Search

Sharing experience via social media communication has been examined in psychology, sociology, communication and linguistic studies. While this research has mostly examined consumption experience and health issues, how people make meanings and develop interactions while sharing mundane matters needs more investigation. This paper reports on a pragmatic investigation into individuals sharing their exam experiences in social media groups. Data were collected from two support groups on Douban , a Chinese social networking site that encourages users to share their thoughts, interests and experiences. A total of 400 original posts, 200 each from a group sharing experiences of failure in examinations and a group sharing their success in examinations, were collected and analysed both quantitatively and qualitatively. The findings showed that the two groups were significantly different in the ways they voluntarily disclosed their identities and in the types of expressions they employed to refer to different participants. In addition, they differed significantly in how they sought or offered support in interactions. Similarities and differences between the two groups' sharing are discussed in relation to the instructions of the two groups, their identity construction, the affordances of social media, and the importance of politeness and face in Chinese culture. • This paper reports on a pragmatic investigation into individuals sharing their exam experiences in social media groups. • The failure and the success groups were significantly different in the ways they voluntarily disclosed their identities. • They were also significantly different in the types of expressions used to refer to different participants. • They differed significantly in how they sought or offered support in interactions. [ABSTRACT FROM AUTHOR]

Hate Speech already been phenomenal expansion over the past decade. The paper proposed a new model that combines advanced CNN and Bidirectional Encoder Representations from Transformers (BERT) context embedding to predict hate speech in social media. This research trained contextual embedding on the datasets and used the learned information to identify objectionable language and hate speech in text. The paper evaluated supervised machine learning classifiers for bigoted and offensive content on Twitter using two datasets and found that advanced CNN context embeddings produced superior results. This research generated optimistic outcomes, which achieves 73% F1-score for Davidson dataset and 56% F1-score for TRAC-1 dataset [ABSTRACT FROM AUTHOR]

• The challenges of digitalizing the work by open, participatory social media platforms are discussed. • The notion of platform-level alignment (vs. drifting) is proposed. • Three elements of digital work are articulated (digital infrastructure work, digital strategy work, and aligning work). • The process of platform drifting is constructed based on the coevolution of digital work elements. • The implications of platform drifting for organizational transformation are discussed. Organizations are increasingly digitalizing the work associated with information exchange by using enterprise social media. However, social media's openness to outsider users poses significant challenges to maintaining alignment between social media logic (or platform logic that guides decisions and actions on the platform) and the dominant organizational logic. Through an in-depth, longitudinal, abductive study of three successive social media implementations in a single organization, I explore the process of maintaining (or losing) social media alignment and its long-term consequences on the nature of the work. The paper shows that digitalization exposes the work to continuous adjustment within and across three elements of digital work: digital infrastructure work (embracing new uses of the platform at the user level), digital strategy work (redesigning governance policies), and aligning work (fitting uses with the platform logic underlying digital work). The findings show that despite initial social media alignment, through continuous coevolution of these three elements, the platform logic underlying digital community work eventually drifted away from supporting the organization's original logic of cohesion to supporting an alternative logic of inclusivity. Accordingly, a process model of platform drifting has been developed. By taking a closer look at actual practices, the paper contributes to digital work research by identifying distinct elements of digital work involved in social media (mis)alignment and illustrates the profound, long-term consequences of social media on the nature of the work. [ABSTRACT FROM AUTHOR]

This paper explores the concept of "Clip Thinking" in the context of the digital age and investigates whether it is a complimentary or contradictory approach. Clip Thinking refers to the tendency of individuals to consume information and ideas in short, fragmented, and easily digestible formats, often facilitated by social media platforms and digital technologies. While Clip Thinking offers benefits such as quick access to diverse perspectives and efficient information processing, it also raises concerns about shallow engagement, echo chambers, and reduced critical thinking skills. This study employs a mixed-methods approach, combining qualitative interviews and quantitative surveys, to gather insights from participants representing different age groups, educational backgrounds, and digital media consumption habits. The qualitative interviews delve into participants' motivations, habits, and experiences related to Clip Thinking, providing an in-depth understanding of the phenomenon. The quantitative surveys measure participants' information consumption patterns, cognitive abilities, and attitudes towards Clip Thinking. On one hand, participants appreciate the convenience and accessibility offered by short, clipped content. They find it easier to navigate the overwhelming information landscape, rapidly browse topics of interest, and connect with a broader range of ideas. On the other hand, concerns emerge regarding the potential for information overload, reduced attention spans, and a lack of depth in understanding complex issues. Participants express worries about the erosion of critical thinking skills and the creation of filter bubbles that reinforce pre-existing beliefs. By analyzing the data collected, this paper aims to provide a nuanced understanding of the impacts of Clip Thinking on individuals' cognitive processes, decision-making abilities, and overall information consumption habits. The study's conclusions will inform educators, policymakers, and media platforms about the challenges and opportunities associated with promoting a balanced information ecosystem in the digital age. [ABSTRACT FROM AUTHOR]

In recent years, social networks have changed the way people communicate, while the dissemination and obtaining of information has become fast and convenient. However, it is a double-edged sword, providing both a breeding ground for disinformation and a medium for its rapid dissemination. Meanwhile, falsehoods are proliferating and becoming a global risk, and deepfake technology makes it harder to discern. Therefore, this paper proposed a large language model-based algorithm to detect disinformation with generated videos and photos, which enhances the ability to discriminate fake news by implanting alignment and task specific instructions on the Llama model. Eventually, the result indicated that the proposed method has the potential to achieve superior performance and aligned with people's judgment of things. Furthermore, the paper delves into practical discussions concerning fine-tuning the model within the constraints of limited computational power, highlighting the challenges and potential solutions in optimizing the algorithm for applications. [ABSTRACT FROM AUTHOR]

Considering the ubiquity of WiFi enabled devices and user's mobility, location privacy within WiFi networks is one of the focus points of many researchers. Preferred Network Lists (PNL), within which devices store a list of names of previously used hotspots - Service Set Identifiers (SSIDs), is transmitted in clear by a portion of devices as a part of WiFi connection protocol. PNL has proven to be one exceptionally interesting source of private data on user's previous whereabouts. However, since the PNL datasets are anonymized, most of the available work focuses on groups of users as opposed to one particular user. In this paper we work towards finding the name of the person behind the device's PNL. We introduce a novel SSID - location tag matching function, followed by an algorithm used for intersecting large PNL datasets with localization tags on Instagram social network. The algorithm enables us to match the user's MAC address and PNL with his full name, photos and activities. We find that deanonymization of a MAC address provides serious implications for potential long term tracking. We tested our work in real life conditions on a large scale music festival. To approach the ground truth we conducted hand check tests performed by 10 testers who concluded that more than 50% of the proposed matches were correct. [ABSTRACT FROM AUTHOR]

