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Digital marketing has become vital to businesses' marketing strategies in today's technology and social media era. However, the effectiveness of digital marketing campaigns largely depends on accurately identifying the target audience. This study aims to implement the simulated annealing initiative algorithm for digital marketing, as well as audience classification and optimum target audience selection. Traditional methods of target audience identification, such as demographic, geographic, and psychographic segmentation, are only sometimes effective in identifying the most responsive audience. Therefore, advanced techniques such as clustering, genetic, and simulated annealing algorithms have been proposed to identify the optimum target audience. The heuristic simulated annealing algorithm is one of the most promising techniques for optimum target audience identification. It is widely used in combinatorial optimization problems and applied in various fields such as engineering, economics, management, and computer science. In this research, a digital marketing campaign is implemented for a new line to sell training courses in empowerment and competency in human resource management within the mining industry. After conducting market research, we have identified five critical segments: age, gender, income group, place of residence, and level of university education. The number of customers at each customer journey stage was 740 people in brand development, email, and advertising campaigns, of which 620 people are in the "Awareness" stage, 431 people in the "Interest" stage, 261 people in the "Consideration" stage, 203 people in the "Intend" stage, 179 people in the "Purchase" and finally, 179 People were evaluated in the "loyalty" stage for the case of educational service company. The results show we should target 20% of our marketing efforts towards the 18-24 age group, 30% towards females, 20% towards highincome individuals, 10% towards rural areas, and 20% towards University education level in BSc. The best cost per conversion we obtain is 78.105×106 Rials. The results show that the simulated annealing algorithm can be valuable for identifying the optimum target audience in digital marketing campaigns. By considering the entire customer journey and allowing for more complex audience targeting, the algorithm can help companies optimize their marketing strategies and maximize their profits. [ABSTRACT FROM AUTHOR]

Textual social media data have become indispensable to researchers' understanding of message strategies and other marketing practices. In a new departure for the field of brand communication, this study adopts and extends a semi-supervised machine-learning approach, guided latent Dirichlet allocation (LDA), which incorporates human insights into the discovery and classification of topics. We used it to analyze tweets from businesses involved with an emerging food technology, cultured meat, and delineated four key message strategies used by these brands: providing functional, educational, corporate social responsibility, and relational content. We further ascertained the relationships between brands and the key topics embedded in their Twitter data. A comparison of model performance suggests that guided LDA can be an advantageous alternative to traditional LDA, which is characterized by high efficiency and immense popularity among researchers, but—because of its unsupervised nature—yields findings that can be difficult to interpret. The present study therefore has critical theoretical and methodological implications for communication and marketing scholars. [ABSTRACT FROM AUTHOR]

This study introduces a novel framework for fast disaster event detection from social media, which incorporates active learning into Bi-directional Long Short-Term Memory (Bi-LSTM). In the face of increasing disaster-related information on social media platforms, our method addresses the challenge of efficiently processing vast, unstructured datasets to accurately extract crucial disasterrelated insights. Leveraging the contextual processing strengths of Bi-LSTM and the data efficiency of active learning, our approach significantly outperforms several baseline models in accuracy, precision, recall, and F1-score. We demonstrate that our method effectively balances these metrics, ensuring reliable disaster detection while minimizing false positives and negatives. The study also explores the impact of various model parameters on performance, offering insights for future optimizations. Despite its promising results, the study acknowledges limitations such as data quality, representativeness, and computational resource requirements. Future work will focus on enhancing data preprocessing, expanding language and scenario applicability, and integrating the model with additional technologies for broader disaster management applications. [ABSTRACT FROM AUTHOR]

Social media is a new emerging field in agricultural marketing, and its use is rapidly evolving, upgrading, and expanding. The aim of this study is to determine the attitudes of agricultural product producers in central Serbia regarding the use of social media in their business, with a focus on identifying the perception of the promotional potential of social networks themselves. The research included a final sample of 200 respondents. The basic hypothesis in this paper is that the internet as a technology for e-commerce, information, and advertising has great potential to improve the position of farmers and producers, but it is not fully utilized. The majority of agricultural producers still use Facebook as one of the main promotional channels. [ABSTRACT FROM AUTHOR]

Since their inception more than 15 years ago, social media have become a vibrant research topic in business and economics research. This article presents an integrative literature review taking stock of and showing the many faces of social media in extant research. Based on N = 1419 articles published in the leading peer‐reviewed business and economics journals in the years 2008–2022, we identify and describe seven overarching research themes, namely, social media as a: (1) market‐oriented interaction hub, (2) resource‐oriented interaction hub, (3) information market, (4) innovation and business venturing hub, (5) societal challenge, (6) political hub, and (7) data source. Finally, we derive a research agenda to stimulate future research on this increasingly important topic. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Production Engineering / Revista Brasileira de Engenharia de Produção is the property of Brazilian Journal of Production Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: With the increasing use of social media in operation and supply chain management (OSCM), it is of great importance for managers to consider risks in advance and take precautions against the risks that might arise from social media usage among supply chain members. The aim of this research is to identify and evaluate the risks related to the use of social media in OSCM. Design/methodology/approach: An initial research of the literature revealed that there is no detailed risk categorization in this area. Current taxonomies on the business risks of social media usage were examined and integrated with classifications identified in a Delphi study. The authors empirically demonstrate how the determined risks are prioritized and how decision-makers may decide to manage risks effectively based on the analytical hierarchy process (AHP) method. Findings: The findings of the research showed that reputation-associated risks such as criticism, language and loss of confidence should be prioritized over human, content and technical-associated risks. Originality/value: To date, a comprehensive approach to determine risks arising from using social media in OSCM is missing. With the Delphi and AHP techniques, the authors provide a novel insight for managers to mitigate risks. The outcomes of this study may assist executives in achieving successful management of social media usage in OSCM. Besides, the proposed AHP model may provide guidelines and direction in this regard. [ABSTRACT FROM AUTHOR]

