoping may be limited as it suffers from low visibility and distrust towards professional fact-checkers. As a remedy, X (formerly known as Twitter) recently launched its community-based fact-checking system “Community Notes” on which fact-checks are carried out by actual X users and directly shown on the fact-checked posts. Yet, an understanding of how fact-checking via Community Notes differs from regular snoping is largely absent. In this study, we empirically analyze differences in how contributors to Community Notes and Snopers select their targets when fact-checking social media posts. For this purpose, we collect and holistically analyze two unique datasets from X: (a) 25,912 community-created fact-checks from X's Community Notes platform, and (b) 52,505 “snopes” that debunk posts via fact-checking replies that link to professional fact-checking websites. We find that Notes contributors and Snopers focus on different targets when fact-checking social media content. For instance, Notes contributors tend to fact-check posts from larger accounts with higher social influence and are relatively less likely to emphasize the accuracy of non-misleading posts. Fact-checking targets of Notes contributors and Snopers rarely overlap; however, those overlapping exhibit a high level of agreement in the fact-checking assessment. Moreover, we demonstrate that Snopers fact-check social media posts at a higher speed. Altogether, our findings imply that different fact-checking approaches – carried out on the same social media platform – can result in vastly different social media posts getting fact-checked. This has important implications for future research on misinformation, which should not rely on a single fact-checking approach when compiling misinformation datasets. From a practical perspective, our findings imply that different fact-checking approaches complement each other and may help social media providers to optimize strategies to combat misinformation on their platforms.
Disappearing without a Trace: Coverage, Community, Quality, and Temporal Dynamics of Wikipedia Articles on Endangered Brazilian Indigenous Languages
Authors: Vasconcelos Marisa , De Souza Mizukami Priscila , Pinhanez Claudio Santos
Published: May 2024

Nearly half of Brazil's 180 Indigenous languages face extinction within the next 20 years. What's more concerning is that most of these languages lack a single scientific article describing them, which means they could disappear without leaving any documented evidence of their existence. This work investigates the state of articles about those languages in Wikipedia, both in the English and Portuguese versions, regarded here as indicative of the minimum world-level trace of the previous existence of these languages. Our study shows that over 30% of these languages do not have a single Wikipedia article describing them. It also highlights that the Portuguese and English editing communities are not only distinct, but have different practices, achieving similar levels of quality through different temporal dynamics. These results, although encouraging, suggest that any effort to enhance coverage comprehensiveness in both Wikipedias should consider different strategies for engaging each editing community.
Tracking Fringe and Coordinated Activity on Twitter Leading Up to the US Capitol Attack
Authors: Vishnuprasad Padinjaredath Suresh , Nogara Gianluca , Cardoso Felipe , Cresci Stefano , Giordano Silvia , Luceri Luca
Published: May 2024

The aftermath of the 2020 US Presidential Election witnessed an unprecedented attack on the democratic values of the country through the violent insurrection at Capitol Hill on January 6th, 2021. The attack was fueled by the proliferation of conspiracy theories and misleading claims about the integrity of the election pushed by political elites and fringe communities on social media. In this study, we explore the evolution of fringe content and conspiracy theories on Twitter in the seven months leading up to the Capitol attack. We examine the suspicious coordinated activity carried out by users sharing fringe content, finding evidence of common adversarial manipulation techniques ranging from targeted amplification to manufactured consensus. Further, we map out the temporal evolution of, and the relationship between, fringe and conspiracy theories, which eventually coalesced into the rhetoric of a stolen election, with the hashtag #stopthesteal, alongside QAnon-related narratives. Our findings further highlight how social media platforms offer fertile ground for the widespread proliferation of conspiracies during and in the aftermath of major societal events, which can potentially lead to offline coordinated actions and organized violence.
Improving Quantification with Minimal In-Domain Annotations: Beyond Classify and Count
Authors: Von Däniken Pius , Deriu Jan Milan , Rodrigo Alvaro , Cieliebak Mark
Published: May 2024