Versailles literature, a newly immerged genre on Chinese social media, is aptly defined as "humblebragging": bloggers showing off different aspects of themselves in a humble way. Previous research on the genre have focused on the "humble" part of the genre, arguing that the different strategies bloggers use to appear humble are for the purpose of appearing to be modest, a key value in Chinese politeness. But do the bloggers achieve that end, i.e., does humblebragging improve the self-image of the humblebraggers? In this paper, we report on an empirical study on this question and find that Versailles literature posters do not improve their self-image via humblebragging. In fact, evidence suggests that their image is damaged. Instead of appearing to be humble, these posters are perceived as showing and hypocritic. Our study therefore contributes to several strands of pragmatics: politeness evaluation, Chinese politeness, and internet pragmatics. • Versailles Literature (VL), defined as humblebragging, is a new genre in Chinese social media. • The literature on VL has focused on the humble, arguing that appearing humble helps enhance the image of VL posters. • This study reveals that VL damages the image of its posters, not enhancing it. • This study contributes to politeness evaluation, politeness research, and internet pragmatics. [ABSTRACT FROM AUTHOR]

In today's modern world, marketing is a fruitful source to grow a business by reaching a larger audience. This paper considers a popular social media source called LinkedIn.com as a way of marketing or reaching a larger audience. We consider a regression approach to explore the statistically significant role of the LinkedIn medium as a tool for reaching a larger audience. In addition to statistically exploring the role of the LinkedIn medium in the financial sector, we also introduce a new Weibull-based probability distribution, namely, the exponential TX inverse Weibull (for short'ETXI-Weibull') distribution. The ETXI-Weibull distribution is specially designed for analyzing LinkedIn marketing data. The beauty of the ETXI-Weibull distribution is that it satisfies the heavy-tailed characteristics, which are explored visually and mathematically. For the ETXI-Weibull distribution, the estimators are derived whose performances are evaluated visually and numerically through a simulation study. The ETXI-Weibull distribution is applied practically by considering the LinkedIn marketing data set as an example. Using certain established statistical tests, we observe that the ETXI-Weibull distribution provides an optimal fit to the LinkedIn marketing data set. Furthermore, a bivariate extension of the exponential TX inverse Weibull model is also introduced. Finally, the bivariate extension of the exponential TX inverse Weibull model is illustrated by modeling the LinkedIn marketing and sales data. [ABSTRACT FROM AUTHOR]

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

Social media platforms generate vast amounts of data that can be mined to gain insights into user behavior, preferences, and opinions. With the widespread adoption of mobile devices and the Android platform, social media data mining on Android has become an important area of research. In this paper, we present a systematic review of existing research related to social media data mining on Android. We conducted a comprehensive literature search and identified relevant research papers published between 2015 and 2022. We analyzed the papers to identify key themes and trends, and we discuss the strengths and weaknesses of current approaches. Our review revealed that most of the research in this area focuses on text data mining, and that machine learning techniques are commonly used for data analysis. We also found that there is a lack of research on image and video data mining, and that privacy and ethical issues are important considerations in this field. Based on our review, we propose future research directions for social media data mining on Android, including the development of more efficient and accurate machine learning algorithms, and the integration of multimedia data analysis. Our paper provides a valuable overview of existing research in this field and offers insights for researchers and practitioners interested in social media data mining on Android. [ABSTRACT FROM AUTHOR]

Moving beyond classical single-label classification tasks proves to be crucial nowadays since the majority of real-life problems are inherently multi-label. Various tasks require involving, for one single instance as an input, several classes simultaneously or none of them. Particularly, detecting abusive texts in Arabic social networks intuitively carries several manifestations with regard to the targets' referred characteristics like gender, race, sexual orientation, etc. Existing multi-label algorithms either invest considerable complexity to handle labels' correlation or explicitly ignore this information to deal with each label as an independent binary problem. To remedy the abovementioned problems, this paper proposes a new multi-label classification solution based on deep neural networks. The main idea is to exploit the correlation between all the dataset's labels to break it into ordered subsets. Then, follow that order to implement a chain of BERT classifiers. Each classifier is therefore trained on a corresponding subset and passes its output via feature space to the following one. For a new instance, we gather all the classifiers' results and pick the best one for each class. Experiments showed that our approach significantly outperformed the single classifier's method using BERT as well as other neural network architectures on our Arabic abusive language dataset. 91%,76%, and 82% were achieved respectively for precision, recall, and F1-score. Moreover, we further investigated the performance of our approach on Kaggle multi-label research paper dataset and achieved very promising results. [ABSTRACT FROM AUTHOR]

The British Journal of Anaesthesia (BJA) had an eventful 2021, following what was a cataclysmic 2020 for the whole world. Despite the tragic challenges of multiple waves of the COVID-19 pandemic and the unparalleled burdens this created for everyone working in anaesthesia and critical care, the BJA underwent a major transformation during 2021. The BJA strongly supported research and education relevant to the pandemic, and to the broader missions of anaesthesia, critical, and pain medicine. Innovations to the BJA in 2021 included a special section on COVID-19 and the Anaesthetist; a new open access journal in the BJA stable; creation of a new social media editor position; new webinar and author interview series; transition to a new manuscript management system; and a move away from paper to electronic publication. [ABSTRACT FROM AUTHOR]

Prior to the technological revolution, reliance on traditional sources such as newspapers and other mass communication channels was the predominant method of consuming news. With the pervasive influence of social media, traditional news sources have undergone significant displacement, yielding predominance to platforms rooted in social media networks. Informal news propagation through social media has been observed to be fast in nature. With the information propagating fast, the misinformation also propagates rapidly on social media. If the misinformation is not detected early, it can cause serious problems. Thus the problem of identifying misinformation at the onset needs much attention. This paper explores various architectures for detecting misinformation, employing deep transfer learning techniques. The study utilizes transformer architectures, namely MPNet, SentenceT5, and Generalizable T5-based dense Retrievers, to generate embeddings. These architectures have undergone pre-training and fine-tuning on diverse tasks. The system's performance is assessed using a benchmark dataset of misinformation from Reddit, referred to as 'Fakeddit'. When tested on unseen data, the models perform well, and outperform previously reported baselines and other models on the given dataset with textual modality. The results indicate that transfer learning with zero-shot or few-shot learning can prove very cost-effective and efficient for the problem of misinformation detection. [ABSTRACT FROM AUTHOR]