This study aims to analyze the effect of the independent variables (media interactivity, recommending affordance, immersion experience, and credibility perception) on the dependent variable (intention to continue use). Respondents in this study focused on business partners who use airports service company social media. The data used in this study is primary data that comes directly from the object of research. The research sample was selected using a purposive sampling method in order to obtain 150 respondents as the sample. The data analysis used to test the hypothesis is Partial Least Square SmartPLS 4 software PLS-SEM. The results showed that the independent variables (media interactivity, recommending affordance, immersion experience, credibility perception) had a positive effect on the dependent variable (intention to continue use). The results of this study also provide information that the existence of social media for airport service companies will increase the company's initiative in making breakthroughs in innovation and development of its social media. In addition, social media increases the efficiency of a company, including reducing the burden of communication costs, and can also reduce research costs because social media makes it easy to conduct direct surveys of business partners. With social media, it can also increase the loyalty of retail business partners to airport service companies (brands) that they own. [ABSTRACT FROM AUTHOR]

Disinformation–false information intended to cause harm or for profit–is pervasive. While disinformation exists in several domains, one area with great potential for personal harm from disinformation is healthcare. The amount of disinformation about health issues on social media has grown dramatically over the past several years, particularly in response to the COVID-19 pandemic. The study described in this paper sought to determine the characteristics of multimedia social network posts that lead them to believe and potentially act on healthcare disinformation. The study was conducted in a neuroscience laboratory in early 2022. Twenty-six study participants each viewed a series of 20 either honest or dishonest social media posts, dealing with various aspects of healthcare. They were asked to determine if the posts were true or false and then to provide the reasoning behind their choices. Participant gaze was captured through eye tracking technology and investigated through "area of interest" analysis. This approach has the potential to discover the elements of disinformation that help convince the viewer a given post is true. Participants detected the true nature of the posts they were exposed to 69% of the time. Overall, the source of the post, whether its claims seemed reasonable, and the look and feel of the post were the most important reasons they cited for determining whether it was true or false. Based on the eye tracking data collected, the factors most associated with successfully detecting disinformation were the total number of fixations on key words and the total number of revisits to source information. The findings suggest the outlines of generalizations about why people believe online disinformation, suggesting a basis for the development of mid-range theory. [ABSTRACT FROM AUTHOR]

It has become increasingly difficult for local brands to compete with global brands, as most foreign brands are perceived as better than local brands. As such, local companies were highly suggested to change their public perception through appropriate marketing communications. Social media for business purposes can be optimized through social media marketing activities (SMMA) to shape positive customer attitudes and behaviors. This study aimed to examine the influence of SMMA on customer love for local brands and the effect of SMMA on increasing brand awareness and image as two essential antecedents for creating brand love. This research design used a survey and involved 200 customers and social media visitors of local brands. Data was analyzed using the WarpPLS software. The findings showed that SMMA could increase customer love for local brands through brand awareness and brand image. Research also found that SMMA can directly influence brand love. The influence of SMMA on brand awareness was more powerful than brand image. From a customer perception perspective, brand awareness and brand image were important conditions for strengthening brand love. The implication of this research is that local product companies should design and manage their marketing activities in social media based on the important criteria from the customer's perspective. [ABSTRACT FROM AUTHOR]

This work aims to identify the contribution of Social Innovation (SI) and Civil Society (CS) in the construction and development of Sustainable Social Businesses (SSB). In the first stage, a bibliometric analysis was conducted, tracking research from the last five years related to Social Innovation and the participation of Civil Society in Social Businesses. The CAPES Journals database was used, and based on the applied criteria, 53 articles from various sources were found. Subsequently, a Descriptive Bibliometric Review was carried out to gather preliminary data to identify the studies. This review considered the country of research application, the presence of SI, the participation of CS, and the type of Social Business. After identifying the cases studied in Brazil, totaling 4 articles, an Analytical Bibliometric Review of these studies was conducted to deepen the analysis and achieve the objective of this study. In this Analytical Review, the type and operation of SI, the level of CS involvement, and the identification of actors and the relevance of this link for business development were considered. The results of this study demonstrate the importance of SI for the continuation of development and profound changes in society. They also show that, like Business and Technological Innovation, SI is growing exponentially and is not limited to philanthropic activities but occupies spaces in significant economic activities. Regarding the achievement of the proposed objective, the results also show that the studied Social Businesses are intrinsically linked to SI and depend on the participation of CS, either as direct actors or as the target audience. This implies that these businesses rely on both these elements to exist and thrive. For future studies, it is relevant to measure the socio-environmental impacts generated by these Social Businesses in their surroundings and in society as a whole. [ABSTRACT FROM AUTHOR]