Quantification is the task of estimating the class distribution in a given collection. With the growing availability of classification models, the use of classifiers for quantification has become increasingly popular, carrying the promise of eliminating the need for manual annotation. However, the naive classify and count approach presents clear limitations, especially evident in the face of domain discrepancies. In this work, we introduce two novel quantification methods, called CPCC and BCC, which can adapt to new target datasets with a small number of annotated in-domain samples (N = 100). To explore their real-world applicability, we apply our methods to a range of quantification tasks in the realm of hateful and offensive language, where they perform markedly better than classify and count and other existing methods.
Calibrate-Extrapolate: Rethinking Prevalence Estimation with Black Box Classifiers
Authors: Wu Siqi , Resnick Paul
Published: May 2024

In computational social science, researchers often use a pre-trained, black box classifier to estimate the frequency of each class in unlabeled datasets. A variety of prevalence estimation techniques have been developed in the literature, each yielding an unbiased estimate if certain stability assumption holds. This work introduces a framework to rethink the prevalence estimation process as calibrating the classifier outputs against ground truth labels to obtain the joint distribution of a base dataset and then extrapolating to the joint distribution of a target dataset. We call this framework "Calibrate-Extrapolate". It clarifies what stability assumptions must hold for a prevalence estimation technique to yield accurate estimates. In the calibration phase, the techniques assume only a stable calibration curve between a calibration dataset and the full base dataset. This allows for the classifier outputs to be used for disproportionate random sampling, thus improving the efficiency of calibration. In the extrapolation phase, some techniques assume a stable calibration curve while some assume stable class-conditional densities. We discuss the stability assumptions from a causal perspective. By specifying base and target joint distributions, we can generate simulated datasets, as a way to build intuitions about the impacts of assumption violations. This also leads to a better understanding of how the classifier's predictive power affects the accuracy of prevalence estimates: the greater the predictive power, the lower the sensitivity to violations of stability assumptions in the extrapolation phase. We illustrate the framework with an application that estimates the prevalence of toxic comments on news topics over time on Reddit, Twitter/X, and YouTube, using Jigsaw's Perspective API as a black box classifier. Finally, we summarize several practical advice for prevalence estimation.
Discovering Collective Narratives Shifts in Online Discussions
Authors: Zhao Wanying , Guo Siyi , Lerman Kristina , Ahn Yong-Yeol
Published: May 2024

Narratives are foundation of human cognition and decision making. Because narratives play a crucial role in societal discourses and spread of misinformation and because of the pervasive use of social media, the narrative dynamics on social media can have profound societal impact. Yet, systematic and computational understanding of online narratives faces critical challenge of the scale and dynamics; how can we reliably and automatically extract narratives from massive amount of texts? How do narratives emerge, spread, and die? Here, we propose a systematic narrative discovery framework that fill this gap by combining change point detection, semantic role labeling (SRL), and automatic aggregation of narrative fragments into narrative networks. We evaluate our model with synthetic and empirical data — two Twitter corpora about COVID-19 and 2017 French Election. Results demonstrate that our approach can recover major narrative shifts that correspond to the major events.
IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict
Authors: Chen Kai , He Zihao , Burghardt Keith , Zhang Jingxin , Lerman Kristina
Published: May 2024

The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled dataset-IsamasRed-comprising nearly 400,000 conversations and over 8 million comments from Reddit, spanning from August 2023 to November 2023. We introduce an innovative keyword extraction framework leveraging a large language model to effectively identify pertinent keywords, ensuring a comprehensive data collection. Our initial analysis on the dataset, examining topics, controversy, emotional and moral language trends over time, highlights the emotionally charged and complex nature of the discourse. This dataset aims to enrich the understanding of online discussions, shedding light on the complex interplay between ideology, sentiment, and community engagement in digital spaces.
NELA-PS: A Dataset of Pink Slime News Articles for the Study of Local News Ecosystems
Authors: Horne Benjamin D. , Gruppi Maurício
Published: May 2024