With the fast growth of online content and social media platforms, the amount of user- generated data available in the form of product evaluations, social media postings, and online debates has rapidly expanded. It has become essential for organizations to comprehend client attitudes, make wise decisions, and improve their goods and services by analysing the sentiment indicated in this data. This paper aims to develop a sentiment analysis system for product reviews using URLs of particular item as data sources. It utilizes web scraping techniques to collect textual information from web pages. Natural Language Processing (NLP) techniques are applied for data pre-processing, including tokenization and stop word removal. Machine learning models like Support Vector Classifier (SVC), Random Forest, KNN, Logistic Regression, Naive Bayes and Deep learning models like LSTM and GRU are used for sentiment analysis. Performance measures such as recall, accuracy, and precision are employed to evaluate the models. High scores were consistently achieved by the LSTM and GRU models across all measures, demonstrating their potency in successfully capturing sequential relationships. With strong F-1 ratings of 91% and 90%, respectively, Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU) regularly beat other models, demonstrating their outstanding precision-recall balance. Conversely, K-Nearest Neighbor (KNN) presents a significant trade-off, offering a blazing-fast training time of 0.01 seconds but demanding an extensive 102.34 seconds for prediction, resulting in a lower F-1 score of 47%. Support Vector Machine (SVM) and Logistic Regression achieve competitive F-1 scores of 85% and 84%, respectively, with relatively slow training and prediction times, striking a balance between performance and efficiency. Naive Bayes and Random Forest, while respectable in accuracy, fall slightly behind with F-1 scores of 71% and 80%, respectively, highlighting the significance of taking into account both performance and computing efficiency when choosing the best approach. Through this research, valuable insights are gained into the effectiveness and accuracy of different algorithms in sentiment analysis. The findings contribute to the understanding of sentiment analysis techniques and offer guidance for selecting the most suitable algorithm for URL-based sentiment analysis applications. [ABSTRACT FROM AUTHOR]

Vaccine hesitancy is a growing concern in public health, with increasing numbers of individuals expressing skepticism or outright refusal to receive vaccines. This factor was significantly highlighted during the COVID-19 pandemic, with large populations refusing to take the vaccine and prolonging the pandemic. This paper presents and compares two transformer-based approaches i.e. XLNET and BERT to classify vaccine misinformation on Twitter using the standard COVID-19 ANTi-Vax dataset. Subsequently, an analysis of vaccine discourse on Reddit is carried out following a user association mapping algorithm. The resultant graph was subsequently analyzed. The XLNET model outperformed BERT by showing a high accuracy of 0.9484, with an F1 score of 0.9353. The methodology can be used in multiple other scenarios to address concerns with regard to the usage of social media by analyzing network interactions. [ABSTRACT FROM AUTHOR]

The outbreak of conflict between Russia and Ukraine on February 23, 2022, was a huge shock, and it quickly became the dominant topic of conversation on social media. Our research is based on Twitter conversations about the conflict between Russia and Ukraine. This paper intends to monitor and analyze the Twitter conversation on the continuing war in Ukraine using natural language processing techniques such as word cloud analysis, sentiment analysis, and visualization. The major goal is to acquire insight into Twitter users' shifting thoughts and opinions on various parts of the conflict over time. The study's findings have important implications for politicians, journalists, and social media users who want to follow and understand how online communities' attitudes on global issues change. We hope that this study will help to gain a deeper knowledge of how social media works in affecting public debate and how it might be used to gain insights into public opinion on crucial global issues. In this work logistic regression, K-nearest neighbours and decision tree algorithm are used to analyse the sentiments and got classification accuracy of 94.58%, 88.89% and 90.45% respectively. [ABSTRACT FROM AUTHOR]

In the present scenario, Internet communities, forum and blogging sites play a very crucial role to present opinions, views and the comments on various events. As the reachability of sites are beyond the control of national boundaries, sometimes this leads to the conviction and persuasion of thoughts without considering any legal subsequences, and also influence the belief of others as well, thus finding or identifying the sentiments of public from the social media content is one of the major research issue. Analysis of the sentiments of social media data is very difficult to understand as this typically does not exhibit a suspicious pattern in the flow of information like individual's actual opinion on a specific occasion etc. With the advancement of methodology and easy to use, the users of social media sites are now expressing their opinions, sharing their views and experiences through images, text, animation, audio, video etc. Because of the complexity the conventional text based sentiment analysis procedure, a lot of methods have been evolved, however studies suggest mostly more complicated procedure and approaches. Twitter® (or X), is one of the most common platform used widely by individual to express their opinions and sentiments on various events. Twitter sentiment analysis basically deals with the analysis of twitter quotes to find the hidden pattern in the sentiments expressed by the users in past. This paper aims to takes the challenges regarding social media sentiments analysis and developed a hybrid approach (Text and visual sentiment) on twitter data for sentiment analysis by using NLP-based opinion clustering, textual mining, emotion API and some machine learning techniques for visual ontology. Simulation result shows the significance of work. [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]

This paper explores the ongoing semantic change of a discourse marker in Japanese social media. An analysis of 140 instances of the sentence-final tteyuu on Twitter, renamed X in July 2023, suggests that this discourse marker is shifting from a textual function of elaboration to a stance marker of lighthearted mockery. I argue this transition is attributed to its recurrent use in specific contexts, leading to invited inferences about the writer's intentions in expanding upon an idea, which extend beyond purely informational to interpersonal and social concerns. The functional ambiguity of these examples suggests an intermediate stage in tteyuu's evolution. The findings of this study underscore the centrality of pragmatic inferencing in semantic changes as well as the context-dependent nature of meaning creation. • Sentence-final tteyuu on Twitter (or X) is shifting from a textual to a stance marker. • Recurrent use in specific contexts drives pragmatic inference and semantic change. • Anonymous writers' self-mockery stance reflects their social meta-self-awareness. [ABSTRACT FROM AUTHOR]

