Narrow Results

Sentiment analysis (SA) is an essential application of machine learning (ML) and natural language processing (NLP) that comprises the automatic extraction of opinions or sentiments presented in textual data. By leveraging methods to distinguish the expressive nature conveyed in written content, SA permits businesses and research workers to gain valuable insights into social media discourse, customer feedback, and public reviews. In the field of SA, the synergy of Applied Linguistics and Artificial Intelligence (AI) has led to a robust method that goes beyond conventional methods. By incorporating linguistic principles into AI methods, this interdisciplinary collaboration allows a more nuanced perception of human sentiments expressed in language. Applied Linguistics offers the theoretical basis for understanding the details of pragmatics, semantics, and linguistic structures, while AI algorithms leverage this knowledge for analyzing large datasets with notable accuracy. This study presents an Applied Linguistics-driven Artificial intelligence Approach for SA and Classification (ALAIA-SAC) system in social media. The primary intention of the ALAIA-SAC technique is to apply an attention mechanism with a fractal hyperparameter-tuned deep learning (DL) method for identifying sentiments. In the ALAIA-SAC technique, data preprocessing takes place in several stages to convert the input data into a compatible format. In addition, the TF-IDF model could be employed for the word embedding method. The self-attention directional long short-term memory (SBiLSTM) model is used for sentiment classification. Finally, the SBiLSTM model’s hyperparameter selection is performed using a Fractal Pelican optimization algorithm (FPOA). The experimentation results of the ALAIA-SAC method are assessed under two benchmark datasets. The comparative study of the ALAIA-SAC technique exhibited a superior accuracy value of 99.17% and 99.39% under Twitter US Airlines and IMDB datasets.

The Arab nation is seriously affected by computational propaganda. The detection of Arab computational propaganda has become a hot research topic in social networking platforms. Propaganda campaigns endeavor to influence people’s mindsets to improve a particular agenda. They automatically employ the anonymity of the Internet, the micro-profiling capability of social network platforms, and the ease of managing and creating coordinated networks to reach masses of social network users with persuasive messages, mainly aimed at topics each user is sensitive to, and ultimately affecting the outcomes on the targeted problem. Using computation techniques and methods, analysts and researchers can better understand the scope, scale, and impact of propaganda efforts in Arabic-speaking communities and develop strategies to counter them. In recent times, deep learning (DL) approaches targeted explicitly at analyzing, detecting, or countering propaganda within online platforms or Arabic-speaking communities. DL is a subset of machine learning (ML), which includes training artificial neural networks (ANNs) with multiple layers for learning data representation. This paper designs an improved fractal walrus optimization algorithm with DL-based Arab computation propaganda detection (IWOADL-ACPD) technique. The IWOADL-ACPD method mainly focuses on the recognition and classification of propaganda in the Arabic language. The IWOADL-ACPD method begins with a preprocessing step to standardize and clean raw Arabic text data. Consequently, BERT word embedding encodes meaningful data, capturing contextual nuances vital for accurately detecting propaganda. In addition, the stacked sparse autoencoder (SSAE) detection technique is employed to discern subtle patterns indicative of propaganda content. To improve the performance of the SSAE method, the IWOADL-ACPD method uses IWOA to fine-tune the hyperparameter effectively. The proposed IWOADL-ACPD method contributes to Arabic computation propaganda detection by providing an adaptive and comprehensive technique for the complexity of cultural, digital, and linguistic landscapes specific to the Arabic-speaking context. The robustness and efficacy of the IWOADL-ACPD technique are demonstrated through stimulation analysis on the Arabic dataset, which showcases its capability to perform better than other existing methods. The IWOADL-ACPD technique exhibited a superior accuracy value of 95.25% over existing methods.