Pink slime news outlets automatically produce low-quality, often partisan content that is framed as authentic local news. Given that local news is trusted by Americans and is increasingly shutting down due to financial distress, pink slime news outlets have the potential to exploit local information voids. Yet, there are gaps in understanding of pink slime production practices and tactics, particularly over time. Hence, to support future research in this area, we built a dataset of over 7.9M articles from 1093 pink slime sources over 2.5 years. This dataset is publicly-available at https://doi.org/10.7910/DVN/YHWTFC.
Vitamin D status in children in the South Marmara Region in Turkey
Authors: Karaca Aynur , Altın Hakan
Published: May 2024

Aim: The aim of our study was to investigate the age, gender and seasonal differences in vitamin D levels in children and to determine the prevalence of vitamin D deficiency.
Materials and Methods: Between January 2022 and December 2023, the records of children aged 0-17 years who underwent serum 25-hydroxyvitaminD (25 (OH) D) levels in the pediatric outpatient clinics of our hospital were retrospectively reviewed. Children were divided into four groups according to age (0-1, 2-5, 6-11 and 12-17 years); three groups according to 25 (OH) D levels (vitamin D deficiency (below 12 ng/mL), vitamin D insufficiency (between 12-20 ng/mL) and vitamin D sufficiency (above 20 ng/mL)); and four groups according to seasons.
Results: The 25(OH)D levels of 41899 children, 19738 (47.1%) boys and 22161 (52.9%) girls, was 18.6 ng/mL (3-145); and was lower in girls than boys (16.8 ng/mL (3-136) in girls and 20.3 ng/mL (3-145) in boys p
La programación del contenido en Instagram como factor clave del Engagement en la Liga Asobal
Authors: Berraquero Rodríguez Diego , Baena González Rafael , Chavarría Ortíz Carlos , Gallardo Guerrero Ana María
Published: May 2024

Social networks for sports brands are a medium that allows very high levels of interaction with followers. A decade ago, Engagement, the indicator that measures the degree of user interaction with the brand, emerged. The aim of the study is to describe the degree of incidence of the scheduling of content, specifically the day and time factor, on the Engagement on Instagram of the Asobal League clubs. The sample consisted of 271 publications belonging to five clubs that competed during the 2023-24 season. The Spearman correlation test between the independent variable day factor and the dependent variable Engagement rate reflects a correlation coefficient whose p-value<0.01 so the correlation is not significant. On the other hand, the Spearman correlation equation between the hours of the day as the independent variable and the Engagement rate as the dependent variable gives a result of p-value>0.01 and confirms a significant and positive relationship. In practical terms, this study provides knowledge and recommendations to brand managers on factors that affect the Engagement rate in order to execute a correct digital marketing and communication strategy in sport.
Fútbol y adoctrinamiento: los casos De Austria, Alemania y Italia, 1920-1942. Un ejemplo de la barbarie deportiva
Authors: Arranz Albó Javier
Published: May 2024

In a World torn apart by the contradictions of classes, states and nationalities football functioned as and effective ideology of justification of the existing order in an environment dominated by the theories of social Darwinism and neo-Malthusian currents which defended inequality between men. A time when football practice was linked to bio political measures aimed at combating dysgenics factors, was a model to follow for national socialism. Football became a favourable scenario for the affirmation of collective identities of local and regional antagonisms establishing a metaphorical relationship between nation states and football.
Regarding the method used in the work we will preferably use to analysis of primary sources newspapers of the time and the analysis of scientific journals.
In our conclusions we will highlight the consequences of the ideological use of football occurred during this period, the indoctrination process, censorship and repression exerted of footballers, clubs and managers.
Monitoring and management of household waste in the village of Nova Praha: from unauthorized landfills to modern solutions
Authors:   Taras Shevchenko National University, Kyiv, Ukraine, Kuznetzova A. , Udovychenko V. ,   Taras Shevchenko National University of Kyiv, Kyiv, Ukraine
Published: May 2024

This publication addresses the problematic issues of unauthorized landfills, monitoring, and management of household waste using the example of the village of Nova Praha (Kirovohrad region), which is relevant and timely in the context of Ukraine's European integration and the implementation of the European waste management model. The object of this study is the unauthorized landfills in the village of Nova Praha, while the subject is the features of their location and the spatial planning aspects of handling, monitoring, and management. During the implementation of this study, a set of empirical (mapping, comparative analysis) and theoretical (synthesis, Annotationion) methods were applied. As a result of the monitoring conducted for subsequent mapping purposes, 9 household waste landfills and 6 construction waste landfills were identified within the relatively small area (50 km²) of the village. Their negative impact on the environment was established, and numerous violations of environmental legislation were detected. To address the suboptimal handling of household waste based on current legislative requirements, specific recommendations were developed for local authorities regarding the elimination of identified unauthorized landfills, the reclamation of disturbed lands, and the prevention of new ones. The practical significance of the developed recommendations lies in their potential use by local authorities to optimize the household waste management system in the interest of the sustainable development of the territorial community.
La interrelación entre colonialidad y decolonialidad en Palmeras en la nieve (2015)
Authors: Herrero Carmen
Published: May 2024

Tomando como punto de partida el marco teórico de la decolonialidad (Quijano, Mignolo, Maldonado-Torres y Walsh), este ensayo examina las complejas relaciones de poder que se presentan en el largometraje Palmeras en la nieve (2015), dirigido por Fernando González Molina, que está basado en la novela homónima de Luz Gabás. El artículo sostiene que el legado colonial que se proyecta en el drama épico del pasado pervive en la colonialidad que permea los conflictos dramáticos del presente histórico que se narra en el largometraje; una colonialidad que tiñe las actitudes ideológicas, éticas y culturales relacionadas con el pasado colonial español en Guinea Ecuatorial.
Communicating Resistance to the Banality of Online Crime Through Advertising
Authors: Haryono Cosmas Gatot , Reyes Rowena Capulong
Published: May 2024

Many Indonesians remain unaware of the perils of cybercrime and often perceive it as an inherent risk of engaging with social media platforms. Exploiting societal negligence and carelessness, criminals target individuals with alarming frequency. BCA, through its recent advertisement titled “Don’t Know, Give No!”, has introduced a novel approach to bank advertising in Indonesia. Researchers are intrigued by the discourse of resistance against online crime that BCA Bank seeks to foster through these advertisements. Employing qualitative methods informed by Van Dijk’s critical discourse analysis, this study delves into the nuanced strategies employed by BCA. The findings illuminate BCA’s endeavor to construct a discourse resisting the normalization of cybercriminal activities. BCA endeavors to encourage the public to fortify their digital defenses by adopting prudent and conscientious social media practices, advocating for a thoughtful approach prior to any online action. In this digital epoch, the responsibility for safeguarding personal data transcends traditional banking realms, with customer security becoming contingent upon individual actions and choices.
Exploring the prevalence and predictors of overweight and obesity in second cycle schools in Ghana
Authors: Amponsem-Boateng Cecilia , Oppong Timothy Bonney , Amertil Ninon P. , Boakye-Yiadom Jonathan , Kpongboe Doris Grace
Published: May 2024

Background: Overweight and obesity have become pressing global public health concerns, with their prevalence steadily increasing in many countries, including Ghana. The burden of these conditions is not limited to adults. Second-cycle schools play a crucial role in shaping the physical and mental well-being of students, making it imperative to understand the prevalence and predictors of overweight and obesity in this specific demographic. This study, therefore, seeks to estimate the prevalence of overweight and obesity and the predicting factors in students in second-cycle schools in Ghana.
Methods: A cross-sectional survey was conducted in selected second-cycle schools in the Eastern, Ashanti, and Brong Ahafo Regions of Ghana from January 31st, 2019 to December 20th, 2021. The sample was determined using an assumed minimum sample of 206 people with an overweight or obesity prevalence of 15%. Two schools were picked by lottery from the list of the three districts that were selected from each region. This investigation comprised six (6) second-cycle institutions.
Results: Overall, 1086 students participated in this study. 43.74% and 56.26% females. The prevalence of obesity and overweight was 28.17%. Overweight (24.95%) and obesity (3.1%).  16.20% (female) and 11.97% (male).  BMI (female-gender) and type of food (cooked at home) are predictive factors.
Conclusions: Predictive factors included BMI, gender (female), and homemade foods. Educating students and the general public on the risks of obesity and helping curb the misconception of overweight and obesity as a sign of ‘good living’ and beauty within Ghanaian communities is recommended.
Acceptability of mental health toolbox talk in primary health care service; a case study of Oshodi/ Isolo local government area, Lagos state, Nigeria
Authors: Silas Usen J. , Dotimi Doris A. , Oladunni Rukayat
Published: May 2024

Background: Mental health services at the primary healthcare level remain at their lowest ebb in most developing countries. Mental health toolbox talk is a way to solve mental health needs in the community. This study aimed at assessing the acceptability of mental health toolbox talk in primary health care (PHC), in Oshodi/Isolo, local government area (LGA).
Methods: This is a descriptive cross-section study conducted among 119 PHC staff. A multistage sampling technique was used to select participants for the study. Data were collected and analyzed using the statistical package for social sciences (SPSS) 26 and the EPI Info application.
Result: Findings revealed that the majority 79% of respondents were females and were within the age range of 40-60 years of age. Also, 76% of PHC staff showed poor knowledge of mental management, while 94% of the respondents accepted the integration of mental health toolbox talk into PHC. Findings revealed that professions (χ2=12.998, p<0.05) had a significant association with the respondents’ knowledge about mental health.  However, the analysis of variance shows there is a statistically insignificant difference in knowledge between the professional groups of PHC staff on the provision of mental health services in the PHC facilities, (f=0.616; p=0.688) (p>0.05).
Conclusions: Lack of knowledge on mental health was observed to mitigate PHC staff in the management of mental health disorders, and the need for training on mental health toolbox talk was determined.
Spatial hearing abilities among children with bimodal versus unilateral cochlear implant: parents report on spatial hearing questionnaire
Authors: Natarajsivam Ashwin Kumar , Thontadarya Suresh , Asuri Srividya , Babu Praveena
Published: May 2024

Background: Spatial hearing questionnaire (SHQ) is a questionnaire developed and validated for evaluating the spatial hearing abilities for the tasks of localization of sound to complex task of speech perception in noise. SHQ is adapted in Dutch language, and in Persian language. The study aimed to compare the spatial hearing abilities of children using bimodal cochlear implant and unilateral cochlear implant using spatial hearing questionnaire a subjective assessment tool, SHQ.
Methods: The participants of the study included parents of 30 cochlear implant using children of age range 5-10 years, among 30 participants, 16 belonged to parents of children using unilateral cochlear implant and 14 were parents of children using bimodal cochlear implants. SHQ consisted of 24 questions distributed under 8 domains and the responses from the parents were collected over telephonic interview and statistically analyzed across domains.
Results: The results revealed that there was no significant difference observed between both the groups across all the 8 domains, except perception of children’s voice, showed statistical difference between 2 groups. The baseline of spatial skills in children population using unilateral and bimodal cochlear implants was analysed.
Conclusions: Across the 8 domains of spatial hearing questionnaire there were no significant statistical difference found between unilateral and bimodal cochlear implanted groups.
Dental health status and treatment needs among the adult population of western Indian city: a cross-sectional study
Authors: Modi Yash , Desai Tilomi , Mandanka Srushti , Soni Princy , Patel Riddhi
Published: May 2024

Background: Oral health is a key indicator for general health, overall well-being and quality of life. This study evaluated the oral hygiene knowledge, practice and its association with oral problems and create awareness about oral health, oral diseases and its treatment to the people in Ahmedabad.
Methods: Sample size was estimated to be approximately 260 based on pilot study and by convenience sampling method. Chi-square test using SPSS statistical software.
Results: It was observed that around 74.22% of participants felt tooth pain, of which female were more expressive of their pain as compared to their male counterpart. Of which a staggering 73.07% participants brushed their teeth only once in a day. It was observed that there was a failure among population to use interdental aids for cleaning and maintaining oral hygiene. Approximately 70% of population visited their dentist not even once in the year.
Conclusions: It was observed and hence concluded on the basis of the derived results that there was a lack of appropriate oral health awareness among the local people in Ahmedabad even among the literate.
Pattern of mortality in a tertiary care teaching hospital according to the 11th Revision International classification of disease
Authors: Shankar Gowri , Kalburgi Eshwar B. , Tamilarasan Vetri Selvan
Published: May 2024

Background: Mortality is the oldest known health care indicator and a valuable tool for planning and managing in hospitals. Identifying the causes of mortality in hospital is important to monitor the health of the nations, identifying priorities and burden of disease analysis to attempt to estimate the causes of loss of healthy life. Hence this study was done to know the causes of death as per International Classification of Diseases (ICD) 11 among patients who died in Hangal Sri Kumareshwar Hospital in the year 2023.
Methods: A retrospective descriptive study was conducted from 01 January to 31 December 2023 in Hangal Sri Kumareshwar Hospital. All mortality data of the year 2023 in the hospital were included in the study. The content of format included demographic variables of patients on the patient record like age, sex, address, mortality and its causes and classified according to ICD 11. Collected data was analyzed using percentages and Chi-square test.
Results: Out of 505 deaths in Hangal Sri Kumareshwar Hospital and Research centre in 2023, 305 (60.40%) were males and 200 (39.60%) were females. Majority of deaths (31.48%) occurred between 41 to 60 years age group followed by 31.29% deaths occurring between 61 to 80 years age group. Regarding cause of death according to ICD-11, majority (24.16%) had a circulatory disease followed by 16.24% due to an infectious disease.
Conclusions: The National Health Program to combat non communicable diseases has been implemented and the 12th five-year plan has made specific provision for the management and control of non-communicable diseases.
ENSINO BASEADO EM NARRATIVAS: REPRESENTAÇÕES DE ALUNOS DE UM CURSO DE FISIOTERAPIA
Authors: Gonzalez Manso Maria Elisa , Jaber Lucato Jeanette Janaína , Claudino Barbosa Renata Cleia
Published: May 2024

Introdução: discussões sobre modificações no perfil do(a) egresso(a) dos cursos de Fisioterapia são hoje uma realidade, como expresso nas Diretrizes Curriculares Nacionais. A fim de conseguir essa mudança, foi gestado o projeto “Ensino baseado em Narrativas em um Centro Universitário localizado na cidade de São Paulo”. Graduandos(as) de Fisioterapia participaram do projeto durante os anos de 2018 e 2019. Objetivo: apresentar as representações que emergiram da participação dos(as) alunos(as), as quais coletaram narrativas de adoecimento de um grupo de pessoas com doenças crônicas. Metodologia: o corpus da pesquisa foi submetido ao software livre IRaMuTeQ® e à análise de conteúdo. Resultados: questões relacionadas com o protagonismo da pessoa ao narrar sua doença, as visões diferentes do que seria uma doença para o(a) educando(a) e o que o(a) paciente considera como tal, os sentimentos que a coleta de narrativas trouxe para estes(as) alunos(as), a escuta ativa e os sentimentos que puderam observar no(a) outro(a) foram destacadas. Comparações com o roteiro de entrevista tradicional e sentimentos de contratransferência puderam ser observadas. Conclusão: evidenciou-se que essa atividade trouxe reflexões importantes para esses(as) graduandos(as) no sentido da consecução do perfil expresso nas atuais diretrizes curriculares
TESTES CLÍNICOS, FUNCIONAIS E CONDUTAS FISIOTERAPÊUTICAS NA CONDROMALÁCIA PATELAR: UMA REVISÃO INTEGRATIVA
Authors: Azevedo Queiroz Natália Cristina , Gomes Leite Danillo , Correia da Silva Filho Ulbiramar , Da Silva Nora Fernanda Grazielle , Martins da Cunha Raphael
Published: May 2024

Introdução: condromalácia patelar é uma condição ortopédica que deteriora e amolece a articulação da patela. Introdução: Condromalácia Patelar (CMP) é uma condição ortopédica que deteriora e amolece a articulação da patela. Objetivo: apresentar os testes clínicos e funcionais utilizados no tratamento da CMP, verificando as condutas fisioterapêuticas para pacientes diagnosticados com essa lesão de joelho. Método: trata-se de uma revisão integrativa com as recomendações da estratégia PICO e escala PEDro. Foi explorada a base de dados PubMed, com demarcação temporal entre 2017 e 2023. Resultados: foram incluídos 12 artigos que avaliaram nos testes clínicos: dor, capacidade funcional, desempenho e equilíbrio. Conclusão: as condutas fisioterapêuticas envolvem: treinamento neuromuscular, alongamento, mobilizações, tração manual, fortalecimento do quadríceps, fortalecimento e estabilização do quadril e do joelho.
The Role of Echocardiography in the Diagnosis of Infective Endocarditis and its Complications
Authors: Choi Kang-Un , Nam Jong-Ho , Son Jang-Won
Published: May 2024

Infective endocarditis (IE) remains associated with high morbidity and mortality rates despite advances in medical technology, diagnostics, and treatment. Hospital mortality for IE reaches up to 22%, with a five-year mortality rate of 40%. Globally, the incidence of IE has increased, with recent figures indicating 13.8 cases per 100,000 annually, up from 1.5–11.6 per 100,000 reported between 1990 and 2010. A retrospective cohort study in a tertiary care center in Korea demonstrated a significant rise in the monthly incidence of IE from 2.0 in 2005 to 3.8 in 2017. This increase is attributed to the enhanced utilization of diagnostic tools such as echocardiography, computed tomography (CT), and positron emission tomography (PET). The 2015 European Society of Cardiology (ESC) guidelines incorporated complementary imaging modalities like Single Photon Emission Computed Tomography-CT (SPECT/CT), 18F-FDG PET/CT, and Cardiac CT, alongside traditional transthoracic and transesophageal echocardiography. The 2023 ESC guidelines further expanded these algorithms to include Cardiac CTA, MRI, PET/CT, and WBC SPECT. However, transthoracic, and transesophageal echocardiography remain the primary and essential diagnostic tools for suspected IE. This review examines the role of echocardiography in diagnosing IE, highlighting typical ultrasound findings, valve damage, and associated complications. Ultrasound is pivotal for early detection and prognosis of IE, identifying vegetations, abscesses, pseudoaneurysms, and fistulas. New vegetations typically appear irregular and oscillate, with transthoracic echocardiography detecting vegetations larger than 5mm with 90% sensitivity. Detailed echocardiographic assessment provides critical insights for the surgical management of IE and enhances the understanding of IE’s complex pathology.
Visualization of Pancreatic Tail Cancer with Liver Metastasis through Abdominal Transsplenic Scan
Authors: Kim Daejin , Ryu Hanjun , Kim Hyunsoo , Park Changkeun , Jung Jaekwon , Kim Jongmin , Song Jeonghoon , Lee Jaewon
Published: May 2024

Pancreatic cancer typically presents as a focal hypoechoic, hypovascular solid mass with irregular margins on ultrasound. Pancreatic tail disease can be difficult to detect on abdominal ultrasonography. A 75-year-old man visited our institution with upper abdominal pain. We successfully visualized a pancreatic tail mass on abdominal transsplenic scan and multiple liver masses via abdominal transverse scan. His diagnosis was confirmed as pancreatic tail cancer with liver metastasis following endoscopic ultrasound-guided fine-needle biopsy. Abdominal transsplenic scan proved valuable for diagnosing pancreatic tail disease because abdominal ultrasound has limited utility for evaluating pancreatic tail masses due to obscuration by bowel gas.