The identification of events from social media platforms such as Twitter (now known as X) is a hot research problem. It has applications in diverse domains such as journalism, marketing, public safety, crisis management and disaster response. The process includes the identification, monitoring, and analysis of events or incidents while they are being discussed or reported on Twitter. When it comes to identifying events from tweets (i.e. feeds from Twitter), many of the currently available event detection methods mainly rely on keyword burstiness features or structural changes in the network. However, due to the intricate characteristics of tweets and the ever-changing nature of events, they frequently fail to recognise noteworthy occurrences before they become trending. Moreover, these methods face difficulties when it comes to capturing the evolving characteristics of events with limited or insufficient contextual information. In this paper, we propose a window-based tweet-processing method called EDTBERT for detecting events and tracking the evolution of events over time. Our proposed method utilizes the structural and semantic affinities that exist among words in tweets. The method starts by generating graph of tweets, where tweets are represented as nodes, and edges are the similarities between tweets. The method utilizes overlapping hashtags and named entities to capture the structural relationship between tweets. Additionally, a pre-trained sentence transformer model, specifically BERT, is employed to collect contextual knowledge and find semantically similar tweets. Next, the graph clustering technique is employed to identify optimized event clusters. Subsequently, our method generates chain of event clusters for each event to track the evolving variation of the event over time by utilising the "Maximum-Weight Bipartite Graph Matching" (MWBGM) algorithm. The effectiveness of our approach is assessed using standard Tweet Datasets. Our evaluation demonstrates that our approach outperforms the baseline approaches. [ABSTRACT FROM AUTHOR]

The development of information technology proves the possibility of acquiring large samples of policy text information and promotes innovative research based on the policy informatics approach of fusing data from multiple sources. This paper constructs a new public policy model based on existing data on economic system reform, and uses multi-types of textual big data such as government website, official weblog, and social media as the object of collection, to conduct a big data measurement on policy concerns related to the theory and practice of economic system reform since the reform and opening up. And this paper constructs a fine-grained measurement and portray the evolutionary trend of policy concerns and policy implementation. Based on the database, this paper helps to analyse the economic development trend since the reform and opening up, evaluate and monitor the implementation effect of different incentive policies and establish an iterative feedback mechanism, and provides a reference for relevant decision-making departments. [ABSTRACT FROM AUTHOR]

When popular topics arise, Internet users typically share their opinions and interact with others on social networking platforms, and the comments generated on social networks contribute to the spread of popular trends. In this paper, we focus on the popular ChatGPT topic and analyze the evolutionary trend of public opinion and the emotional polarity of comments related to ChatGPT on the Weibo and WeChat social media platforms. In this paper, emotions are divided into six categories, the emotions contained in Weibo and WeChat comments are discussed, and the main opinions reflected in each emotion are examined. We expect that our finding will help reveal the relationship between opinion and sentiment. [ABSTRACT FROM AUTHOR]

This paper demonstrates a holistic approach for conducting multi-platform and multilingual sentiment analysis of a main stream football club in the United Arab Emirates. The paper reports first iterations of the research project which anticipates to build an automated sentiment analysis system for football clubs to detect and prevent a potential social media crisis at an early stage. The article illustrates a schematic of how state of the art machine learning algorithms, methods and techniques can be put together to provide a basis for automated data collection, classification, sentiment analysis and visual reporting. The article also presents that the time-consuming and costly process of human text labelling can be effectively replaced by lexicon-based sentiment analysis. This paper reports the complete process from data collection to visual reports to discuss challenges and opportunities associated with design and development of such artefacts. [ABSTRACT FROM AUTHOR]

Digital marketing has been on the epicentre of many researches. However, there is the need to make clear which are the current trends of the Digital Marketing Strategies regarding luxurious hotels. Unfortunately, the COVID-19 pandemic has seriously "damaged" the hospitality industry and it has affected the digital marketing strategies of hotels. The aim of this paper is the evaluation of the most recent applications of digital marketing and especially the social media used by luxurious hotels and their guests (such as Facebook, Trip-advisor). The outcome of the research indicates that social media play a key role on the marketing of luxurious hotels. More specifically, a hotel should take into serious consideration the reviews made on social media by customers. A review can leverage a hotel but also it can doom its reputation. For this reason, the practitioners must give special attention on the reviews that the guests post on social media. The methodology of the paper is literature review. Hence, it will rely on publications. The authors have collected publications from various academic databases, such as SCOPUS, by using the appropriate keywords. The publications were selected based on how relative are with the purpose of this paper. To sum up, the hotel industry has to face a major challenge due to COVID-19 crisis. This crisis, which is an ongoing phenomenon, is expected to change the way that hotels are copying with. One of the changes that the hotel should take into consideration is which strategy will use on social media. This paper has relied on existing theories and speculations that have been made by several authors and they have not been cross-checked with primary researches. Therefore, there is the need for more primary data coming from upcoming publications. [ABSTRACT FROM AUTHOR]

This paper examines the effects of digital technologies on children's rights. All children's rights ought to be reinforced, respected, and protected. Majority of children devote their time on Internet using various social media platforms which have become a key means of teaching and learning. Increased internet access by children is associated with risks and escalation of children's rights violations. Institutions dealing with children continuously struggle to represent the rights of children enshrined within the Convention on the Rights of Children. The paper is mainly focuses on children's rights such as freedom of expression and information, privacy and data protection, protection and safety. Children's rights are not fully protected online in most cases particularly in countries like Botswana. The publication of sensitive information relating to children online is a violation of their rights to privacy and may affect their childhood development. The lack of relevant expert to investigate and prosecute cybercrime leads to the increase of cybercrime activities committed against children. [ABSTRACT FROM AUTHOR]

Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide, where millions of users share tweets continuously every second. By leveraging 1724328 tweets, this research-in-progress paper aims to understand the dynamics of social media users' interactions on cryptocurrencies using social set analysis (SSA). The findings reveal that Twitter users are more positive about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The research-in-progress paper also contributes to demonstrating the effectiveness of the social set analysis framework to analyse and visualize a big social media data. [ABSTRACT FROM AUTHOR]

DeepFake uses Generative + Adversarial Network for successfully switching the identities of two people. Large public databases and deep learning methods are now rapidly available because of the proliferation of easily accessible tools online. It has resulted in the emergence of very real appealing fake content that produced a bad impact and challenges for society to deal. Pre-trained generative adversarial networks (GANs) that can flawlessly substitute one person's face in a video or image for that other are proving supportive for implementing deepfake. This paper primarily presented a study of methods used to implement deepfake. Also, discuss the main deepfake's manipulation and detection techniques, and the implementation and detection of deepfake using Deep Convolution-based GAN models. A study of Comparative analyses of proposed GAN with other exiting GAN models using parameters Inception Score "IS" and Fréchet Inception Distance "FID" is also embedded. Along with the abovementioned, the paper discusses open issues and future trends that should be considered to advance in the field. [ABSTRACT FROM AUTHOR]

Sentiment analysis is a cognitive tool to extract the emotional tone of a piece of text. It is an area of research that is actively pursued in Natural Language Processing. Social media, forums and blogs, among other places, have seen a massive surge in the number of personalized reviews since the emergence of Internet-based applications. Sentiment analysis is primarily concerned with the classification and prediction of users' thoughts and emotions from these reviews. In recent years, numerous deep learning techniques have emerged to achieve this task. This paper provides a technical summary of Sentiment Analysis using a Bidirectional LSTM network. This model is capable of handling long-term dependencies by introducing memory into the model for making better predictions. The Amazon Product Review dataset served as the source of data for this research. Concretely, the analysis is performed on 104,975 product reviews reflecting users' attitudes toward mobile electronics products. The proposed model attempts to classify the reviews into two categories: positive and negative. Finally, the paper outlines the results of the analysis and suggests potential avenues for future research. [ABSTRACT FROM AUTHOR]

Big data has become omnipresent since most human activities are recorded using various technologies, thus generating huge databases on various platforms such as social media. Human activity has become reflected in these databases, making them suitable for various research that used to be conducted based on more traditional research approaches, such as survey research. Labour markets research is also affected by big data since social media has become one of the most dominant means of communication in various areas, being employed as one of them. This paper aims to map the research field of big data and labor markets using a systematic literature review and bibliography. Web of Science database has been searched using the keywords "big data" and "labo(u)r market". The most relevant papers for the area of big data for labour analysis have been selected, using transparent and consistent criteria. Papers have been analysed using a bibliometric approach supported by the VOSviewer and R bibliometrix, which has been used for detecting various research trends, such as research topics. The assessment of the current research in big data for the labour market has been compiled to detect the research gaps and generate future research directions. [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 emergence of online social networks (OSN) platforms removes communication barriers that are essential to human life, catalyzing social networking growth. However, this emergence has given rise to a negative impact when someone abuses the platform to commit cyberbullying activities. Hence, it is crucial to work on automated cyberbullying-related classification to mitigate the societal phenomena in OSN. The research on the automated classification model for cyberbullying was pioneered over the last decade with growing interest among researchers. It is helpful to track its growth over the decades to elucidate the state-of-arts techniques applied in this field. This paper presents a large amount of literature germane to cyberbullying classification from past to present to provide a comprehensive review. A total of 126 papers were reviewed. This paper emphasizes text-based cyberbullying and multi-modal cyberbullying. The review was presented around the machine learning workflow, encompassing four core sections: dataset analysis, pre-processing analysis, feature analysis, and technique analysis. Based on the critical analysis, limitations are addressed along with the future works that can be conducted to fill the gap in previous research. Furthermore, the review also examined the ethical implications associated with the implementation of these techniques. This review paper is expected to assist readers in fully comprehending the current trend, architecture, and techniques applied to the field. [ABSTRACT FROM AUTHOR]

This paper explores consumer green consumption practices and considers a set of factors, including cognitive and behavioural level constructs, that influence green consumption. The paper primarily aims to predict the green purchase intention and classify a consumer as a green or non-green consumer. A total of 310 responses were collected and analyzed using machine Learning techniques like Decision Tree, Random Forest, Gradient Boosting, XGBoost, K-Nearest Neighbour, and Support Vector Machine, and the models were validated using different performance metrics. The paper reveals that the main driving factors for a consumer to consider greener options are green self-identification, followed by environmental knowledge, environmental consciousness, and the impact of social media. The current work will allow better product development and the targeting and positioning of green products/services offerings to customers already classified by the system. • Machine learning techniques were employed to predict green purchase intention (GPI). • Green Self-identification & Environmental Consciousness were the crucial factors in influencing GPI. • Social and health consciousness had less of an impact on influencing GPI. • Perceived personal inconvenience was reported to have no significant impact on GPI. [ABSTRACT FROM AUTHOR]

• The positive correlation between social media use and corporate ESG performance underscores the platform's role in ESG performance. • The paper finds the importance of public participation in improving corporate ESG performance. • The paper finds that credit financing policy is a key factor supporting corporate ESG performance. The potential causal relationship between social media, relationship marketing and corporate ESG performance is explored in this study. By collecting data from China's A-share companies, this research employs benchmark regression analysis, tests action mechanisms, and conducts heterogeneity analysis. The findings indicate that leveraging social media and implementing relationship marketing strategies can enhance enterprises' ESG performance. This improvement is achieved by strengthening the external monitoring pathway to elevate the ESG level of enterprises. Moreover, these effects are more pronounced in non-state-owned enterprises. [ABSTRACT FROM AUTHOR]

With social media becoming a part of people's lives, Internet Public Opinion (IPO) is regarded as an important consideration that affects people's decision-making. However, it is also easy to be manipulated with intentions through specific methods to spread malicious thoughts or false information. IPO manipulation behavior is also an important indicator of "fake news" detection, so IPO manipulation detection techniques have been extensively studied in the past few years; however, this topic still lacks a systematic survey regarding various detection techniques. In order to solve this problem, this paper first introduces the characteristics and influencing factors of IPO, followed by sorting out the relevant literature in Google Scholar with specific keywords. This paper organizes the relevant literature into three main aspects, which are content-based, profile-based, and pattern-based, and provides a detailed analysis of each aspect and indicates their contributions. Furthermore, the research gaps and future research directions are also provided in this paper. [ABSTRACT FROM AUTHOR]