Sarcasm is a language phrase that expresses the opposite of what is stated, often used for mocking or offending. It is commonly seen on social media platforms day by day. The opinion analysis process is susceptible to errors due to the potential for sarcasm to alter the statement’s meaning. As automated social media research tools become more prevalent, the reliability problems of analytics have also increased. According to the prior study, sarcastic reports alone have greatly diminished the automatic Sentiment Analysis (SA) performance in complex systems platforms. Sarcasm detection utilizing Deep Learning (DL) contains training models to identify the nuanced linguistic cues that indicate sarcasm in text. Typically, this process applies large datasets annotated with sarcastic and non-sarcastic samples to teach models to discriminate between them. DL methodologies, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer methods like BERT or GPT, are widely applied due to their ability to capture intricate patterns in language. This model learns to detect sarcasm by discriminating exaggerated expressions, contextual incongruities, and semantic reversals frequently related to sarcastic remarks. Therefore, this study presents a Fractal Red-Tailed Hawk Algorithm with Hybrid Deep Learning-Driven Sarcasm Detection (RTHHDL-SD) technique on complex systems and social media platforms. The purpose of the RTHHDL-SD technique is to identify and classify the occurrence of sarcasm in social media text. In the RTHHDL-SD approach, data preprocessing is performed in four ways to transform input data into valuable design. Besides, the RTHHDL-SD technique applies the FastText word embedding approach to generate word embeddings. The RTHHDL-SD technique applies a Deep Neural Network (DNN) with bi-directional long short-term memory for sarcasm detection, called the deep BiLSTM model. The RTH method was utilized as the hyperparameter optimizer to enhance the detection performance of the deep BiLSTM model. Moreover, the large language model is used to estimate the outcomes of the social media corpora. The simulation outcomes of the RTHHDL-SD methodology are examined under Twitter and Headlines datasets. The investigational outcomes of the RTHHDL-SD methodology exhibited superior accuracy values of 89.10% and 92.77% with other approaches.

In this paper, we undertook a COVID-19 mathematical model with the social media impact in using of COVID-19 vaccination. For the mentioned study, we use fractals fractional order model to study the complex geometry behind the said dynamical systems. For the existence theory of such model, we used fixed point approach. Ulam–Hyers stability is also required in numerical findings. Some fundamental results such as basic reproductive number and equilibrium points are derived. Numerical analysis is performed to simulate the theoretical results. For the simulations purposes, a numerical scheme based on interpolation is developed. Various graphical presentations are given to demonstrate the results.

With the global popularity of social media, how to effectively analyse the massive text data generated on these platforms to better understand users’ emotions and perspectives has become an important research direction. This study proposes a multidimensional sentiment analysis technique based on viewpoint extraction to overcome the shortcomings of traditional sentiment analysis methods in capturing emotional diversity and complexity. First, the study collects text data from various social media platforms, and after cleaning and preprocessing, constructs a sentiment analysis model that includes both serial and hybrid networks. In serial networks, a multi-layer architecture is adopted, including bidirectional encoders, convolutional neural networks, and bidirectional long short-term memory networks, to extract text features in an orderly manner. The hybrid network integrates the feature representations of different models and introduces a dual attention mechanism to enhance the ability to recognise evaluation objects and viewpoint holders. The results demonstrated that the proposed method exhibited enhanced accuracy, with improvements ranging from 1.51% to 0.96% in comparison to other serial or parallel models, and from 9.09% in comparison to other models. Introducing a dual attention mechanism significantly improves the accuracy of sentiment information extraction, with a performance improvement of about 5-6% compared to using only ordinary algorithms. This further substantiates the pivotal role of hierarchical feature extraction. Finally, the research findings provide a new perspective for social media sentiment analysis, which is expected to play an important role in practical applications such as marketing and public opinion monitoring. Further research will be conducted with the aim of expanding the data sample to enhance the model’s generalisation ability.

