Search Results (89)

Russia’s invasion of Ukraine has profoundly altered the global geopolitical landscape. Owing to its geographical proximity, the conflict has had a considerable impact on Europe. Marked by the professionalisation and democratisation of technology, it has underscored the growing significance of hybrid warfare, in which disinformation and propaganda serve as additional instruments of war. Within this context, the aim of this article is to examine the characteristics of false information related to the war between Russia and Ukraine in four European countries between 2022 and 2023. To this end, a content analysis of 297 hoaxes was conducted across eight fact-checking platforms, complemented by ten in-depth interviews with specialised professionals. The findings indicate that disinformation is characterised by viral audiovisual hoaxes, particularly on Facebook and X (formerly Twitter), with a notable surge in disinformation flows at the onset of the invasion. In the early months, misleading content predominantly consisted of decontextualised images of the conflict, whereas a year later, the focus shifted to narratives concerning international support and alliances. The primary objective of this disinformation is to polarise public opinion against a perceived common enemy. The conclusions provide a broader and more nuanced understanding of wartime disinformation within the European context. Full article

Background and Aim: Digital surveillance, which utilizes data from social media, search engines, and other online platforms, has emerged as an innovative approach for the early detection of infectious disease outbreaks. This scoping review aimed to systematically map and characterize the methodologies, performance metrics, and limitations of digital surveillance tools compared to traditional epidemiological monitoring. Methods: A scoping review was conducted in accordance with the Joanna Briggs Institute and PRISMA-SCR guidelines. Scientific databases including PubMed, Scopus, and Web of Science were searched, incorporating both empirical studies and systematic reviews without language restrictions. Key elements analyzed included digital sources, analytical algorithms, accuracy metrics, and validation against official surveillance data. Results: The reviewed studies demonstrate that digital surveillance can provide significant lead times (from days to several weeks) compared to traditional systems. While performance varies by platform and disease, many models showed strong correlations (r > 0.8) with official case data and achieved low predictive errors, particularly for influenza and COVID-19. Google Trends and X (formerly Twitter) emerged as the most frequently used sources, often analyzed using supervised regression, Bayesian models, and ARIMA techniques. Conclusions: While digital surveillance shows strong predictive capabilities, it faces challenges related to data quality and representativeness. Key recommendations include the development of standardized reporting guidelines to improve comparability across studies, the use of statistical techniques like stratification and model weighting to mitigate demographic biases, and leveraging advanced artificial intelligence to differentiate genuine health signals from media-driven noise. These steps are crucial for enhancing the reliability and equity of digital epidemiological monitoring. Full article

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. Full article

This work aims to use a suitable regression model to study a count response random variable, namely, the number of citations of a research paper, that is affected by some explanatory variables. The count variable exhibits substantial variation, as the sample variance is larger than the sample mean; thus, the classical Poisson regression model seems not to be appropriate. We concentrate our attention on the negative binomial regression model, which allows the variance of each measurement to be a function of its predicted value. Nevertheless, the process of citations of papers may be divided into two parts. In the first stage, the paper has no citations, while the second part provides the intensity of the citations. A hurdle model for separating documents with citations and those without citations is considered. The dataset for empirical application consisted of 43,190 research papers in the Economics and Business field from 2014–2021, which were obtained from The Lens database. Citation counts and social attention scores for each article were gathered from the Altmetric database. The main findings indicate that both collaboration and funding have positive impacts on citation counts and reduce the likelihood of receiving zero citations. Open access (OA) via repositories (green OA) correlates with higher citation counts and a lower probability of zero citations. In contrast, OA via the publisher’s website without an explicit open license (bronze OA) is associated with higher citation counts but also with a higher probability of zero citations. In addition, open access in subscription-based journals (hybrid OA) increases citation counts, although the effect is modest. There are clear disciplinary differences, with the prestige of the journal playing a significant role in citation counts. Articles with lower expert ratings tend to be cited less frequently and are more likely to be cited zero times. Meanwhile, news and blog mentions boost citations and reduce the likelihood of receiving no citations, while policy mentions also enhance citation counts and significantly lower the risk of being cited zero times. In contrast, patent mentions have a negative impact on citations. The influence of social media varies: X/Twitter and Wikipedia mentions increase citations and reduce the likelihood of being uncited, whereas Facebook and video mentions negatively impact citation counts. Full article

The integration of social intelligence mining with Large Language Models (LLMs) and unstructured social data can enhance land management by incorporating human behavior, social trends, and collective decision-making. This study investigates the role of social intelligence—derived from social media—in enhancing land use, urban planning, and environmental policy crafting. To map the structure of public concerns, a new algorithm is proposed based on contextual analysis and LLMs. The proposed method, along with public discussion analysis, is applied to posts on the X-platform (formerly Twitter) to extract public perception on issues related to land use, urban planning, and environmental policies. Results show that the proposed method can effectively extract public concerns and different perspectives of public discussion. This case study illustrates how social intelligence mining can be employed to support policymakers when used with caution. The cautionary conditions in the use of these methods are discussed in more detail. Full article

During natural disasters, social media platforms, such as X (formerly Twitter), become a valuable source of real-time information, with eyewitnesses and affected individuals posting messages about the produced damage and the victims. Although this information can be used to streamline the intervention process of local authorities and to achieve a better distribution of available resources, manually annotating these messages is often infeasible due to time and cost constraints. To address this challenge, we explore the use of semi-supervised learning, a technique that leverages both labeled and unlabeled data, to enhance neural models for disaster tweet classification. Specifically, we investigate state-of-the-art semi-supervised learning models and focus on co-training, a less-explored approach in recent years. Moreover, we propose a novel hybrid co-training architecture, Multihead Average Pseudo-Margin, which obtains state-of-the-art results on several classification tasks. Our approach extends the advantages of the voting mechanism from Multihead Co-Training by using the Average Pseudo-Margin (APM) score to improve the quality of the pseudo-labels and self-adaptive confidence thresholds for improving imbalanced classification. Our method achieves up to 7.98% accuracy improvement in low-data scenarios and 2.84% improvement when using the entire labeled dataset, reaching 89.55% accuracy on the Humanitarian task and 91.23% on the Informative task. These results demonstrate the potential of our approach in addressing the critical need for automated disaster tweet classification. We made our code publicly available for future research. Full article

Despite extensive scholarship on social media political party strategies or intra-party-political campaigns across digital platforms, it remains relatively unexplored how individual presidential candidates adopt social media to frame their messages on key political issues for voter engagement, especially in the West Africa region. To fill this gap, this study examines how the two major presidential candidates in Ghana, John Mahama of the National Democratic Congress (NDC) and Mahamudu Bawumia of the New Patriotic Party (NPP), use social media platforms to frame key political issues during the 2024 election campaign. Using framing theory and digital multimodal discourse analysis as the conceptual and methodological frameworks, the study examines content on X (formerly Twitter), Facebook, and Instagram, with a focus on issues related to the economy and education, while also assessing how platform-specific affordances shape the presentation and visibility of these frames. The findings of the study reveal three core dynamics in the framing strategies of both candidates: (1) contrasting economic narratives (‘Resetting Ghana’ vs. ‘It Is Possible’), (2) competing visions of education (reform vs. continuity), and (3) platform-specific engagement patterns. These findings offer insight into how political actors leverage digital affordances beyond simple messaging tools into structured framing mechanisms and strategically construct narratives to shape public discourse and influence voter engagement. Full article

On 8 September 2022, Queen Elizabeth II, the United Kingdom’s longest-serving monarch, died at Balmoral, aged 96. She had reigned for 70 years. The death of Queen Elizabeth II was met with mixed reactions worldwide. On the one hand, some mourners wanted to pay their last respects to the longest-ruling monarch in the world. On the other hand, disgruntled people wanted to remember and narrate the Queen’s legacy, including her role in British colonialism. The debates opened up conversations, questioning the British Royal Family’s relevance in today’s world, particularly in light of its largely unrevised colonial history. On X, debates were rife and played out much more fiercely. In this paper, the author undertakes a digital ethnography analysis of how Black Twitter worldwide received and responded to the death of Queen Elizabeth. The study found that Black Twitter reacted to the Queen’s death by (1) resisting respectability politics; (2) resisting the erasure of Black history in Britain and beyond; (3) educating Black people about their history. The study argues that Black Twitter is an essential digital space for people worldwide to mobilize and form racial identity politics. Full article

Journalists have incorporated social networks into their work as a standard tool, enhancing their ability to produce and disseminate information and making it easier for them to connect more directly with their audiences. However, this greater presence in the digital public sphere has also increased their exposure to harassment and hate speech, particularly in the case of women journalists. This study analyzes the presence of harassment and hate speech in responses (n = 60,684) to messages that 200 journalists and media outlets posted on X (formerly Twitter) accounts during the days immediately preceding and following the July 23 (23-J) general elections held in Spain in 2023. The results indicate that the most common forms of harassment were insults and political hate, which were more frequently aimed at personal accounts than institutional ones, highlighting the significant role of political polarization—particularly during election periods—in shaping the hostility that journalists face. Moreover, although, generally speaking, the total number of harassing messages was similar for men and women, it was found that a greater number of sexist messages were aimed at women journalists, and an ideological dimension was identified in the hate speech that extremists or right-wing populists directed at them. This study corroborates that this is a minor but systemic issue, particularly from a political and gender perspective. To counteract this, the media must develop proactive policies and protective actions extending even to the individual level, where this issue usually applies. Full article

This study examines the dynamics of information dissemination and opinion formation in Indonesian social media through a comprehensive analysis of a high-profile youth violence case. Using social network analysis (SNA), we analyzed 264,155 activities from 83,097 accounts on platform X (formerly Twitter) to understand the patterns of information flow, cluster formation, and inter-group interactions. The analysis revealed four distinct clusters with unique characteristics: a dominant support cluster (40.12%), a context-focused cluster (26.93%), a mainstream media cluster (14.14%), and a peripheral engagement cluster (6.05%). This study found significant patterns in information dissemination, with retweets dominating at 68% of total activities and strategic hashtag usage at 28%. Cross-cluster interactions comprised 20% of total activities, challenging assumptions about echo chambers in digital discourse. The network showed high resilience with 85% path reliability and demonstrated a consistent multiplier effect with a 1:5:15 ratio in message amplification. Bridge nodes (10–15% of accounts) played crucial roles in facilitating cross-cluster dialogue and maintaining network cohesion. The temporal evolution of discourse showed distinct phases, from initial factual reporting to later systemic analysis, with each phase characterized by different engagement patterns and narrative focuses. These findings extend existing theoretical frameworks while highlighting the need for more culturally nuanced approaches to understanding digital discourse in contexts of collectivist cultural dimensions. This study’s results have significant implications for digital literacy education, social media intervention strategies, and youth violence prevention efforts, suggesting the need for sophisticated, network-aware approaches that consider both structural dynamics and cultural contexts. Full article

Social media (SM) platforms allow users to communicate rapidly, exchange information, and create and share real-time content. Currently, 4.5 billion people use social media worldwide, making it an influential part of daily life. Beyond information sharing, social media facilitates communication, transfers information, and serves as a platform for advertising and shaping public opinion. Researchers analyse these aspects to understand and describe societal realities. The primary purpose of this paper is to analyse social media’s impact on global research. The research included an analysis of the most popular social platforms, considering the number of Web of Science (WoS) articles relating to them and the year in which the platform was established or the Monthly Active Users (MAU) factor. Data were collected based on the WoS database in the topic (which contains texts of title, abstract, author keywords, and Keywords Plus) of the articles, where phrases containing names of SM platforms were used. Quantitative research is a type of research that analyses data numerically to find relationships and statistical regularities of searched phrases. The impact of social media on the dissemination of research and findings was analysed based on the results of the study and also on the literature data. This research reveals a lack of correlation between the number of articles indexed in the WoS and the MAU of individual social media platforms. This observation raises an important question: do social media researchers focus on studying the platforms used by the majority, thereby providing a more accurate representation of current social dynamics? This article is helpful for researchers, policymakers, and social media platform developers seeking to understand the role of social media in shaping modern communication and public discourse. The most important finding of the paper is the low correlation between the number of SM users and the impact of social media platforms on learning, as exemplified by the Twitter (Note: Twitter was an American social networking service rebranded as X in 2023. As the period of data analysed in this paper covered the years up to 2022, the authors decided to stay with the name Twitter) platform, which is the 17th largest SM platform but is the 2nd (after Facebook) in implications for science. Full article

Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. Full article

This open-source-based article presents an automated method for identifying and tracing popular Salafi discussions online. The novelty of this method lies in its inter-disciplinary approach developed through collaboration among experts in the fields of the Middle East, Islamic studies, and computer science. The computerized model presented here harnesses machine learning techniques to accurately identify popular Salafi writings on social media and to distinguish them from the writings of Muslims from other denominations. Creating an AI-supported model to distinguish between writings on social media that pertain to two different Islamic denominations is a highly difficult task. Based on this machine learning model and the methodology that it implements, the study presented here identifies United Kingdom-based Twitter accounts that embody Salafi thinking (even if they do not utilize terminology that is manifestly Salafi) and, based on that identification, analyzes and characterizes the United Kingdom-based Salafi community on Twitter. Unlike other machine learning ideology-related studies that are focused on Salafi-jihadism, the present research is focused on quietist Salafism (Salafi-taqlidis) in the United Kingdom. The purpose of this study is to examine the virtual Salafi community in the United Kingdom, with a focus on identifying the key issues of concern to its members and assessing the influence of global Salafi trends within this UK-based community. Full article

Media platforms provide an effective way to gauge public perceptions, especially during mass disruption events. This research explores public responses to the 2023 Ohio train derailment event through Twitter, currently known as X, and Google Trends. It aims to unveil public sentiments and attitudes by employing sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and topic modeling using Latent Dirichlet Allocation (LDA) on geotagged tweets across three phases of the event: impact and immediate response, investigation, and recovery. Additionally, the Self-Organizing Map (SOM) model is employed to conduct time-series clustering analysis of Google search patterns, offering a deeper understanding into the event’s spatial and temporal impact on society. The results reveal that public perceptions related to pollution in communities exhibited an inverted U-shaped curve during the initial two phases on both the Twitter and Google Search platforms. However, in the third phase, the trends diverged. While public awareness declined on Google Search, it experienced an uptick on Twitter, a shift that can be attributed to governmental responses. Furthermore, the topics of Twitter discussions underwent a transition across three phases, changing from a focus on the causes of fires and evacuation strategies in Phase 1, to river pollution and trusteeship issues in Phase 2, and finally converging on government actions and community safety in Phase 3. Overall, this study advances a multi-platform and multi-method framework to uncover the spatiotemporal dynamics of public perception during disasters, offering actionable insights for real-time, region-specific crisis management. Full article

The pervasive use of social media platforms, such as X (formerly Twitter), has become a part of our daily lives, simultaneously increasing the threat of cyber attacks. To address this risk, numerous studies have explored methods to detect and predict cyber attacks by analyzing X data. This study specifically examines the application of AI techniques for predicting potential cyber threats on X. DeepNN consistently outperforms competing methods in terms of overall and average figure of merit. While character-level feature extraction methods are abundant, we contend that a semantic focus is more beneficial for this stage of the process. The findings indicate that current studies often lack comprehensive evaluations of critical aspects such as prediction scope, types of cybersecurity threats, feature extraction techniques, algorithm complexity, information summarization levels, scalability over time, and performance measurements. This review primarily focuses on identifying AI methods used to detect cyber threats on X and investigates existing gaps and trends in this area. Notably, over the past few years, limited review articles have been published on detecting cyber threats on X, especially those concentrating on recent journal articles rather than conference papers. Full article