This paper proposes a method to represent the sentiment characteristics of opinions on social media by using some datasets as a pseudo corpus without any annotations. The widespread social media enables us to easily share our own opinions on the Web and communicate with each other. The more the data on social media increase, the more the demands for analysis of the data increase, e.g., text classification and sentiment analysis. Usually, the annotated data should be required in the existing text classification using the supervised machine learning method. However, it is reasonable to say that the criteria for the annotated labels should differ for each period, culture, and independent sense. Effective text classification for such different criteria needs the different types of annotations corresponding to each measure, and it requires much time and human resources. A pseudo corpus consists of multiple existing datasets with different characteristics in the proposed method. The classification model for each dataset is obtained as learning the pseudo corpus. The sentiment of the input text, which domain is different from the learned datasets, is represented as the likelihood distribution for the datasets in the pseudo corpus. This paper discusses the potential and limitations of this idea through the experiment. [ABSTRACT FROM AUTHOR]

Recently, Social Media (SM)'s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM's inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM's SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges' mitigation, students' intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies. [ABSTRACT FROM AUTHOR]

Affect detection from text has captured the attention of researchers recently. This is due to the rapid use of social media sites (e.g. Twitter, Facebook), which allows users to express their feelings, emotions, and thoughts in textual format. Analyzing emotion-rich textual data of social networks has many real-life applications. The context of an emotional text can be measured by analyzing certain features of this rich source of emotional information. Classifying text into emotional labels/intensities is considered a difficult problem. This paper resolves one of the state-of-the-art NLP research emotion and intensity detection tasks using Deep Learning and ensemble implementations. In this paper, we developed several innovative approaches; (a) bidirectional GRU_CNN (BiGRU_CNN), (b) conventional neural networks (CNN), and (c) XGBoost regressor (XGB). The ensemble of BiGRU_CNN, CNN, and XGB is used to solve an emotion intensity (EI-reg) task of the SemEval-2018 Task1 (Affect in Tweets). Our proposed ensemble approach was evaluated using a reference dataset of the SemEval-2018 Task1. Results show that our approach is well above the baseline for this task. It also achieved a Pearson of (69.2%), with an enhancement of 0.7% in comparison with previous best performing models. [ABSTRACT FROM AUTHOR]

This study analyses how affective stance is used for evaluation and positioning in fan discourse. Adopting a corpus-based approach, the paper focuses on affective stance verbs in an English-speaking popular media fandom on three social media platforms, Twitter, Tumblr, and Reddit. Fans use affective stance to construct positions in relation to the popular media franchise, in this case a popular Japanese manga and anime series. By combining quantitative and qualitative linguistic analysis the study shows that stancetaking verbalises fans' investment in the fictional world of the narrative media, in the franchise production, and in the context of fan productivity. The discursively constructed positions reflect common fan practices such as publicising fan affect and emotions, reviewing the franchise critically, and sharing textual fan productivity. The analysis associates platform differences with the social norms of the fans and the technological affordances that enable their interaction. Because hashtag-based interaction on micro-blogs is primarily indirect, the study argues that the interactive functions of stance can be understood in relation to the general fandom affect. • Stancetaking in a narrative media fandom marks investment in fictional world. • Social media platforms vary in frequency and function of stance verb constructions. • Positions taken through stancetaking reflect the practices of the online community. • Stance as an interactive process occurs in relation to the general fandom affect. [ABSTRACT FROM AUTHOR]

[Purpose/meaning] As the development of social media and the rise of visual culture, image in the social media has received more and more attention from scholars and sorting out its content is beneficial to clarifying the related research of images in social media and provide a new research perspective. [Method/procedure] This article takes the image literature in social media as the research object, carries on the keyword analysis to the literature and summarizes the typical research methods. Then this paper adopts analytic induction method to summarize the research progress of images in social media in recent ten years from the perspective of "publisherinformation-receiver" in information interaction. [Results/conclusions] Through the analysis of research topics, it is found that the feature of images on social media and user's information interaction behavior based on image are the key content that researchers pay attention to. Studies on images in social media can be divided into three parts:the characteristics of images in social media, image publishing behavior in social media, and image perception and acquisition behavior of social media users.[Innovation/value]This paper constructs a systematic cognitive frame for image research in social media, summarizes the existing research results, and predicts the future research from three aspects: the impact of image-based information interaction on users' social relationships in social media environments, the problem of user privacy disclosure in image social communication and the advancement of computer vision technique in image research in social media environment. [ABSTRACT FROM AUTHOR]

On 02.06.2020, the social media team of the Deutsche Bahn, the German railway company, announced on Twitter that for a three-month launching phase, they would use the singular T-form du to address their customers. On the basis of a corpus of public tweets analyzed as metapragmatic comments, I examine which stances users adopt and what these stances tell us about how the German-speaking Twittersphere metapragmatically assesses the appropriateness of pronouns of address in social networks. I show how the shift to the T-form is well accepted only if restricted to Twitter, while being strongly disfavored and deemed inappropriate if understood against the background of a customer-client's relation. While the users in favor of the V-form present their arguments in a direct way, most users who plead for the T-form resort to irony and banter, thus constructing the online persona of 'cool' people aware of appropriateness norms on social media. I conclude by showing how the clash between antagonistic — and possibly irreconcilable — positions can then be framed as a conflict between globalized norms fostering the use of T-forms as a conventionalized practice on social media and local norms of politeness, as the V-form remains the unmarked way of addressing an unknown adult in Germany. • The paper is an empirical paper on the metapragmatics of address forms in German based on Twitter data. • The paper explores how people react to the switch to the T-form to address customers. • The paper is of interest to anyone dealing with politeness, appropriateness norms, or pronoun choice in online communities. • The paper opens new avenues on the research on colloquialization and globalization in online communities. [ABSTRACT FROM AUTHOR]

The tremendous growth of event dissemination over social networks makes it very challenging to accurately discover and track exciting events, as well as their evolution and scope over space and time. People have migrated to social platforms and messaging apps, which represent an opportunity to create a more accurate prediction of social developments by translating event related streams to meaningful insights. However, the huge spread of 'noise' from unverified social media sources makes it difficult to accurately detect and track events. Over the last decade, multiple surveys on event detection from social media have been presented, with the aim of highlighting the different NLP, data management and machine learning techniques used to discover specific types of events, such as social gatherings, natural disasters, and emergencies, among others. However, these surveys focus only on a few dimensions of event detection, such as emphasizing on knowledge discovery form single modality or single social media platform or applied only to one specific language. In this survey paper, we introduce multiple perspectives for event detection in the big social data era. This survey paper thoroughly investigates and summarizes the significant progress in social event detection and visualization techniques, by emphasizing crucial challenges ranging from the management, fusion, and mining of big social data, to the applicability of these methods to different platforms, multiple languages and dialects rather than a single language, and with multiple modalities. The survey also focuses on advanced features required for event extraction, such as spatial and temporal scopes, location inference from multi-modal data (i.e., text or image), and semantic analysis. Application-oriented challenges and opportunities are also discussed. Finally, quantitative and qualitative experimental procedures and results to illustrate the effectiveness and gaps in existing works are presented. • Classifying event detection with textual, spatial, temporal, and semantic features. • Defining event spatio-temporal evolution by considering incremental architectures. • Investigating fusion techniques from multiple data sources and multiple modalities. • Discussing various languages and dialects or language-independent mechanisms. • Presenting big data processing tools for scalable and efficient event detection. [ABSTRACT FROM AUTHOR]

Text sentiment tendency analysis is a hot task in natural language processing. And text as the essential expression form of language, both individual word information, and overall utterance, deserves to be focused on. This paper proposes a fusion model to achieve high precision text sentiment analysis. This model combines the advantages of CNN to extract local information of text and BiLSTM to extract contextual association of text and introduces the attention mechanism to increase the focus on words with a solid emotional tendency in the text. The training datasets are comments that crawled from several social media sites such as Facebook, Twitter, Instagram, WhatsApp, etc. Based on the attention mechanism, this paper investigates the semantic sentiment analysis to reach the study of classification prediction for analyzing the positive and negative sentiment of financial news, social media, etc. The experimental results show that the proposed method can better extract features from the text and classify them than other baseline models. [ABSTRACT FROM AUTHOR]

Hate speech pertains to the expression of negative emotions by people. The spread of hate speech contributes to societal inequity by imposing adverse consequences on individuals' emotional and physical well-being, financial circumstances, and overall quality of life, thereby hindering their daily existence. Hence, the development of a hate detection and diffusion prediction system is of utmost importance. The literature study reveals two primary findings regarding the development of a hate speech model. Firstly, it is crucial to understand the potential impact of a hate speech message, specifically the extent to which it influences the users exposed to it. Secondly, numerous researchers have focused on studying information propagation, with a particular emphasis on analysing network topology and structure. This paper introduces psycho-sociological analysis as another approach to understanding hate speech spread on social networks. We develop a hate speech diffusion model considering many psycho-sociological dimensions of social media users. This paper aims to answer a few basic questions: (a) Who starts hateful tweets on social media? (b) Who are the users who share, respond to, or like such posts? (c) Can we build a better hate-speech diffusion model by understanding each user's psycho-sociological traits towards hateful information? To answer the above questions, we analyse user psycho-sociological traits by studying three different dimensions of user behaviour, i.e., inner personality, societal personality, and dark emotion using the Big5 personality model, Schwartz value model, and the Dark triad behaviour model. We develop a collection of classifiers to detect users' personality traits. In addition, we proposed a system to predict the spread of hate content on social networks. Several empirical analyses are presented in this paper in order to understand the correlation between human personality and online hateful behaviour. From our empirical analysis of hostile behaviour, several key observations are reported in this paper, a few of which include the following: (i) People with dark triad orientations are mostly the originators of online hate speech, (ii) Those who post sexist tweets are often Machiavellian, (iii) Narcissistic users are more likely to make racist tweets, and (iv) Those who post sexist and racist tweets on social networks are often psychopathic. Using these insights, we have mapped the user behaviour dimension into a vector space called behaviour embedding. This space is used for developing the hate-speech diffusion model. We show that the proposed models also perform better than the state-of-the-art with a significant F 1 -score margin. • Hate spreaders can be identified by DL model using physiological features. • Dark triad traits are linked to the hate user and hate post spreader. • Deep CNN network to detect hate users on social networks. • Diffusion system predicts who will spread the hate post on social media. [ABSTRACT FROM AUTHOR]

In this paper, we present a digital tool named Diversity Perspectives in Social Media (DivPSM) which conducts automated content analysis of strategic diversity communication in organizational social media posts, using supervised machine-learning. DivPSM is trained to identify whether a post makes mention of diversity or a diversity-related issue, and to subsequently code for the presence of three diversity dimensions (cultural/ethnic/racial, gender, and LHGBTQ+ diversity) and three diversity perspectives (the moral, market, and innovation perspectives). In Study 1, we describe the training and validation of the instrument, and examine how it performs compared to human coders. Our findings confirm that DivPSM is sufficiently reliable for use in future research. In study 2, we illustrate the type of data that DivPSM generates, by analyzing the prevalence of strategic diversity communication in social media posts (n = 84,561) of large organizations in the Netherlands. Our results show that in this context gender diversity is most prevalent, followed by LHGBTQ+ and cultural/ethnic/racial diversity. Furthermore, gender diversity is often associated with the innovation perspective, whereas LHGBTQ+ diversity is more often associated with the moral perspective. Cultural/ethnic/racial diversity does not show strong associations with any of the perspectives. Theoretical implications and directions for future research are discussed at the end of the paper. • DivPSM can analyze how organizations communicate about diversity on social media • DivPSM codes whether a post includes cultural/ethnic/racial, gender, or LHGBTQ+ diversity • DivPSM identifies the underlying rationale for diversity management that the organization uses • In social media posts by large Dutch organizations, gender diversity is mentioned most often • Gender diversity is often communicated as a way to increase creativity and innovation [ABSTRACT FROM AUTHOR]

Following the usage-based model, construction grammar assumes the co-existence of different construction inventories, it allows for constructions beyond clause level, and it embraces many types of meaning including discourse-functional meanings. It is thus well-suited to investigate the relationship between grammar and genre on the one hand and between grammar and possible sub-grammars on the other. In the current paper, we discuss these relationships based on a multimodal analysis of Danish and US department stores' Instagram strategies. We find that Instagram posts may constitute stable configurations of multimodal choices. Instagram posts may therefore be seen as discourse-grammatical constructions, thus supporting the view that genre may be part of grammar. Nevertheless, considerable variation in the realization of Instagram posts can be observed, which however correlates with the success of the respective posts, measured by the numbers of likes they receive. The immediate and objective feedback available for social media posts may have supported the establishment of particular practices, which seem to be attended to and exploited by experts; at the same time, these choices are functionally motivated, transparent and compositional for non-experts. We discuss whether different grammars for experts (professional producers of Instagram posts) and non-experts (the consumers) should be assumed, the role of exemplars and the functional motivation of the choices made by experts. We conclude that the fact that the expert constructions are encoding idioms speaks against separate grammatical subsystems; instead, in accordance with the usage-based model, we find that both experts and non-experts are likely to make use of conventions, exemplars and functional knowledge. In the current paper, we discuss the relationships between multiple grammars on the example of Instagram posts, a text-type that is constrained by technological restrictions. In particular, • We find that Instagram posts by expert users constitute stable configurations of choices, which may be understood as discourse-grammatical constructions from a construction grammar perspective. • We present a multimodal analysis of 70 Instagram posts by US and Danish department stores and identify different patterns of flexibility and restrictedness. • We discuss these patterns with respect to possible causes: increasing conventionalization, functional motivation, restricted knowledge of available choices, and an orientation to good exemplars. • We conclude that the data speak against separate grammatical subsystems. • Instead, it is plausible that both experts and non-experts are likely to make use of conventions, exemplars and functional knowledge. [ABSTRACT FROM AUTHOR]

New technologies that are applied in many areas of social and economic life, make the processes of creating, distributing and communicating value to customers continuously transform. It is also connected with redefining the roles performed by market entities, including producers, service providers, distributors and consumers. New technologies expand the possibilities of engaging the consumer in processes that have been conducted before only by companies. In this context, technology management is important. It translates into creating a spiral of values, in which what is possible in the field of new technologies will inspire what is indicated in the area of value management for the customer and vice versa. This is reflected in the concept of technological determinism, as well as in the ongoing changes in consumer behaviour and consumption virtualization. The aim of the paper is to present the scope of Internet applications in the processes of managing value for customers in Poland. The paper is based on literature studies and the results of a quantitative empirical study, which was performed on a sample of 156 companies operating on the Polish market. The research included people responsible for the use of the Internet in marketing or for maintaining contacts with customers with the use of new technologies. The analysis of the research results shows that the Internet is used to a greater extent to communicate and distribute value rather than to involve customers in its creation. [ABSTRACT FROM AUTHOR]

The paper focuses on the use of social media in the process of acquisition of the second language (L2) or foreign language learning (FLL), namely, English in university students. Recently, we have seen a dramatic change in favour of distance learning due to the COVID-19 pandemics, therefore, it could be useful to analyse the impact of social media in FLL as they have been used much more now. It is a study summarizing the most important findings of the most recent research into the impact of social media on L2 acquisition from the psycholinguistic perspective. The paper analyses data from research papers as they appear in Scopus and Web of Science databases in the past ten years (i.e. 2010-2020). The purpose of the study is to describe the current research findings, to analyse their importance, to compare the most important data, and to look for further development of the psycholinguistics of L2. Its primary intention is to look into the acquisition of L2, particularly English as a second language (ESL) in the younger generation, i.e., Generation Z of current university students. It also attempts to highlight both the advantages and disadvantages of using social media in L2 acquisition and therefore to highlight future strategies regarding FLL in university education. The current research in this area provides us with overtly optimistic results in FLL with the assistance of social media, however, the paper claims that the situation of utilizing social media in FLL needs further investigation and analysis, mainly from the psycholinguistic point of view. The current COVID-19 situation makes us aware that the impact of social media will increase due to social distancing. [ABSTRACT FROM AUTHOR]

Generating fake news has become a prevalent issue in the digital age, with profound consequences on society, politics, and media. Due to an exponential increase in the use of unverified information, spread on social media by users with different backgrounds, a significant task facing natural language processing is real-time recognition of generated fake news. The objective of this paper is to investigate the feasibility of utilizing artificial intelligence (AI) for the automatic generation of fake news that possesses a sufficiently high level of quality to serve as a synthetic corpus. These automatically generated texts are subsequently used to improve fake news detectors, evaluated on different publicly available datasets fake news datasets, but also against a novel fake news corpus, especially developed for this research. [ABSTRACT FROM AUTHOR]

The use of social media has become so popular that people share photos every day on them. Automatic face recognition and tagging of people's photos have caused privacy preservation issues and some methods have been proposed for hiding the identity of presented people in these images. Blurring and blacking the face area, adding physical adversarial patches to the face, and adding adversarial masks are some proposed methods for this purpose. However, these methods particularly suffer from dissimilarity of the input and output images and inadequate performance in identity concealment from automatic face recognition (AFR) systems. In this paper, we propose the Generative Mask-guided Face Image Manipulation (GMFIM) model based on Generative Adversarial Networks (GANs) to apply imperceptible edits to the input face image to preserve the identity of the person in the image. Our model consists of a face mask module, a GAN-based optimization module, and a merge module. Different criteria are considered in the objective function of the optimization step to produce high-quality images that are as similar as possible to the input image while they cannot be recognized by AFR systems. The results of the experiments on different datasets show that our model provides promising results in terms of the quality of the generated images and the identity concealment performance. [Display omitted] • A mask-guided GAN-based model (GMFIM) is proposed for deidentification of face images. • An optimization module based on two loss functions is proposed for hiding the identity of images. • A mask-guided model is adopted for separating the face area from the background. • A Fourier transform based model is used for merging the modified face with the background. • Qualitative and quantitative experiments are performed to evaluate and analyze the proposed model. [ABSTRACT FROM AUTHOR]

The issue of natural disasters, their management, and their prevention, requires good communication among stakeholders and between stakeholders and the population. The Nyiragongo volcanic crisis was a traumatic experience for those who were in Goma city in 2021, on 22 and 23 May. It was characterized by insufficient communication, demonstrating the lack of cohesion between stakeholders. In order to prepare a possible implementation of dedicated social media and groupware for effective communication during the Nyiragongo Volcanic Crisis, this paper presents a survey conducted among Goma citizens and proposes a possible architecture of a dedicated social media integrating text mining, which would help develop a flexible evacuation plan. Based on the results obtained, it appears that the establishment of a single information system related to the volcanic crisis can reduce the challenges related to social media where the volcano observatory, political authorities, different partners and citizens can share alert messages, locations of danger, and more comments. This unified information system would help to gather, discuss and disseminate accurate information on volcanic activities and associated hazards and risks. [ABSTRACT FROM AUTHOR]