Social media and e-entrepreneurial innovation are vital for businesses, providing unique opportunities to engage with target customers, enhance brand visibility, drive customer acquisition, and maintain competitiveness in an increasingly digital marketplace. Therefore, this study aims to investigate the impact of social media on e-entrepreneurial innovation, with a specific focus on the roles of Artificial Intelligence (AI) adoption and government involvement as critical moderating factors. Utilizing a quantitative approach, we surveyed 345 entrepreneurs operating in Oman, aiming to examine how these elements influence entrepreneurial endeavors within the digital realm. Our findings reveal a significant relationship between social media utilization and advancements in e-entrepreneurial innovation, underscoring the essential role that effective social media strategies play in driving innovative practices. Furthermore, we explore the moderating effects of AI adoption, revealing how integrating advanced technologies can enhance the impact of social media on entrepreneurial innovation. Importantly, our results highlight the pivotal role of government involvement as a moderator, demonstrating how regulatory frameworks and support mechanisms can shape the relationship between social media engagement and innovation outcomes. By addressing these key dimensions, this research contributes valuable insights to academic discourse on digital entrepreneurship and offers practical implications for policymakers and entrepreneurial stakeholders. The findings guide fostering innovation and growth within the Omani entrepreneurial ecosystem, emphasizing the need for strategic alignment between social media efforts, AI adoption, and government policies to enhance e-entrepreneurial success.

The growth of technology and social media websites has increased the potential to online explore different products and places around the globe. While online websites are primarily responsible for the generation of large amounts of data, this big data may be beneficial to other users provided the proper decision pattern can be analyzed. This work is focusing on the big data from social media to determine the travel destination preferences for Indian tourists. The analysis of online tourism reviews is beneficial to both tourists and businesses in tourist countries. Tourists can analyze all the required aspects prior to traveling and businesses in the destination country can enhance their products. The study aims to analyze the online tourist reviews using supervised machine learning methods (decision tree, k-nearest neighbor, back propagation neural networks and support vector machine) and ensemble learning in order to ascertain the travel preferences of Indian tourists visiting other countries. For the research experiments, significant travel data histories of tourists for the five destination places (Dubai, Indonesia, Malaysia, Thailand and Singapore) are extracted from TripAdvisor. TripAdvisor is a worldwide popular tourism website that provides access to consumers to share their travel experiences. From the selected five destination places, the preferences of Indian tourists are analyzed for the factors of travel & destination comfort, hotel facilities, food quality and attractions of the place. The analysis results of the proposed recommendation system indicate the determination of precise suggestions for Indian tourists traveling to other countries.

The factors influencing the dissemination of public opinion on social media, the main carrier of public opinion, are diverse, complex and changeable. Existing studies of influential factors of public opinion dissemination focus on the information itself and information sources in the dissemination process, failing to consider the comprehensive influence of multidimensional factors, such as information content, sources and channels. This study takes the identification of multidimensional influential factors of social media information dissemination as the research object and comprehensively sorts out the influencing factors of public opinion. To improve the scientific basis and accuracy of the research, multidimensional factors, including information characteristics, dissemination network structure and user-level attributes, are selected to analyze the effect of influential factors in different dimensions on the dissemination of social media public opinion information using econometric models. Three main conclusions of this paper are as follows: (1) The traditional information characteristics (information content) and information source attributes (user-level factor) are not the only key factors affecting information dissemination, while the information channel (network structure) is worth more consideration. (2) Netizens tend to pay more attention to the psychological and emotional attributes of information when forwarding public opinions. The communication mode in which offline social elites enlighten the public no longer exists; whether a user is a network celebrity or lives in the central area no longer significantly affects public opinion dissemination. (3) The higher the total amount of information users release, the more the information would interfere with the public opinion. This is mainly because users with a higher level of activity may release more invalid information about advertising that has nothing to do with public opinion events.

Social media blockchain is emerging as a promising solution to deal with privacy issues, by putting user privacy in edge nodes rather than centralized nodes. Under the protection of information encryption, only those who have cryptographic keys can get access to key information. This work aims at multimedia information in social media blockchain and utilizes the RSA encryption mechanism to construct the information encryption system. Due to the resilience of biometric features, the biometric cryptographic keys are not easy to be fabricated. Thus, this paper proposes a biometric keys-enhanced multimedia encryption algorithm for social media blockchain. First of all, the wiener filter is adopted to make some preprocessing operations to images, such as noise reduction. On this basis, the discrete wavelet transform is adopted to extract feature representation from images, and nonlinear approximation of contourlet transform is adopted to make feature fusion. Next, cryptographic keys can be generated from the fused biometric feature vectors to encrypt biometric data. Finally, some simulation experiments are conducted to evaluate performance of the proposal from three aspects: key generation time, security level and encryption-decryption time complexity. For key generation time, processing speed of the proposal is approximately 1–2ms per sample. For security level, the proposal can reach an index value beyond 95% which is higher than comparison methods. For encryption-decryption time complexity, the proposal is about 30% lower than comparison methods.