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. [ABSTRACT FROM AUTHOR]

The proliferation of fake images in today's digital landscape poses a significant threat to various domains, including media integrity, social media, and online security. Recognizing the urgent need to distinguish real images from their deceptive counterparts, this paper underscores the importance of developing a robust detection system. While substantial efforts have been made in the realms of computer vision and deep learning, the advent of Generative Adversarial Networks (GANs) has added a new layer of complexity to this challenge. In response to these evolving threats, we present a novel two-step methodology for detecting fake images, with a specific focus on those generated by GANs. Our approach harnesses the combined strengths of GANs and traditional Convolutional Neural Networks (CNNs), offering a comprehensive solution that significantly enhances accuracy in identifying both fake images and machine-generated fake images. The results of our experiments demonstrate the efficacy of our methodology. Using CNNs alone, we achieved a training accuracy of 87%. However, when employing the collaborative power of GANs and CNNs, our model exhibited a remarkable accuracy rate of 94.4%. This substantial improvement underscores the superiority of the GANs+CNN approach, suggesting its potential as a groundbreaking solution in the realm of fake image detection. This research opens up new horizons in fields such as media forensics, social media monitoring, and online security, where the ability to discern genuine content from manipulated or synthetic media is of paramount importance. The promising outcomes of this study not only provide an immediate and effective solution but also pave the way for further exploration and innovation in this critical area of digital security. [ABSTRACT FROM AUTHOR]

The continuous growth of social media is causing significant modifications in the business strategies developed by organizations. Using a structural equation modeling approach, this research analyzes how the work environment affects the social media managers thriving at work and task performance. The proposed model is tested using a sample of 190 social media managers and 190 supervisors from 190 companies in the tourism sector. The results highlight the importance of proper design and implementation of social media marketing planning and top management support to enhance both thriving at work and the performance of social media managers. This research contributes to the literature on social media by examining how and when the work environment influences the attitudes and performance of social media managers, whose role is crucial in organizational performance. Simultaneously, it expands the literature on thriving, as knowledge about the impact of contextual factors on thriving is still limited. The results also demonstrate that managers can compensate for the lack of certain contextual or personal resources with other resources, providing insights into when the work environment is more beneficial in shaping positive attitudes and behaviors in employees. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models. [ABSTRACT FROM AUTHOR]

In recent years, the usage of social networking sites has considerably increased in the Arab world. It has empowered individuals to express their opinions, especially in politics. Furthermore, various organizations that operate in the Arab countries have embraced social media in their day-to-day business activities at different scales. This is attributed to business owners' understanding of social media's importance for business development. However, the Arabic morphology is too complicated to understand due to the availability of nearly 10,000 roots and more than 900 patterns that act as the basis for verbs and nouns. Hate speech over online social networking sites turns out to be a worldwide issue that reduces the cohesion of civil societies. In this background, the current study develops a Chaotic Elephant Herd Optimization with Machine Learning for Hate Speech Detection (CEHOML-HSD) model in the context of the Arabic language. The presented CEHOML-HSD model majorly concentrates on identifying and categorising the Arabic text into hate speech and normal. To attain this, the CEHOML-HSD model follows different sub-processes as discussed herewith. At the initial stage, the CEHOML-HSD model undergoes data pre-processing with the help of the TF-IDF vectorizer. Secondly, the Support Vector Machine (SVM) model is utilized to detect and classify the hate speech texts made in the Arabic language. Lastly, the CEHO approach is employed for fine-tuning the parameters involved in SVM. This CEHO approach is developed by combining the chaotic functions with the classical EHO algorithm. The design of the CEHO algorithm for parameter tuning shows the novelty of the work. A widespread experimental analysis was executed to validate the enhanced performance of the proposed CEHOML-HSD approach. The comparative study outcomes established the supremacy of the proposed CEHOML-HSD model over other approaches. [ABSTRACT FROM AUTHOR]

Summary: Social media has become a significant part of daily life and nowadays it has started to turn out to be common practice for consumers and companies to employ social media to form, establish, and connect relationships. In addition, social media is based on web and mobile‐based technologies for creating highly interactive paradigms by which communities and individuals co‐create, share, modify and discuss user‐generated content. However, it is never easy for businesses to create social media advertising that attracts people and encourages them to buy their products. This article intends to present a statistical analysis that focuses on social media marketing among people of different ages. Six categories—personal profile, usage, assessment, higher‐order data, trustworthiness, and elements of social media ads—are included in the analysis of social media marketing. The initial phase focuses on questionnaire preparation depending on social media advertising. Subsequently, the planned questionnaire is collected from diverse respondents at various educational levels utilizing a Google form. Then, these respondents' responses are collected for examination. To determine whether the outcomes of the experiment are meaningful, the evaluation in this piece of study is based on an ANOVA analysis. [ABSTRACT FROM AUTHOR]

Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings. [ABSTRACT FROM AUTHOR]

The gaming industry has changed since advanced technologies and hardware specifications were able to create the possibility of gaming with connective network or known as online gaming. Some communities are accepting positively, and others are contradicted. Thus, there is an urgency to know the insight from their sentiment. The present research extracts 10 thousand sentiments at social media with sentiment text analysis. The text is interpreted into six based emotions namely anger, disgust, fear, joy, sadness, and surprise. The mining analysis by lexicon valance aware dictionary and sentiment reasoner (VADER) projects the Joy emotions as the domination result with 94.74%. Positive joy expression on online gaming is revealed by extracting the sentiment analysis. In total, ten thousand data were collected from the corpus, which resulted in the happy sentiments that were experienced. [ABSTRACT FROM AUTHOR]

Indonesian cuisine, especially Bakso, is one of the trendy foods that many travelers try to consume. Among the variations of Bakso, Bakso Malang is one of the most popular variants that is much talked about in social media. The present study analyzes the sentiment text mining of Bakso Malang business on post covid period from a natural language processing (NLP) social perspective. The data was taken from scraping to social media by "bakso malang" keywords. A total of 331 lexicon-filtering Tweets were gathered in the middle of March 2023. The corpus data were analyzed by valance aware dictionary and sentiment reasoner (Vader) sentiment analysis. K-means conducted the cluster of expressions. The result shows the variation of sentiments expressed in social media by anger, disgust, fear, joy, sadness, and surprise with 1.21%, 1.21%, 4.83%, 32.33%, 3.02%, and 57.40%, respectively. Many contents were discussed with keywords of "malang", "bakso", "enak", "makan", "foodfess2", "mau", and "pengen". Behavioural insight shows joy expression when discussing Bakso Malang. Further insights and recommendations are discussed in the paper. [ABSTRACT FROM AUTHOR]

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega‐populated country, we examined the importance of social media records in conservation decision‐making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF‐only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000–10,000 km2. Community efforts are needed to drive the implementation of the ambitious Kunming–Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up‐to‐date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms. [ABSTRACT FROM AUTHOR]

The targeted digital advertising infrastructures on which the business models of the social media platform economy rest have been the subject of significant academic and political interest. In this paper, we explore and theorise the appropriation of these infrastructures -- designed for commercial and political advertising -- by the state. In the U.K., public sector bodies have begun to repurpose the surveillance and messaging capacities of these social media platforms, along with the influencer economy, to deliver targeted behaviour change campaigns to achieve public policy goals. We explore how frameworks of behavioural government have aligned with Internet platforms' extensive infrastructures and the commercial ecologies of professionalised strategic marketing. We map the current extent of these practices in the U.K. through case studies and empirical research in Meta's Ad Library dataset. Although the networks of power and discourse within the ad infrastructure are indeed acting to shape the capacities of the state to engage in online influence, public bodies are mobilising their own substantial material networks of power and data to re-appropriate them to their own ends. Partly as a result of attempts by Meta to restrict the targeting of protected characteristics, we observe state communications campaigns building up what we term patchwork profiles of minute behavioural, demographic, and location-based categories in order to construct and reach particular groups of subjects. However, rather than a clear vision of a 'cybernetic society' of reactive information control, we instead find a heterogeneous and piecemeal landscape of different modes of power. [ABSTRACT FROM AUTHOR]

This research investigates the effect of museums applying advanced technology and employing social media and internet tools to achieve their organizational goals. Qualitative research was conducted focusing on museums in Jordan and Iraq. The findings strongly indicated that the usage of technology and internet tools in the contexts of museums in could increase visits and help curators spark an interest among young people. The findings are important since both Jordan and Iraq have a long and illustrious past dating back to the dawn of civilization, which is why it is particularly important to preserve their national heritage for future generations and reach out to young people. The results of this research demonstrate to curators the value of advanced technologies and a strong online presence in attracting visitors and engaging with the general public. [ABSTRACT FROM AUTHOR]

This study aimed to test the concept derived from resource-based and knowledge-based views to determine the success of digital transformation. The Industrial Revolution 4.0 has changed the way organisations survive and are adaptive in all rapidly changing environments, one of which is the use of social media in business. We investigated how SMEs in creative industries adapt their strategies, resources, and knowledge capabilities to achieve digital transformation through organisational agility. Data was collected from 269 SMEs in Indonesia using a questionnaire and analysed using the SEM (Structural Equation Modelling) statistical software SmartPLS. The results showed that organisational agility succeeded in mediating the influence of knowledge absorption capabilities, social media engagement, and digital resources in strengthening digital transformation. Achieving digital transformation can be successful if SMEs can use social media business and technology as their strategy and knowledge to survive and achieve competitive advantage. [ABSTRACT FROM AUTHOR]

In this paper, we analyze double modal use in American English based on a multi-billion-word corpus of geolocated posts from the social media platform Twitter. We identify and map 76 distinct double modals totaling 5,349 examples, many more types and tokens of double modals than have ever been observed. These descriptive results show that double modal structure and use in American English is far more complex than has generally been assumed. We then consider the relevance of these results to three current theoretical debates. First, we demonstrate that although there are various semantic tendencies in the types of modals that most often combine, there are no absolute constraints on double modal formation in American English. Most surprisingly, our results suggest that double modals are used productively across the US. Second, we argue that there is considerable dialect variation in double modal use in the southern US, with double modals generally being most strongly associated with African American Language, especially in the Deep South. This result challenges previous sociolinguistic research, which has often highlighted double modal use in White Southern English, especially in Appalachia. Third, we consider how these results can help us better understand the origins of double modals in America English: although it has generally been assumed that double modals were introduced by Scots-Irish settlers, we believe our results are more consistent with the hypothesis that double modals are an innovation of African American Language. [ABSTRACT FROM AUTHOR]

The transition from Industry 4.0 towards Industry 5.0 marks a paradigm shift, emphasising human-centricity in industrial settings. Industry 5.0 focuses on improving the future role of people in addition to merely technological progress. While "human centricity" gains recognition, ambiguity surrounds its definition and application. The literature lacks clear consensus on the concept and its industrial implications. This paper provides clarity on human-centricity by analysing viewpoints and public opinions based on posts published on LinkedIn in the last five years regarding human-centricity. The analysis involved text mining techniques, including semantic clustering to discover distinct clusters of discussions related to human-centricity and keywords extraction to tag the different clusters. The findings reveal that public opinion predominantly centres on the skills required by future workers, encompassing both hard and soft skills, as well as social themes such as gender equity and workplace comfort. This research underscores the critical relevance of these components in the transition towards Industry 5.0, offering valuable insights for industrial practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

The study aims to evaluate and compare the performance of various machine learning (ML) classifiers in the context of detecting cyber-trolling behaviors. With the rising prevalence of online harassment, developing effective automated tools for aggression detection in digital communications has become imperative. This research assesses the efficacy of Random Forest, Light Gradient Boosting Machine (LightGBM), Logistic Regression, Support Vector Machine (SVM), and Naive Bayes classifiers in identifying cyber troll posts within a publicly available dataset. Each ML classifier was trained and tested on a dataset curated for the detection of cyber trolls. The performance of the classifiers was gauged using confusion matrices, which provide detailed counts of true positives, true negatives, false positives, and false negatives. These metrics were then utilized to calculate the accuracy, precision, recall, and F1 scores to better understand each model's predictive capabilities. The Random Forest classifier outperformed other models, exhibiting the highest accuracy and balanced precision-recall trade-off, as indicated by the highest true positive and true negative rates, alongside the lowest false positive and false negative rates. LightGBM, while effective, showed a tendency towards higher false predictions. Logistic Regression, SVM, and Naive Bayes displayed identical confusion matrix results, an anomaly suggesting potential data handling or model application issues that warrant further investigation. The findings underscore the effectiveness of ensemble methods, with Random Forest leading in the cyber troll detection task. The study highlights the importance of selecting appropriate ML algorithms for text classification tasks in social media contexts and emphasizes the need for further scrutiny into the anomaly observed among the Logistic Regression, SVM, and Naive Bayes results. Future work will focus on exploring the reasons behind this occurrence and the potential of deep learning techniques in enhancing detection performance. [ABSTRACT FROM AUTHOR]

The advent of Information and Communication Technology (ICT) has profoundly impacted human behaviour, revolutionizing various aspects of life, including business operations. Rural Micro Entrepreneurs (RMEs) were said to gain significant economic benefits from the adoption of ICT in their business including increased productivity, sales, marketing, and ultimately higher income. However, not all rural entrepreneurs effectively leverage ICT advancements to enhance their businesses, prompting a need for further investigations. Derived on quantitative approach i.e., questionnaire guided interview processes, a series of field study have been conducted on March to April 2023, focusing on seven main rural pottery businesses from five villages in the Kuala Kangsar district, Perak. The primary objective is to assess the level of ICT usage among RMEs and identify the factors that motivate the utilization of ICT facilities. The study highlights that majority of respondents acknowledged the importance of ICT that allows flexibility to conduct business alongside with time and cost savings. By embracing ICT, RMEs in the study area not only improve their business efficiency, but also contribute to the overall local economic development. The findings of this study highlighted the opportunities for enhancing ICT adoption among RMEs for local economic development. [ABSTRACT FROM AUTHOR]

Social media has gained prevalence in most business fields, with LinkedIn being at the forefront of professional networking. We hereby aim to identify and analyze the factors affecting Turkish university students' understanding of professional social media and LinkedIn in a business context. The data was collected from 300 respondents using purposive sampling. The final dataset was taken to exploratory factor analysis which created three representative factors: companies' social media use, companies' LinkedIn use and LinkedIn concerns. Finally, comparative tests based on gender (female versus male) and school levels (freshmen, sophomore, junior and senior) variables applied to dataset. We conclude that students' understanding of social media and LinkedIn are quite vast and there is overall agreement as to the main uses and benefits of such platforms, both for individuals and companies. However, there is less agreement as to the concerns related to professional networking sites. This means that students are likely to ignore the threats and risks stemming from use of LinkedIn. From a practical point of view, recommendations refer to the inclusion in academic courses of references to information security, vulnerabilities and malicious attacks that can expose both private data and company knowledge. [ABSTRACT FROM AUTHOR]

Mental health disorders pose a significant global public health challenge. Social media data provides insights into these conditions. Analysing text can help identify indications of mental health disorders through text-based analysis. However, despite the large number of studies on the analysis of mental health disorders, the predominant algorithm in the existing literature is the Multi-Class Single-Level (MCSL) classification algorithm, which is often used for simple classification tasks involving a limited number of classes. Typically, these classes are binary, representing either an unhealthy or a healthy mental state. This paper uses English text data from Reddit to classify mental health disorders. The Multi-Class Multi-Level (MCML) classification algorithm was applied to perform detailed classification and address the limitations of the research scope using several approaches, including machine learning, deep learning, and transfer learning approaches. Two different pre-processing scenarios were proposed to handle unstructured text data, one of the most challenging aspects of classifying text from social media. The results of the experiments show that the MCML classification algorithm successfully performs detailed classification and produces promising results for each classification level. The proposed pre-processing scenario influences the performance of each classifier and improves classification accuracy. The best accuracy results were obtained for the Robustly Optimised BERT Pre-training Approach (RoBERTa) classifier at level 1 and level 2 classifications, namely 0.98 and 0.85, respectively. Overall, the MCML classification algorithm is proven to be used as a benchmark for early detection of text-based mental health disorders. [ABSTRACT FROM AUTHOR]

Blockchain was first only utilized to support cryptocurrencies, but as time passes, more and more industries are embracing this new technology. Several other crypto currencies started to emerge piecemeal from bitcoin. With the use of blockchain technology and smart contracts, Defy Systems increases the power of websites. Blockchain is expected to be used as an efficient method of conducting online transactions as technology matures. One of the diligences to which blockchain technology may be applied is crowdfunding sites. A brand-new and cuttingedge method for funding many kinds of bets is crowdfunding, in which the bets' individual writers can ask for money. With the advent of the internet and social media, new platforms emerged. For charitable organizations and business owners looking to grow their wealth, the internet and social media are crucial. In this study, it is proposed as a cutting-edge hybrid blockchain crowdsourcing platform called CrypFunds to further explore the benefits of merging blockchain and crowdsourcing. Cryptocurrency donations, whether they are made to a political party or an association, are frequently accepted in the Contributions in cryptocurrencies can give organizations access to a new group of implicit philanthropists who would rather give via blockchain than some of the more conventional methods that might enable them to avoid capital gains taxes. [ABSTRACT FROM AUTHOR]

Even after Elon Musk announced Twitter Blue, subscription services such as Snapchat+, YouTube Premium and Meta Verified, remain an uncritiqued aspect of social media. Within this article, subscription services on social media take centre stage. This article focuses on three key points: First, subscription services have remained uncritiqued because of the blind spot created by the emphasis on data collection. Second, the Social Media Subscription Model (SMSM) now asserts social media as part of a mixed model (Fuchs 2020, 134) instead of only being considered as an advertising model. Third, applying the classical Marxian twist of capitalism as self-negating, the SMSM is also a structural response to a contradiction of "peak data", meaning how does social media sustain itself if commodifies all data? Stressing the necessity of viewing the SMSM and social media as part of a Mixed Model. [ABSTRACT FROM AUTHOR]

Purpose: Entrepreneurs and individual sellers heavily leverage their social ties embedded in social media, expressive or instrumental, to penetrate the market and achieve business success. However, the extant social commerce literature offers limited understanding on how different forms of buyer−seller social ties embedded in social media affect buyers' purchase behaviors. The study draws on the theoretical lens of social ties and proposes an integrative theoretical framework to understand the direct and indirect influences of expressive and instrumental ties (ExTSM and InTSM) between buyers and sellers on buyers' purchase intention (PI) in social commerce. Design/methodology/approach: The authors first validated the measures of ExTSM and InTSM with survey data from 166 Weibo commerce buyers. They then tested their theoretical framework and hypotheses with survey data from 246 buyer−seller dyads in WeChat commerce. Findings: With a buyer-centric view, (1) ExTSM and InTSM, respectively, had a direct negative and a positive influence on PI; (2) both trust and perceived product value displayed inconsistent mediation effects on the negative relationship between ExTSM and PI; and (3) only perceived product value mediated the positive influence of InTSM on PI. From sellers' viewpoint, (1) their ExTSM and InTSM with buyers were mixed up, and (2) the mingled social ties negatively impacted buyers' purchase intention. Originality/value: The findings of the study advance the theoretical understanding of social commerce and offer practical guidance for small and medium-sized enterprises to effectively utilize social media for business purposes. [ABSTRACT FROM AUTHOR]

Purpose: Relying on social influence and sociomateriality theories, this research provides new insights about the social and material drivers relating to the sexualisation of online behaviour of social media influencers. Design/methodology/approach: Using a netnographic approach, observation data were gathered from the Instagram accounts of 20 influencers dedicated to beauty and fashion. In addition, 15 in-depth interviews were conducted with women adopting sexualisation practices online. The data were analysed using an abductive strategy; all materials were coded according to thematic analysis principles. Findings: The authors observe that sexualisation is a result of a complex system of social interactions encouraged and reinforced by multiple factors and actors. In particular, the authors outline the major influence of technology that has become a non-human authority defining implicit norms and shaping the beliefs and behaviours of women influencers. Originality/value: In contrast to existing literature which mainly focuses on the negative consequences of sexualisation, this work sheds some light on social constructs in social media. The authors contribute to the growing literature on social media influencers. Although many works focus on their persuasiveness, this work helps to better understand the social setting, motivations and pressures that are contained in social and technological contexts. [ABSTRACT FROM AUTHOR]

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This study aims to examine the importance of feature selection in machine learning, specifically in predicting user engagement with social media post photographs on university Facebook pages. The paper uses a thorough analysis to demonstrate the crucial significance of choosing suitable features and their corresponding algorithms. The research intends to demonstrate how this strategic approach affects the accuracy of prediction findings in social media interaction. The research presents a compelling case study involving 24 leading universities from Australia, the United Kingdom, and the United States. The results underscore the efficacy of the method, stressing that the meticulous selection of characteristics and the use of appropriate algorithms are crucial elements for attaining best results in social media forecasts. Implications: The study's results have important consequences, particularly within the changing environment of machine learning and its use in social media. Feature selection and algorithm choice are vital for optimizing social media initiatives for institutions. [ABSTRACT FROM AUTHOR]

The purpose of the study is to analyse the biological consideration in the publicity and marketing strategies for the pharmaceutical content on short video platforms from ethical perspectives. This study examines the impact of bioethics and social media content on the pharmaceutical awareness with the mediating role played by marketing strategies and moderating role played by online communication. The research method of the study is quantitative, and the researcher has collected data by adopting a questionnaire-based survey. Data has been analysed statistically with the help of a statistical software Smart PLS. Findings of the study revealed that bioethics and social media content significantly influence marketing strategies and pharmaceutical awareness, similarly marketing strategies and online communication has a significant impact on pharmaceutical awareness. The mediation of marketing strategies has been accepted as well as the moderation of online communication is significant. [ABSTRACT FROM AUTHOR]

This study systematically reviews the research on applying social media data (SMD) in business decision-making. We applied bibliometric mapping and a Latent Dirichlet Allocation topic modelling approach to conduct a systematic literature review. Results show that research to date has uncovered that sentiment analysis and opinion mining supported businesses in observing, analysing and predicting customer behaviour in various sectors. However, descriptive and predictive analyses are prevalent, while prescriptive analyses on SMD are rare. Our analysis highlights the need for future research to shed light onto newly discovered forms of SMD increasing the accuracy of sentiment detection with concept-level analysis. [ABSTRACT FROM AUTHOR]

Digitalization has inaugurated a new approach to publicize socialization in all types of organizations, generally, and businesses specifically. Stakeholders now favour companies that adhere to social standards and virtues. The companies comprehend that it is essential for the advertising of their CSR activities so that the viewing public can fully comprehend that they are engaged with a business that delivers facilities that benefit society. With the advancement of technology, cyberspace provides several portals upon which content can be posted. Once the viewers are engaged with the organization, the word is dispersed in such a way that the company garners prominence. Using such an added benefit, the company uses this framework to endorse the business to its full extent too. This is accomplished with the assistance of digital marketing. This paper emphasizes the marketing potential of CSR efforts for businesses and the necessity of social media advertising of such programs in the current technology era. This essay also discusses how using digital channels like social media, websites, blogs, and emails to promote CSR initiatives can be advantageous to the business in the long run. As a result, this article aims to connect the CSR principle to marketing by categorizing various theoretical viewpoints that suggest a connection between the two concepts. The present paper is an attempt to capture the extent of digitalisation of services and the subsequent impact on the CSR performances of the largest bank in the Indian sub-continent- State Bank of India. The paper tries to analyse the growth of CSR activities vis a vis the rate of digital services provided by the bank. [ABSTRACT FROM AUTHOR]

The digitization of goods and services is predicated on the production of value by contemporary organizations. What customers say about a firm on social media has become a significant part of the big data revolution. Furthermore, social media data analytics is a challenging topic to learn because of the partiality of text assessment and the extra variables. Social media data analytics using the deep learning (SMDA-DL) framework is proposed in this paper. A two-phase framework is suggested in the SMDA-DL framework: Pre-processing stage and selecting an efficient deep learning technique for this information is the emphasis of phase one; the second phase relies on existing components of large data structures and the model developed in phase one. First, small and large databases in non-big data sets will be analyzed in the first step of the suggested approach. While the first step of the deep learning model may be used to analyze massive data databases, the second stage is recommended. For the first layer of the architecture, there is a case study for assessing social media assessments. The proposed framework yields good simulation results in an accuracy ratio of 98.7%, precision ratio of 99.7%, recall ratio of 98.4% and F score of 98%. [ABSTRACT FROM AUTHOR]

This article examines the issue of whether social media companies should be held responsible for the outcomes of their recommendation algorithms. It discusses a case where Twitter was sued for allegedly allowing ISIS to use its algorithms for recruitment. The Supreme Court ruled that Twitter was not liable. The article argues that as algorithms play a more active role in content distribution, the idea that social media companies should be protected from liability should be reconsidered. It explores the history of Section 230 of the Communications Decency Act, which shields social media companies from liability, and suggests that algorithms may be regulated as commercial speech. The article concludes that social media companies should be held accountable for the consequences of their algorithms. [Extracted from the article]

Copyright of Revista Internacional de la Imagen is the property of Common Ground Research Networks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent times, it has been observed that social media exerts a favorable influence on consumer purchasing behavior. Many organizations are adopting the utilization of social media platforms as a means to promote products and services. Hence, it is crucial for enterprises to understand the consumer buying behavior in order to thrive. This article presents a novel approach that combines the theory of planned behavior (TPB) with machine learning techniques to develop accurate predictive models for consumer purchase behavior. This study examines three distinct factors of the theory of planned behavior (attitude, social norm, and perceived behavioral control) that provide insights into the primary determinants influencing online purchasing behavior. A total of eight machine learning algorithms, namely K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, Naive Bayes, Support Vector Machine, AdaBoost, and Gradient Boosting, were utilized in order to forecast consumer purchasing behavior. Empirical findings indicate that gradient boosting demonstrates superior performance in predicting customer buying behavior, with an accuracy rate of 0.91 and a macro F1 score of 0.91. This holds true when all factors, namely attitude (ATTD), social norm (SN), and perceived behavioral control (PBC), are included in the analysis. Furthermore, we incorporated Explainable AI (XAI), specifically LIME (Local Interpretable Model-Agnostic Explanations), to elucidate how the best machine learning model (i.e. gradient boosting) makes its prediction. The findings indicate that LIME has demonstrated a high level of confidence in accurately predicting the influence of low and high behavior. The outcome presented in this article has several implications. For instance, this article presents a novel way to combine the theory of planned behavior with machine learning techniques in order to predict consumer purchase behavior. This integration allows for a comprehensive analysis of factors influencing online purchasing decisions. Also, the incorporation of Explainable AI enhances the transparency and interpretability of the model. This feature is valuable for organizations seeking insights into factors driving predictions and the reasons behind certain outcomes. Moreover, these observations have the potential to offer valuable insights for businesses in customizing their marketing strategies to align with these influential factors. [ABSTRACT FROM AUTHOR]

In the digital age, social media has emerged as a significant platform, generating a vast amount of raw data daily. This data reflects the opinions of individuals from diverse backgrounds, races, cultures, and age groups, spanning a wide range of topics. Businesses can leverage this data to extract valuable insights, improve their services, and effectively reach a broader audience based on users' expressed opinions on social media platforms. To harness the potential of this extensive and unstructured data, a deep understanding of Natural Language Processing (NLP) is crucial. Existing approaches for sentiment analysis (SA) often rely on word co-occurrence frequencies, which prove inefficient in practical scenarios. Identifying this research gap, this paper presents a framework for concept-level sentiment analysis, aiming to enhance the accuracy of sentiment analysis (SA). A comprehensive Urdu language dataset was constructed by collecting data from YouTube, consisting of various talks and reviews on topics such as movies, politics, and commercial products. The dataset was further enriched by incorporating language rules and Deep Neural Networks (DNN) to optimize polarity detection. For sentiment analysis, the proposed framework employs predefined rules to trigger sentiment flow from words to concepts, leveraging the dependency relations among different words in a sentence based on Urdu language grammatical rules. In cases where predefined patterns are not triggered, the framework seamlessly switches to its sub-symbolic counterpart, passing the data to the DNN for sentence classification. Experimental results demonstrate that the proposed framework surpasses state-of-the-art approaches, including LSTM, CNN, SVM, LR, and MLP, achieving an improvement of 6–7% on Urdu dataset. In conclusion, this research paper introduces a novel framework for concept-level sentiment analysis of Urdu language data sourced from social media platforms. By combining language rules and DNN, the proposed framework demonstrates superior performance compared to existing methodologies, showcasing its effectiveness in accurately analyzing sentiment in Urdu text data. [ABSTRACT FROM AUTHOR]

The continent of Antarctica is one of the most challenging regions in terms of generating and updating geodata. Extensive research has been conducted on geodata acquisition, primarily focusing on earth observation satellites and local surveys in polar regions. Earth observation satellites offer limited spatial, temporal, and semantic data, while local surveys are constrained by polar regions' size and challenging conditions. This article delves into the crowdsourced data, aiming to significantly enhance geodata contribution in polar regions beyond current methods. To our knowledge, this study is the first of its kind to address social media data, a form of crowdsourcing, specifically for the Antarctic continent. Encompassing 5 years of social media data collection, the study uniquely presents original insights by investigating data reliability based on user activity levels and user movement consistency. The primary outcome of the study is that the activity level of users negatively correlates with spatial behavior consistency. This indicates that dominant user influence has led to inconsistent content manipulation. However, while the overall rate summary indicates a high inconsistency ratio for the active group, there still exists consistent behavior within these groups. A tight method to discriminate these reliable data generators with consistent behavior should be aimed as proposed in this study to prevent valuable data loss. This study contributes to reliable data scrutiny techniques from social media data in a general sense while providing a glimpse into the spatial quality of data generated specifically for Antarctica. This glimpse will enable future assessments of data collected in Antarctica for reliability checks and offer benefits in terms of processing workload and result accuracy in preprocessing steps for text, image, and spatial-based data processing. [ABSTRACT FROM AUTHOR]