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In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]

Exponential growth in social media has led to the increasing popularity of hate speech and hate-based propaganda. Hate speech or malicious expression refers to the use of offensive, violent, or offensive language and its religious conduct with a specific group of people who share a common property, such as gender, ethnicity, race, or beliefs. Online hate diffusion has now become a serious problem as it creates a series of international initiatives aimed at defining problems and developing effective countermeasures; this study delves into the exploration of the intention of hate speech posting on social media, especially on Twitter. Both dramaturgical models of social interaction and cultivation theory were used to explain the hate speech culture phenomenon. A qualitative method is proposed for this study as part of the exploration. Results revealed that most of the previous studies on hate speech focused on the field of computer science but rarely on the communication field. The paper presents the results of past studies and shows the new proposed framework. The investigation suggests future directions for the problem and possible solutions; it starts with the background of the research, the explanation of the problem, the meaning of the research, and pursuing the research questions and goals of the research before finally explaining the limits. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) plays a pivotal role in modern life by enabling computers to comprehend, analyze, and respond to human language meaningfully, thereby offering exciting new opportunities. As social media platforms experience a surge in global usage, the imperative to capture and better understand the messages disseminated within these networks becomes increasingly crucial. Moreover, the occurrence of adverse events, such as the emergence of a pandemic or conflicts in various parts of the world, heightens social media users' inclinations towards these platforms. In this context, this paper aims to explore the scientific literature dedicated to the utilization of NLP in social media research, with the goal of highlighting trends, keywords, and collaborative networks within the authorship that contribute to the proliferation of papers in this field. To achieve this objective, we extracted and analyzed 1852 papers from the ISI Web of Science database. An initial observation reveals a remarkable annual growth rate of 62.18%, underscoring the heightened interest of the academic community in this domain. This paper includes an n-gram analysis and a review of the most cited papers in the extracted database, offering a comprehensive bibliometric analysis. The insights gained from these efforts provide essential perspectives and contribute to identifying pertinent issues in social media analysis addressed through the application of NLP. [ABSTRACT FROM AUTHOR]

Fake news is an explosive subject, being undoubtedly among the most controversial and difficult challenges facing society in the present-day environment of technology and information, which greatly affects the individuals who are vulnerable and easily influenced, shaping their decisions, actions, and even beliefs. In the course of discussing the gravity and dissemination of the fake news phenomenon, this article aims to clarify the distinctions between fake news, misinformation, and disinformation, along with conducting a thorough analysis of the most widely read academic papers that have tackled the topic of fake news research using various machine learning techniques. Utilizing specific keywords for dataset extraction from Clarivate Analytics' Web of Science Core Collection, the bibliometric analysis spans six years, offering valuable insights aimed at identifying key trends, methodologies, and notable strategies within this multidisciplinary field. The analysis encompasses the examination of prolific authors, prominent journals, collaborative efforts, prior publications, covered subjects, keywords, bigrams, trigrams, theme maps, co-occurrence networks, and various other relevant topics. One noteworthy aspect related to the extracted dataset is the remarkable growth rate observed in association with the analyzed subject, indicating an impressive increase of 179.31%. The growth rate value, coupled with the relatively short timeframe, further emphasizes the research community's keen interest in this subject. In light of these findings, the paper draws attention to key contributions and gaps in the existing literature, providing researchers and decision-makers innovative viewpoints and perspectives on the ongoing battle against the spread of fake news in the age of information. [ABSTRACT FROM AUTHOR]

The proliferation of misinformation presents a significant challenge in today's information landscape, impacting various aspects of society. While misinformation is often confused with terms like disinformation and fake news, it is crucial to distinguish that misinformation involves, in mostcases, inaccurate information without the intent to cause harm. In some instances, individuals unwittingly share misinformation, driven by a desire to assist others without thorough research. However, there are also situations where misinformation involves negligence, or even intentional manipulation, with the aim of shaping the opinions and decisions of the target audience. Another key factor contributing to misinformation is its alignment with individual beliefs and emotions. This alignment magnifies the impact and influence of misinformation, as people tend to seek information that reinforces their existing beliefs. As a starting point, some 56 papers containing 'misinformation detection' in the title, abstract, or keywords, marked as "articles", written in English, published between 2016 and 2022, were extracted from the Web of Science platform and further analyzed using Biblioshiny. This bibliometric study aims to offer a comprehensive perspective on the field of misinformation detection by examining its evolution and identifying emerging trends, influential authors, collaborative networks, highly cited articles, key terms, institutional affiliations, themes, and other relevant factors. Additionally, the study reviews the most cited papers and provides an overview of all selected papers in the dataset, shedding light on methods employed to counter misinformation and the primary research areas where misinformation detection has been explored, including sources such as online social networks, communities, and news platforms. Recent events related to health issues stemming from the COVID-19 pandemic have heightened interest within the research community regarding misinformation detection, a statistic which is also supported by the fact that half of the papers included in top 10 papers based on number of citations have addressed this subject. The insights derived from this analysis contribute valuable knowledge to address the issue, enhancing our understanding of the field's dynamics and aiding in the development of effective strategies to detect and mitigate the impact of misinformation. The results spotlight that IEEE Access occupies the first position in the current analysis based on the number of published papers, the King Saud University is listed as the top contributor for the misinformation detection, while in terms of countries, the top-5 list based on the highest contribution to this area is made by the USA, India, China, Spain, and the UK. Moreover, the study supports the promotion of verified and reliable sources of data, fostering a more informed and trustworthy information environment. [ABSTRACT FROM AUTHOR]

During times of war, displaced families carry various material items that later serve as means for preserving the memories of lost homes and maintaining a sense of identity. In divided Cyprus, the use of material objects by people displaced before and during the 1974 Turkish invasion has been influenced by the opening of checkpoints between the two sides in 2003. This paper explores how different generations in my family reacted to and interpreted the rediscovery of a lost material item—a handmade mirror piece—during the return journey. It discusses how my mother located the item, photographed it, and placed its photograph in the family's photographic archive. During the research project upon which this paper follows from, both items re-emerged through my mother's storytelling about her experience of return, transforming the project into a form of intergenerational transmission. The paper portrays how the storytelling about the mirror piece and its photograph was interpreted differently by me and my mother, influenced by the different politics of memory. The paper also showcases the resourcefulness of refugee families in maintaining the memory of their lost homes, which simultaneously reveals their views and hopes regarding the political future of the island. [ABSTRACT FROM AUTHOR]

Human trafficking thrives in the shadows, and the rise of social media has provided traffickers with a powerful and unregulated tool. This paper delves into how these criminals exploit online platforms to target and manipulate vulnerable populations. A thematic analysis of existing research explores the tactics used by traffickers on social media, revealing how algorithms can be manipulated to facilitate exploitation. Furthermore, the paper examines the limitations of current regulations in tackling this online threat. The research underscores the urgent need for collaboration between governments and researchers to combat algorithmic exploitation. By harnessing data analysis and machine learning, proactive strategies can be developed to disrupt trafficking networks and protect those most at risk. [ABSTRACT FROM AUTHOR]

This paper investigates the importance of digital presence for the emerging visual artists' careers. The study first examines how artists manage their digital presence and subsequently analyzes the impact of this presence on their careers by applying a model that consists of four objectives (branding, engagement, networking, and conversion). A qualitative method was employed, and interviews were conducted with illustrators at an early or emerging career stage. The findings demonstrate that an effective presence on digital platforms requires not only producing and showing artistic work but also managing an artistic brand, engaging with the audience, and making use of networking opportunities. It is also established that artists need to complement their digital presence with interactions in the physical world to increase the chances for advancing their careers. This paper follows the literature that studies the visual artist as brand manager and adds to the body of knowledge on how artists build successful careers. [ABSTRACT FROM AUTHOR]

Recent research has documented the relationship between the promotion of 'ideal', 'fit' bodies in social media, body image and associated body concerns and conditions. This article expands this scholarship, focusing specifically on gender, body disaffection and social media. Thus far, body disaffection has mostly been understood through a psychological framing, as a pathology residing within an individual and strongly associated with poor body image because of internalizing media images. In this paper, drawing on feminist new materialism, I offer a framing of body disaffection as a relational phenomenon. The paper draws on a mixed method study in England, with over 1000 young people examining their experiences with a range of digital health technologies. I focus specifically on their engagement with social media, to explore the relationship between ideal images and body concerns. Far from being a simple process of internalization of negative perceptions or image one has of their body, disaffection is formed through the body via a complex process of entanglement with social media and other elements. I outline how disaffection materialises as part of an assemblage of elements, including discourses, humans, bodies, digital objects and platforms. The paper reveals how entanglements with social media can generate powerful affects such as shame, pleasure and belonging along gendered lines, which may have significant implications for young people's relationships with their bodies. I analyse how social media events focused on the 'transformation' of bodies generate powerful affects, which open or limit capacities for what 'boys' or 'girls' bodies might become in deeply gendered and sometimes harmful ways. [ABSTRACT FROM AUTHOR]

Studies on social media (SM) and disaster management (DM) have mainly focused on the adaptation, application, and use of SM in each stage of DM. With the widespread availability and use of SM, the effective utilisation of SM in DM is impeded by various challenges but not yet comprehensively researched. Therefore, this paper aims to identify the challenges as well as the strategies to overcome the challenges and categorises them into a classified model. This study adopts a systematic literature review to present and analyse the challenges and strategies for using SM in DM. Utilising prominent databases, 72 publications were carefully selected and analysed qualitatively using content analysis. The findings revealed four key challenges to its users: the spread of misinformation; insufficient human resources to manage SM use; the lack of trust in information and authorities; and the poor information quality and content of messages. This study identified several strategies to overcome challenges, which can be classified into three sectors of the SM community: individuals, organisations, and SM companies. These findings contribute to enhancing the effective utilisation of SM in DM by community practitioners. Furthermore, this study provides insight into the current status of knowledge and identifies the research gaps around SM in DM for future research. [ABSTRACT FROM AUTHOR]

The era of digital capitalism poses conundrums for communication for development and social change scholarship and practice. On one hand, mainstream social media platforms are an increasingly ubiquitous element of the everyday media practices of growing portions of the global population. On the other, the profit-driven architectures can make these hostile spaces for progressive social change dialogues. While a burgeoning literature exists on the uses of social media as part of hashtag-activism and social movements, much less critical consideration has been given to NGOs' and civil society organizations' uses of capitalist-driven social media platforms in their development and social change efforts, and the challenges and compromises they navigate in this, consciously or not. This paper argues that meaningful uses of social media platforms for social change requires cultivating a hacker mindset in order to find tactics to subvert, resist, and appropriate platform logics, combined with an ecological sensibility to understanding media and communication. This paper analyzes how metaphors, specifically of a recipe, can offer a productive, praxis-oriented framework for fostering these sensibilities. The paper draws on insights from workshops with IT for Change, a civil society organization in India, which is both a leader in critiquing the political and economic power of Big Tech especially in the Global South, and beginning to use Instagram for its work on adolescent empowerment. [ABSTRACT FROM AUTHOR]

During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

This paper aims to test the hypothesis that the quality of social media bot detection systems based on supervised machine learning may not be as accurate as researchers claim, given that bots have become increasingly sophisticated, making it difficult for human annotators to detect them better than random selection. As a result, obtaining a ground-truth dataset with human annotation is not possible, which leads to supervised machine-learning models inheriting annotation errors. To test this hypothesis, we conducted an experiment where humans were tasked with recognizing malicious bots on the VKontakte social network. We then compared the "human" answers with the "ground-truth" bot labels ('a bot'/'not a bot'). Based on the experiment, we evaluated the bot detection efficiency of annotators in three scenarios typical for cybersecurity but differing in their detection difficulty as follows: (1) detection among random accounts, (2) detection among accounts of a social network 'community', and (3) detection among verified accounts. The study showed that humans could only detect simple bots in all three scenarios but could not detect more sophisticated ones (p-value = 0.05). The study also evaluates the limits of hypothetical and existing bot detection systems that leverage non-expert-labelled datasets as follows: the balanced accuracy of such systems can drop to 0.5 and lower, depending on bot complexity and detection scenario. The paper also describes the experiment design, collected datasets, statistical evaluation, and machine learning accuracy measures applied to support the results. In the discussion, we raise the question of using human labelling in bot detection systems and its potential cybersecurity issues. We also provide open access to the datasets used, experiment results, and software code for evaluating statistical and machine learning accuracy metrics used in this paper on GitHub. [ABSTRACT FROM AUTHOR]

Multimodal sarcasm detection is a developing research field in social Internet of Things, which is the foundation of artificial intelligence and human psychology research. Sarcastic comments issued on social media often imply people's real attitudes toward the events they are commenting on, reflecting their current emotional and psychological state. Additionally, the limited memory of Internet of Things mobile devices has posed challenges in deploying sarcastic detection models. An abundance of parameters also leads to an increase in the model's inference time. Social networking platforms such as Twitter and WeChat have generated a large amount of multimodal data. Compared to unimodal data, multimodal data can provide more comprehensive information. Therefore, when studying sarcasm detection on social Internet of Things, it is necessary to simultaneously consider the inter-modal interaction and the number of model parameters. In this paper, we propose a lightweight multimodal interaction model with knowledge enhancement based on deep learning. By integrating visual commonsense knowledge into the sarcasm detection model, we can enrich the semantic information of image and text modal representation. Additionally, we develop a multi-view interaction method to facilitate the interaction between modalities from different modal perspectives. The experimental results indicate that the model proposed in this paper outperforms the unimodal baselines. Compared to multimodal baselines, it also has similar performance with a small number of parameters. [ABSTRACT FROM AUTHOR]

In parallel with their counterparts in neighboring countries, specifically Slovakia, the Czech Republic, and Poland, Hungarian university students also exhibit a high level of involvement in internet activities, particularly within the realm of online social networking. Our quantitative data analysis, in line with our primary hypothesis, reveals a substantial proportion of university students displaying additional awareness also of academic online platforms (hereinafter: AOPs) specifically. These platforms, as per our typology, encompass the following categories: (1) academic online social networking sites; (2) databases lacking social features; and (3) author profiles linked to publishers. Notably, student awareness is most prominently affiliated with academic social networking sites offering comprehensive access to full-paper views. The data gathered in the framework of qualitative research was based on a contingent of 100 university respondents' (mostly female residents of the capital Budapest) answers to seven demographic and thematic questions The corresponding findings further indicate that, with the exception of a notable segment actively engaging with these AOPs, the majority of students exhibit sporadic usage patterns concentrated during specific seasonal peaks. At the same time, author profiles linked to publishers currently remain beyond the immediate reach of the broader student population. [ABSTRACT FROM AUTHOR]

Artificial intelligence-generated content (AIGC) technology has had disruptive results in AI, representing a new trend in research and application and promoting a new era of AI. The potential benefits of this technology are both profound and diverse. However, the benefits of generative tools are accompanied by a series of significant challenges, the most critical of which is that it may cause AI information pollution on social media and mislead the public. Traditional network security models have shown their limitations in dealing with today's complex network threats, so ensuring that generated content published on social media accurately reflects the true intentions of content creators has become particularly important. This paper proposes a security framework called "secToken". The framework adopts multi-level security and privacy protection measures. It combines deep learning and network security technology to ensure users' data integrity and confidentiality while ensuring credibility of the published content. In addition, the framework introduces the concept of zero trust security, integrates OAuth2.0 ideas, and provides advanced identity authentication, fine-grained access control, continuous identity verification, and other functions, to comprehensively guarantee the published content's reliability on social media. This paper considers the main issues of generative content management in social media and offers some feasible solutions. Applying the security framework proposed in this paper, the credibility of generated content published on social media can be effectively ensured and can help detect and audit published content on social media. At the operational level, when extracting key information summaries from user-generated multimodal artificial intelligence-generated content and binding them to user identity information as a new token to identify user uniqueness, it can effectively associate user identity information with the current network status and the generated content to be published on the platform. This method significantly enhances system security and effectively prevents information pollution caused by generative artificial intelligence on social media platforms. This innovative method provides a powerful solution for addressing social and ethical challenges and network security issues. [ABSTRACT FROM AUTHOR]

Crowdsourcing applications that integrate storytelling and geotagging capabilities offer new avenues for engaging the public in cultural heritage. However, standardised evaluation frameworks are lacking. This paper presents an applied evaluation methodology involving the analysis of relevant web-based tools. Towards this goal, this paper presents the development of crowdsourcing applications using, as a case study, the collection of myths and legends associated with the monumental heritage site of the Palace of the Grand Master of the Knights of Rhodes in Greece. Additionally, the paper presents an evaluation conducted through a criteria-based approach and user-based survey. The study reviews the concepts of crowdsourcing and digital storytelling within digital heritage, along with current concepts of living heritage and folklore, and examines relevant initiatives. The evaluation follows a four-stage methodology: (i) initial web-based tool selection based on the minimum requirements of web compatibility, crowdsourced data display, and map-based storytelling capability; (ii) application development using the selected web-based tools; (iii) a five-criteria assessment, based on the factors of open access, usability/tool support, participatory content/story creation, metrics provision and metadata model usage; and (iv) a crowd-based survey, indicating the most effective option. Findings from 100 respondents reveal limited exposure to participatory storytelling applications but interest in contributing content. Social media and influential figures serve as key channels for promoting crowdsourcing open calls. The results highlight gaps in understanding user expectations and perceptions, suggesting future research for gaining insights into engagement rates. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive analysis of the social media posts of prefectural governors in Japan during the COVID-19 pandemic. It investigates the correlation between social media activity levels, governors' characteristics, and engagement metrics. To predict citizen engagement of a specific tweet, machine learning models (MLMs) are trained using three feature sets. The first set includes variables representing profile- and tweet-related features. The second set incorporates word embeddings from three popular models, while the third set combines the first set with one of the embeddings. Additionally, seven classifiers are employed. The best-performing model utilizes the first feature set with FastText embedding and the XGBoost classifier. This study aims to collect governors' COVID-19-related tweets, analyze engagement metrics, investigate correlations with governors' characteristics, examine tweet-related features, and train MLMs for prediction. This paper's main contributions are twofold. Firstly, it offers an analysis of social media engagement by prefectural governors during the COVID-19 pandemic, shedding light on their communication strategies and citizen engagement outcomes. Secondly, it explores the effectiveness of MLMs and word embeddings in predicting tweet engagement, providing practical implications for policymakers in crisis communication. The findings emphasize the importance of social media engagement for effective governance and provide insights into factors influencing citizen engagement. [ABSTRACT FROM AUTHOR]

Green practices are social practices that aim to harmonize the relations between people and the natural environment. They may involve minimizing the use of resources and the generation of waste and emissions. Detecting green practices in social media posts helps to understand which green practices are currently common and to develop recommendations on the scaling of green practices to reduce environmental problems. This paper describes GreenRu, a novel Russian social media dataset for detecting the mentions of green practices related to waste management. It has a sentence-level markup and consists of 1326 posts collected in Russian online communities. The total number of mentions of green waste practices is 3765. The paper assessed the effectiveness of the multi-label and one-versus-rest BERT-based models for detecting the mentions of green practices in social media posts and compared several data augmentation methods in terms of both classification metrics and human evaluation. To augment the dataset, a backtranslation method and generative language models, such as RuGPT, RuT5, and ChatGPT, were used in this study. The results enable researchers to monitor the green waste practices on social networks and develop environmental policies. Additionally, GreenRu can support machine learning models to analyze social media content, assess the prevalence and effectiveness of green waste practices, and identify ways to expand them. [ABSTRACT FROM AUTHOR]

Social media platforms such as Twitter/X are increasingly important for political communication but the empirical question as to whether such communication enhances democratic consensus building (the ideal of deliberative democracy) or instead contributes to societal polarisation via fostering of hate speech and "information disorders" such as echo chambers is worth exploring. Political deliberation involves reciprocal communication between users, but much of the recent research into politics on social media has focused on one-to-many communication, in particular the sharing and diffusion of information on Twitter via retweets. This paper presents a new approach to studying reciprocal political communication on Twitter, with a focus on extending network-analytic indicators of deliberation. We use the Twitter v2 API to collect a new dataset (#debatenight2020) of reciprocal communication on Twitter during the first debate of the 2020 US presidential election and show that a hashtag-based collection alone would have collected only 1% of the debate-related communication. Previous work into using social network analysis to measure deliberation has involved using discussion tree networks to quantify the extent of argumentation (maximum depth) and representation (maximum width); we extend these measures by explicitly incorporating reciprocal communication (via triad census) and the political partisanship of users (inferred via usage of partisan hashtags). Using these methods, we find evidence for reciprocal communication among partisan actors, but also point to a need for further research to understand what forms this communication takes. [ABSTRACT FROM AUTHOR]

This scoping review aimed to investigate the association between depression and social media use among adolescents. The study analyzed 43 papers using five databases to identify articles published from 2012 to August 2022. The results revealed a connection between social media use and depression, as well as other negative outcomes such as anxiety, poor sleep, low self-esteem, and social and appearance anxiety. Surveys were the most used study strategy, with multiple common scales applied to assess depression, social media use, and other factors such as self-esteem and sleep quality. Among the studies, eight reported that females who use social media showed higher depression symptoms than males. This scoping review provides an overview of the current literature on the relationship between social media use and depression among adolescents. The findings emphasize the importance of monitoring social media use and providing support for individuals struggling with depression. However, more research is needed to better understand the factors contributing to this relationship and to develop more standardized assessment methods. [ABSTRACT FROM AUTHOR]

Over through the years, people have invested in stock markets in order to maximize their profit from the money they possess. Financial sentiment analysis is an important topic in stock market businesses since it helps investors to understand the overall sentiment towards a company and the stock market, which helps them make better investment decisions. Recent studies show that stock sentiment has strong correlations with the stock market, and we can effectively monitor public sentiment towards the stock market by leveraging social media data. Consequently, it is crucial to develop a model capable of reliably and quickly capturing the sentiment of the stock market. In this paper, we propose a novel and effective sequence-to-sequence transformer model, optimized using a sparse attention mechanism, for financial sentiment analysis. This approach enables investors to understand the overall sentiment towards a company and the stock market, thereby aiding in better investment decisions. Our model is trained on a corpus of financial news items to predict sentiment scores for financial companies. When benchmarked against other models like CNN, LSTM, and BERT, our model is "lightweight" and achieves a competitive latency of 10.3 ms and a reduced computational complexity of 3.2 GFLOPS—which is faster than BERT's 12.5 ms while maintaining higher computational complexity. This research has the potential to significantly inform decision making in the financial sector. [ABSTRACT FROM AUTHOR]

A Cyber–Physical–Social System (CPSS) is an evolving subset of Cyber–Physical Systems (CPS), which involve the interlinking of the cyber, physical, and social domains within a system-of-systems mindset. CPSS is in a growing state, which combines secure digital technologies with physical systems (e.g., sensors and actuators) and incorporates social aspects (e.g., human interactions and behaviors, and societal norms) to facilitate automated and secure services to end-users and organisations. This paper reviews the field of CPSS, especially in the scope of complexity theory and cyber security to determine its impact on CPS and social media's influence activities. The significance of CPSS lies in its potential to provide solutions to complex societal problems that are difficult to address through traditional approaches. With the integration of physical, social, and cyber components, CPSS can realize the full potential of IoT, big data analytics, and machine learning, leading to increased efficiency, improved sustainability and better decision making. CPSS presents exciting opportunities for innovation and advancement in multiple domains, improving the quality of life for people around the world. Research challenges to CPSS include the integration of hard and soft system components within all three domains, in addition to sociological metrics, data security, processing optimization and ethical implications. The findings of this paper note key research trends in the fields of CPSS, and recent novel contributions, followed by identified research gaps and future work. [ABSTRACT FROM AUTHOR]

The advancement of mobile internet technology has enabled companies to leverage social media for e-commerce, where some use personified images and language to communicate with consumers. This paper investigates how personified communication affects consumer behavior in social commerce and whether consumers are willing to accept this new form of communication. Specifically, the study explores consumers' willingness to accept personified communication in social commerce, considering the role of cognitive needs in regulating the internal mechanism. The paper proposes suggestions for enterprises to improve their social media communication and presents an improved model based on the Technology Acceptance Model (TAM). The model introduces perceived interaction as a new independent variable and adds cognitive needs as a regulatory variable, which is more suitable for social commerce. The study conducts a questionnaire survey online and analyzes the data using AMOS and SPSS. The results demonstrate that perceived usefulness and perceived interaction positively impact consumers' attitudes, which subsequently influences their willingness to purchase. Furthermore, cognitive needs as a regulatory variable significantly affect the path from perceived usefulness to attitude and purchase intention. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet and social media, false information, rumors, and misleading content have become pervasive, posing significant threats to public opinion and social stability, and even causing serious societal harm. This paper introduces a novel solution to address the challenges of fake news detection, presenting the "Rumor Large Language Models" (RumorLLM), a large language model finetuned with rumor writing styles and content. The key contributions include the development of RumorLLM and a data-augmentation method for small categories, effectively mitigating the issue of category imbalance in real-world fake-news datasets. Experimental results on the BuzzFeed and PolitiFact datasets demonstrate the superiority of the proposed model over baseline methods, particularly in F1 score and AUC-ROC. The model's robust performance highlights its effectiveness in handling imbalanced datasets and provides a promising solution to the pressing issue of false-information proliferation. [ABSTRACT FROM AUTHOR]

The research presented in this paper is the first to introduce a thorough Descriptive-Predictive–Prescriptive (DPP) Framework for comprehending the interaction between social media and cryptocurrencies. Recognizing the underexplored domain of the social-media–cryptocurrency interaction, we delve into its many aspects, better understanding present dynamics, forecasting potential future trajectories, and prescribing best solutions for stakeholders. We evaluate social media speech and behavior connected to cryptocurrencies using big data analytics, translating raw data into meaningful insights using Natural Language Processing (NLP) techniques like sentiment analysis. When applied to an experimental dataset, the DPP nets superior results compared to the baseline approach, displaying an improvement of 3.44% of the Root Mean Square Error (RMSE) metric and 4.59% of the Mean Absolute Error (MAE) metric. The unique DPP framework enables a more in-depth assessment of social media's influence on cryptocurrency trends, and lays the path for strategic decision-making in this nascent but rapidly developing field of study. [ABSTRACT FROM AUTHOR]

This paper delves into the challenges faced by scientists to effectively communicate regarding photoprotection and skin cancer as a result of the pervasive, harmful effects of disinforming messages. In order to do so, the Spanish population's understanding of photoprotection and skin cancer is examined. This paper is as an extension of the COMUNICANCER initiative, the ultimate goal of which is to establish protocols for producing and disseminating accurate content that raises the awareness of skin cancer-related dangers, as well as transferring knowledge on health prevention. Therefore, we have monitored the prevalence of misinformation and lack of information regarding sun photoprotection in Spain, aiming to reflect, ultimately, on the added difficulties faced by the scholarly community to disseminate accurate content in today's communication environment, which has become even more complex due to the distorting influence of disinformation. Employing a quantitative methodology, the research involved a comprehensive analysis of 2498 Spanish-language tweets related to skin cancer and photoprotection collected between August 2021 and August 2022. The study proves that scientists face a social media landscape, particularly on X/Twitter, where there is not only a lack of comprehensive information on the various dimensions of skin cancer, its prevention, and treatment, but which also serves as a breeding ground for the dissemination of inaccurate and misleading information regarding sun-related health risks and preventive measures. This leads to an urgent need to develop strategies aimed at fostering comprehensive and accurate information dissemination, especially regarding health information, due to the critical effect this can have on people and public health systems. [ABSTRACT FROM AUTHOR]

The process of determining and understanding the emotional tone expressed in a text, with a focus on textual data, is referred to as sentiment analysis. This analysis facilitates the identification of whether the overall sentiment is positive, negative, or neutral. Sentiment analysis on social networks seeks valuable insight into public opinions, trends, and user sentiments. The main motivation is to enable informed decisions and an understanding of the dynamics of online discourse by businesses and researchers. Additionally, sentiment analysis plays a vital role in the field of hate speech detection, aiding in the identification and mitigation of harmful content on social networks. In this paper, studies on the sentiment analysis of texts in the Slovak language, as well as in other languages, are introduced. The primary aim of the paper, aside from releasing the "SentiSK" dataset to the public, is to evaluate our dataset by comparing its results with those of other existing datasets in the Slovak language. The "SentiSK" dataset, consisting of 34,006 comments, was created, specified, and annotated for the task of sentiment analysis. The proposed approach involved the utilization of three datasets in the Slovak language, with nine classification methods trained and compared in two defined tasks. For the first task, testing on the "SentiSK" and "Sentigrade" datasets involved three classes (positive, neutral, and negative). In the second task, testing on the "SentiSK", "Sentigrade", and "Slovak dataset for SA" datasets involved two classes (positive and negative). Selected models achieved an F1 score ranging from 75.35% to 95.04%. [ABSTRACT FROM AUTHOR]

Social media platforms play a vital role in determining valuable tourist objectives, which greatly aids in optimizing tourist path planning. As data classification and analysis methods have advanced, machine learning (ML) algorithms such as the k-means algorithm have emerged as powerful tools for sorting through data collected from social media platforms. However, traditional k-means algorithms have drawbacks, including challenges in determining initial seed values. This paper presents a novel approach to enhance the k-means algorithm based on survey and social media tourism data for tourism path recommendations. The main contribution of this paper is enhancing the traditional k-means algorithm by employing the genetic algorithm (GA) to determine the number of clusters (k), select the initial seeds, and recommend the best tourism path based on social media tourism data. The GA enhances the k-means algorithm by using a binary string to represent initial centers and to apply GA operators. To assess its effectiveness, we applied this approach to recommend the optimal tourism path in the Red Sea State, Sudan. The results clearly indicate the superiority of our approach, with an algorithm optimization time of 0.01 s. In contrast, traditional k-means and hierarchical cluster algorithms required 0.27 and 0.7 s, respectively. [ABSTRACT FROM AUTHOR]

In the context of the increasing frequency of urban flooding disasters caused by extreme weather, the accurate and timely identification and monitoring of urban flood risks have become increasingly important. This article begins with a bibliometric analysis of the literature on urban flood monitoring and identification, revealing that since 2017, this area has become a global research hotspot. Subsequently, it presents a systematic review of current mainstream urban flood monitoring technologies, drawing from both traditional and emerging data sources, which are categorized into sensor-based monitoring (including contact and non-contact sensors) and big data-based monitoring (including social media data and surveillance camera data). By analyzing the advantages and disadvantages of each technology and their different research focuses, this paper points out that current research largely emphasizes more "intelligent" monitoring technologies. However, these technologies still have certain limitations, and traditional sensor monitoring techniques retain significant advantages in practical applications. Therefore, future flood risk monitoring should focus on integrating multiple data sources, fully leveraging the strengths of different data sources to achieve real-time and accurate monitoring of urban flooding. [ABSTRACT FROM AUTHOR]

Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning technology. Notably, computer vision solutions have significantly contributed to these achievements. Human face analysis, a core area of computer vision, has gained considerable attention due to its wide applicability in fields such as law enforcement, social media, and marketing. However, existing methods for facial age estimation often struggle with accuracy due to limited feature extraction capabilities and inefficiencies in learning hierarchical representations. This paper introduces a novel framework to address these issues by proposing a Multi-Stage Deep Neural Network (MSDNN) architecture. The MSDNN architecture divides each CNN backbone into multiple stages, enabling more comprehensive feature extraction, thereby improving the accuracy of age predictions from facial images. Our framework demonstrates a significant performance improvement over traditional solutions, with its effectiveness validated through comparisons with the EfficientNet and MobileNetV3 architectures. The proposed MSDNN architecture achieves a notable decrease in Mean Absolute Error (MAE) across three widely used public datasets (MORPH2, CACD, and AFAD) while maintaining a virtually identical parameter count compared to the initial backbone architectures. These results underscore the effectiveness and feasibility of our methodology in advancing the field of age estimation, showcasing it as a robust solution for enhancing the accuracy of age prediction algorithms. [ABSTRACT FROM AUTHOR]

The bipartite polarization problem is an optimization problem where the goal is to find the highest polarized bipartition on a weighted and labeled graph that represents a debate developed through some social network, where nodes represent user's opinions and edges agreement or disagreement between users. This problem can be seen as a generalization of the maxcut problem, and in previous work, approximate solutions and exact solutions have been obtained for real instances obtained from Reddit discussions, showing that such real instances seem to be very easy to solve. In this paper, we further investigate the complexity of this problem by introducing an instance generation model where a single parameter controls the polarization of the instances in such a way that this correlates with the average complexity to solve those instances. The average complexity results we obtain are consistent with our hypothesis: the higher the polarization of the instance, the easier is to find the corresponding polarized bipartition. In view of the experimental results, it is computationally feasible to implement transparent mechanisms to monitor polarization on online discussions and to inform about solutions for creating healthier social media environments. [ABSTRACT FROM AUTHOR]

Social networking services (SNSs) address estrangement in cyberspace through their communication, recognition, and resonance features, yet they simultaneously generate new forms of alienation. This dual effect can both advance and hinder sustainability. This phenomenon is particularly evident in Xiaohongshu (RED), a platform widely used in China and beyond, which has evolved from providing consumption guidance to facilitating lifestyle sharing. This paper utilizes Hartmut Rosa's Resonance Theory to explore resonance and alienation within RED, examining the multifaceted impact of SNSs on digital well-being and sustainability through qualitative methodologies, including netnography and thematic analysis. The study reveals that RED fosters material, spiritual, and social resonance, thereby enhancing digital well-being and sustainability. However, RED also generates alienation in these dimensions, which can impede sustainability. Moreover, the resonance and alienation generated by the RED platform affect not only its own sustainability but also broader environmental, social, economic, and cultural sustainability. Theoretically, this study constructs a sociological framework integrating resonance and alienation within SNSs to interpret digital well-being and sustainability. Practically, it offers recommendations for promoting sustainable practices among SNSs, addressing material, spiritual, and social dimensions. [ABSTRACT FROM AUTHOR]

The opinion recognition for comments in Internet media is a new task in text analysis. It takes comment statements as the research object, by learning the opinion tendency in the original text with annotation, and then performing opinion tendency recognition on the unannotated statements. However, due to the uncertainty of NLP (natural language processing) in short scenes and the complexity of Chinese text, existing methods have some limitations in accuracy and application scenarios. In this paper, we propose an opinion tendency recognition model HGAT (heterogeneous graph attention network) that integrates text vector and context structure methods to address the above problems. This method first trains a text vectorization model based on annotation text content, then constructs an isomorphic graph with annotation, news, and theme as its apex, and then optimizes the feature vectors of all nodes using an isomorphic graph neural network model with attention mechanism. In addition, this article collected 1,684,318 news items and 57,845,091 comments based on Toutiao, sifted through 511 of those stories and their corresponding 103,787 comments, and tested the impact of HGAT on this dataset. Experiments show that this method has stable improvement effect on different NLP methods, increasing accuracy by 2–10%, and provides a new perspective for opinion tendency recognition. [ABSTRACT FROM AUTHOR]

Flood modelling is among the most challenging scientific task because it covers a wide area of complex physical phenomena associated with highly uncertain and non-linear processes where the development of physically interpretive solutions usually suffers from the lack of recorded data. Exposure risk using 100- and 500-year special flood hazard areas, 1-10 ft of sea level rise scenarios and high-tide flooding and sensitivity using time series analyses of Landsat 8-derived multispectral indices were quantified. The overall outcome demonstrated the benefits of combined modeling systems in predicting the extent of flood inundation. The aim was to evaluate the predictive capacity of SWAT by comparisons with streamflow observations and to derive, using HECRAS, the flood inundation maps. [Extracted from the article]

Background: Social media is a ubiquitous phenomenon that serves as a tool for communication, collaboration, and creativity. In the long term, it has an indispensable place in the field of education. The research paper discusses the use of certain social networking sites where learning management systems are extensively and consistently used in the teaching and learning process at universities. The rapid transition to online platforms had a tremendous impact on the educational process, exposing and promoting the effectiveness of heutagogy. The circumstances made it necessary to emphasize the value of self-directed learning and the capacity to identify educational requirements and strategies for reaching study objectives. This kind of research corresponds to the increased need to develop desired self-study skills in students. Objectives: The goal of the paper is to demonstrate how undergraduate students perceive the trends in acceptance and the degree of appropriateness of social media for academic purposes. Methods: Research on social media use in education was inspired by a review of the literature. The purpose of the questionnaire is to track awareness, use, general satisfaction, communication, testing, and revision. The set of social media examined in the survey (years 2021 and 2022) was modified to reflect the social media landscape at the time. This study compares the findings of four different studies. The outcomes are displayed in graphs. Results: Two areas are analyzed: social media as a means of communication in the process of education and social media as a tool enabling students' revision and testing of study materials. The first two studies, which were conducted in 2014 and 2019, are based on the same set of examined social media and fit the comparison, enabling to define the trend in students' perceived satisfaction with social media for study purposes. The third and fourth studies discussed in this paper were run during the winter semesters of 2021 and 2022. Conclusions: The most significant differences between 2021 and 2022 can be seen in Instagram, Twitch, and other platforms, where a substantial drop in satisfaction in the communication field can be seen. The biggest difference in revision and testing is found in Facebook, Twitch, and Teams, which are abandoning online teaching and learning after the COVID-19 event. Social media use for studying has significantly decreased, with noteworthy decreases in Wiki, Facebook, and video calls. [ABSTRACT FROM AUTHOR]

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and X (formerly Twitter). [ABSTRACT FROM AUTHOR]

SocialMedia Marketing (SMM) has become a mainstream promotional scheme. Almost every business promotes itself through social media, and an educational institution is no different. The users' responses to social media posts are crucial to a successful promotional campaign. An adverse reaction leaves a long-term negative impact on the audience, and the conversion rate falls. This is why selecting the content to share on social media is one of the most effective decisions behind the success of a campaign. This paper proposes a Data-Driven Promotional Management System (DPMS) for universities to guide the selection of appropriate content to promote on social media, which is more likely to obtain positive user reactions. The main objective of DPMS is to make effective decisions for Social Media Marketing (SMM). The novel DPMS uses a well-engineered and optimized BiLSTM network, classifying users' sentiments about different university divisions, with a stunning accuracy of 98.66%. The average precision, recall, specificity, and F1-score of the DPMS are 98.12%, 98.24%, 99.39%, and 98.18%, respectively. This innovative Promotional Management System (PMS) increases the positive impression by 68.75%, reduces the adverse reaction by 31.25%, and increases the conversion rate by 18%. In a nutshell, the proposed DPMS is the first promotional management system for universities. It demonstrates significant potential for improving the brand value of universities and for increasing the intake rate. [ABSTRACT FROM AUTHOR]

The evolution of the media environment and the expanding use of social media pose a question about how the public sphere has changed in the last decade and what standards churches and religious communities can refer to in order to thrive and be present. It is vital to see that the nature of mass communication is different from the way religious ideas are conveyed. Consequently, there is a constant threat that, if mass media report on religions and religious activities, even with the greatest possible neutrality, they can easily falsify them. This paper sets out to explore this paradox; we examine the social media activity of a tradition-driven religious community. This research focuses on particular phenomena that lead to general assumptions. Despite the fact that the online reality is not the primary space of the researched community, its activity has moved towards cyber space because of the diminishing presence of interpersonal and group relationships. This paper analyses this community's Facebook presence by applying quantitative and qualitative methods. [ABSTRACT FROM AUTHOR]

In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection. [ABSTRACT FROM AUTHOR]

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. [ABSTRACT FROM AUTHOR]

In the era of social media, the influence of food exploration bloggers is increasingly apparent. Sharing their culinary experiences stimulates the audience's interest in visiting and consuming food destinations. This paper seeks to understand how the characteristics of food exploration bloggers on the Douyin platform influence audience perceptions of food and locations and how these perceptions may relate to visiting intentions, using the stimulus–organism–response (SOR) model. A cross-sectional online survey analyzed responses from 437 individuals interested in food exploration videos on Douyin. The results indicate that source credibility is significantly associated with the stimulation of taste desires and the formation of taste awareness. The audience's taste desire and taste awareness are positively linked to the intention to visit. This study contributes to the expansion of the SOR model's application in digital media, underscoring the substantial role of social media in influencing audience consumption intentions. It highlights that as an effective communication tool, social media can significantly impact users' behavioral responses and consumption decisions. [ABSTRACT FROM AUTHOR]

This study is a feature perception study of traditional village landscapes, refines the research method of traditional village landscape features using tourists online comment data, combs the landscape perception features of Xijiang Thousand Households Miao Village from tourists perspectives, digs out the managers' problems in landscape planning, protection, utilisation, and puts forward the corresponding improvement strategies. In this paper, the Octopus data collector was used to collect online evaluation data from nine target platforms, and ROST CM6 was used to analyse the data for word frequency analysis, semantic network analysis, and sentiment analysis. Finally, using Nvivo12, the data was coded and a multidimensional model of landscape perception was constructed: natural landscapes, cultural and social landscapes, perceived landscapes, seasonal landscapes, and services and infrastructures. The results show that in the landscape perception model, the cultural and social landscape modules are the core factors affecting tourists' psychological perceptions, with most tourists holding positive emotions towards the scenic spots and a few negative emotions mainly focusing on the relatively weak natural landscape, the unsatisfactory road system and charging system, and the over-commercialised development. In response to these problems, this study proposes corresponding planning recommendations. The results of the study reflect the trend of deep integration of culture and tourism in traditional villages in China, respond to the policy guidance of China's rural revitalisation policy, and provide useful references and insights for the landscape construction and development of the Xijiang Thousand Households Miao Village and even similar tourist-oriented traditional villages. [ABSTRACT FROM AUTHOR]

International heritage management approaches have developed into a more inclusive process wherein public participation is identified as a pivotal tool. Thus, determining how to assess public interests and include the public's ideas in heritage protection has become a technical issue, but relevant research still remains limited. This paper aims to test the digital footprints of social media users as a public participatory tool, with the objectives of identifying industrial heritage landscape attributes and assessing associated values. Targeting the Sanxian industrial heritage landscape of Liangdancheng in China as a case study, in this research the data from user-generated content on social media platforms Ctrip, Weibo, and Meituan were collected and processed with ROST CM 6 and NVivo 12, and content analysis (CA) and importance-performance analysis (IPA) were conducted. Results revealed that the industrial heritage landscape of Liangdancheng encompasses various built, cultural, and natural environmental resources, including both tangible and intangible attributes such as architectural constructions, historic artifacts, cultural events, and plants. These attributes were assessed and categorized into four quadrants of importance–performance characteristics, wherein cultural environmental resources show relatively high performance but built environmental resources need further actions to improve their value perception and interpretation among the public. This research demonstrated that the digital footprints of social media users as a participatory tool can work well in terms of data accessibility, value identification, and public representation, advancing the theoretical framework of Chinese industrial heritage management and global practices. [ABSTRACT FROM AUTHOR]

Ignorance, or the lack of knowledge, appears to be steadily spreading, despite the increasing availability of information. The notion of informed ignorance herein proposed to describe the widespread position of being exposed to an abundance of information yet lacking relevant knowledge, which is tied to the exponential growth in misinformation driven by technological developments and social media. Linked to many of societies' most looming catastrophes, from political polarization to the climate crisis, practices related to knowledge and information are deemed some of the most imminent and daunting modern threats, evidenced by the latest report of the World Economic Forum, which has named misinformation the most severe short-term global risk. This paper's epistemic perspective links the properties of today's information culture and the ways in which it interacts with individual capacities and limitations in current technological and socio-political contexts. Such a position is analyzed through the lens of epistemic principles as a contemporary epistemic phenotype that emerges from an environment of ill-adapted and excessive information inputs and leads to a distinctive type of social injustice that is primarily epistemic in nature. While equity and accessibility are widely discussed as important contributing factors to epistemic discrepancies, other overlooked but fundamental issues underlying epistemic injustices are considered, such as information manipulation, cognitive limitations, and epistemic degradation. To effectively face this elusive threat, we propose an inclusive viewpoint that harnesses knowledge from cognitive science, science and technology studies, and social epistemology to inform a unifying theory of its main impacts and driving forces. By adjusting a modern epistemic framework to the described phenomena, we intend to contextually outline its trajectory and possible means of containment based on a shared responsibility to maintain ethical epistemic standards. In a time of international unrest and mounting civil acts of violence, it is pertinent to emphasize the ethical principles of knowledge systems and authorities and suggest policy adaptations to maintain a social contract based on the shared values of truth and freedom. [ABSTRACT FROM AUTHOR]

The aim of this paper is to shape the identity of digital video advertising in the new digital media landscape, focusing on whether and to what extent popular digital ads on social media are differentiated from traditional television ads in the context of the convergence of traditional and new electronic media. Content analysis was preferred for the study of popular advertising content in social media. In this respect, the differentiation of digital video advertising from its television counterpart is examined in terms of the properties and effects of the transmission medium itself on the advertising content and of the features of the digital advertising content itself. Out of the findings emerged that digital video advertising is still in a phase of adaptation/transition, consisting of only a potential breakthrough in the contemporary media environment, since it has not yet exhausted the possibilities offered by the internet and Web 2.0. Currently, digital advertising only partially incorporates and exploits the advantages of enriched, multimedia, interactive, and personalized content, characteristics that would potentially differentiate it to a greater extent from advertising shown in traditional media, especially television. [ABSTRACT FROM AUTHOR]

Stance detection is a crucial task in natural language processing and social computing, focusing on classifying expressed attitudes towards specific targets based on the input text. Conventional methods predominantly view stance detection as a task of target-oriented, sentence-level text classification. On popular social media platforms like Twitter, users often express their opinions through hashtags in addition to textual content within tweets. However, current methods primarily treat hashtags as data retrieval labels, neglecting to effectively utilize the semantic information they carry. In this paper, we propose a large language model knowledge-enhanced stance detection framework (LKESD) for stance detection. LKESD contains three main components: an instruction-prompted background knowledge acquisition module (IPBKA) that retrieves background knowledge of hashtags by providing handcrafted prompts to large language models (LLMs); a graph convolutional feature-enhancement module (GCFEM) is designed to extract the semantic representations of words that frequently co-occur with hashtags in the dataset by leveraging textual associations; an a knowledge fusion network (KFN) is proposed to selectively integrate graph representations and LLM features using a prompt-tuning framework. Extensive experimental results on three benchmark datasets demonstrate that our LKESD method outperforms 2.7% on all setups over compared methods, validating its effectiveness in stance detection tasks. [ABSTRACT FROM AUTHOR]

Through critical discourse analysis of widely circulated and debated video speeches by three selected religious authorities in Ethiopia—representing the Ethiopian Orthodox Tewahedo Church (EOTC), Islam, and Protestantism—this paper examines how religious authority and social media complimentarily help to reach geographically and religiously diverse audiences and to draw politico-religious boundaries. It shows how the politicisation of religion, mainly by a supportive Protestant and calculative Muslim groups, with different intentions, on the one hand and a religiously motivated and repressive government on the other have created a "religious other" (i.e., the EOTC). Perceived discourse of historical marginalisation is used to justify both supportive and calculative tendencies of continued religious repression in the reconstructed new Ethiopia. On the other hand, a struggle for justice to curb this development is religiously justified by the EOTC, which elevates tensions to the level of a holy war against the religious other. [ABSTRACT FROM AUTHOR]