People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people's perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into a number of sub-regions, and trains language models over social media conversations within these sub-regions. Using Bayesian and geo-smoothing methods, the ensemble of language models can be queried with phrases embodying a perception. Discrete and continuous visualization methods represent the extent to which social media posts within the sub-regions express the query. The capabilities of the perception mining approach are illustrated using transportation-themed scenarios.

In this paper, we introduce a novel methodology for personalized advertising using hotlink assignment. We provide an automated procedure that places advertising content improving the success of the campaign. Our goal is website reconstruction to enhance browsing experience and to lead customers to certain advertising content through hotlinks. The proposed methodology reduces the number of steps that users need to reach their interest through hotlinks. Also, our algorithm places advertising content in the generated browsing paths taking into account user’ preferences using information from social media and complexity in terms of load and object requests of webpages. Our experiments show a reduction in the steps in about 11% to reach the webpage target/Ads and an improvement on time and memory loss in about 17:5% and 20% respectively during the browsing. Furthermore, the results of users’ relevance feedback show that the majority of the users are satisfied with the provided information using our methodology.

Social media contain rich information that can be used to help understand human mind and behavior. Social media data, however, are mostly unstructured (e.g., text and image) and a large number of features may be needed to represent them (e.g., we may need millions of unigrams to represent social media texts). Moreover, accurately assessing human behavior is often difficult (e.g., assessing addiction may require medical diagnosis). As a result, the ground truth data needed to train a supervised human behavior model are often difficult to obtain at a large scale. To avoid overfitting, many state-of-the-art behavior models employ sophisticated unsupervised or self-supervised machine learning methods to leverage a large amount of unsupervised data for both feature learning and dimension reduction. Unfortunately, despite their high performance, these advanced machine learning models often rely on latent features that are hard to explain. Since understanding the knowledge captured in these models is important to behavior scientists and public health providers, we explore new methods to build machine learning models that are not only accurate but also interpretable. We evaluate the effectiveness of the proposed methods in predicting Substance Use Disorders (SUD). We believe the methods we proposed are general and applicable to a wide range of data-driven human trait and behavior analysis applications.

Social media platforms have become vast repositories of user-generated content, offering an abundant data source for sentiment analysis (SA). SA is a natural language processing (NLP) algorithm that defines the sentiment or emotional tone expressed in the given text. It includes utilizing computational techniques to automatically detect and categorize the sentiment as negative, positive, or neutral. Aspect-based SA (ABSA) systems leverage machine learning (ML) approaches to discriminate nuanced opinions within the text, which break down sentiment through particular attributes or aspects of the subject matter. Businesses and researchers can gain deep insights into brand perception, public opinion, and product feedback by integrating social media data with ABSA methodologies. This enables the extraction of sentiment polarity and more actionable and targeted insights. By applying ML approaches trained on the abundance of social media data, organizations can identify areas for improvement, tailor their strategies to meet their audience’s evolving needs and preferences and better understand customer sentiments. In this view, this study develops a new Fractal Snow Ablation Optimizer with Bayesian Machine Learning for Aspect-Level Sentiment Analysis (SAOBML-ALSA) technique on social media. The SAOBML-ALSA approach examines social media content to identify sentiments into distinct classes. In the primary stage, the SAOBML-ALSA technique preprocesses the input social media content to transform it into a meaningful format. This is followed by a LeBERT-based word embedding process. The SAOBML-ALSA technique applies a Naïve Bayes (NB) classifier for ALSA. Eventually, the parameter selection of the NB classifier will be done using the SAO technique. The performance evaluation of the SAOBML-ALSA methodology was examined under the benchmark database. The experimental results stated that the SAOBML-ALSA technique exhibits promising performance compared to other models.

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

Social media conveys a reachable platform for users to share information. The inescapable practice of social media has produced remarkable volumes of social data. Social media gathers the data in both structured-unstructured and formal-informal ways as users are not concerned with the exact grammatical structure and spelling when interacting with each other by means of various social networking websites (Twitter, Facebook, YouTube, LinkedIn, etc.). People are increasingly involved in and dependent on social media networks for data, news and opinions of other handlers on a variety of topics. The strong dependence on social media network sites contributes to enormous data generation characterized by three issues: scale, noise, and variety. Such problems also hinder social network data to be evaluated manually, resulting in the correct use of statistical analytical methods. Mining social media data can extract significant patterns that can be advantageous for consumers, users, and business. Pattern mining offers a wide variety of methods to detect valuable knowledge from huge datasets, such as patterns, trends, and rules. In this work, data was collected comprised of users’ opinions and sentiments and then processed using a significant number of pattern mining methods. The results were then further analyzed to attain meaningful information. The aim of this paper is to deliver a summary and a set of strategies for utilizing the ubiquitous pattern mining approaches, and to recognize the challenges and future research guidelines of dealing out social media data.

In social media, the data-sharing activities have turned out to be more pervasive; individuals and companies have comprehended the significance of promoting info by social media network. However, these individuals and companies face more challenges with the issue of “how to obtain the full benefit that the platforms provide”. Therefore, social media policies to improve the online promotion are turning out to be more significant. The popularization of social media contents are related to public attention and interest of users, thus the popularity fore cast of online contents has considered being the major task in social media analytic and it facilitates several appliances in diverse domain as well. This paper intends to introduce a popularity forecast approach that derives and combines the richest data of “text content encoder, user encoder, time series encoder, and user sentiment analysis”. The extracted features are then predicted via Long Short Term Memory (LSTM). Particularly, to enhance the prediction accuracy of the LSTM, the weights are fine-tuned via Self Adaptive Rain optimization (SA-RO).

Web 2.0 as a contemporary phenomenon receives considerable attention by IS scholars due to its perceived transformational impact on businesses. This paper critically elaborates on the value creation potential of Web 2.0 for small and medium enterprises (SME). By conducting an inductive study we reveal that SMEs can effectively use Web 2.0 as a means to support customer acquisition, alleviate resource limitations and to maintain customer enthusiasm associated with the customer purchasing process. In case that a high customer convenience is required which is based on the involvement of different parties or on personal service support, there is hardly any Web 2.0 value creation potential. This research contributes to the domain of business modeling by assessing the notion of value in a Web 2.0 setting. It also contributes to IS research on Web 2.0 adoption.

With the ubiquitous presence of smart phones and the availability of easy-to-use applications, there is an increase in the number of online services. A growing number of people now search for information and interact online. They expect to see services available and accessible online. To meet citizens’ expectations, governments have also increased their online presence. However, information and services are not the only reasons people go online. People also build their social circle online, seeking support and empathy, looking for someone with whom they can talk and who can understand their situation and worries. Online communities (and social networks in general) have been shown to have the potential to provide social and emotional peer-support. Our work aimed at determining whether online communities could be deployed in the public administration domain, in particular to support people receiving welfare payments, with similar benefits. We hypothesized that an online community could provide such support to disadvantaged citizens. Toward testing this hypothesis, after a user requirements analysis and some preparatory work, we designed and developed an online community for a specific target group of welfare recipients, as a collaboration between CSIRO and the Australian Department of Human Services. The community was deployed for one year. In this paper, we briefly explain our aims and the work that went into preparing for the community. We introduce the portal and the support it offered. We then report our observations and findings about both the informational and emotional support participants received, through an analysis of the comments posted in the community, and whether this support was perceived as welcome and useful.

Learning to Code may give you a Vanilla Sky

Social Media Literacy: One step ahead or one step back?

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning.