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Social media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing Multi‐Pop, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post‐content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics. [ABSTRACT FROM AUTHOR]

In Web3 social platforms, i.e. social web applications that rely on blockchain technology to support their functionalities, interactions among users are usually multimodal, from common social interactions such as following, liking, or posting, to specific relations given by crypto-token transfers facilitated by the blockchain. In this dynamic and intertwined networked context, modeled as a financial network, our main goals are (i) to predict whether a pair of users will be involved in a financial transaction, i.e. the transaction prediction task, even using textual information produced by users, and (ii) to verify whether performances may be enhanced by textual content. To address the above issues, we compared current snapshot-based temporal graph learning methods and developed T3GNN, a solution based on state-of-the-art temporal graph neural networks' design, which integrates fine-tuned sentence embeddings and a simple yet effective graph-augmentation strategy for representing content, and historical negative sampling. We evaluated models in a Web3 context by leveraging a novel high-resolution temporal dataset, collected from one of the most used Web3 social platforms, which spans more than one year of financial interactions as well as published textual content. The experimental evaluation has shown that T3GNN consistently achieved the best performance over time and for most of the snapshots. Furthermore, through an extensive analysis of the performance of our model, we show that, despite the graph structure being crucial for making predictions, textual content contains useful information for forecasting transactions, highlighting an interplay between users' interests and economic relationships in Web3 platforms. Finally, the evaluation has also highlighted the importance of adopting sampling methods alternative to random negative sampling when dealing with prediction tasks on temporal networks. [ABSTRACT FROM AUTHOR]

This paper aims to highlight the latest and most widely used artificial intelligence methods in text similarity prediction. The study focuses on articles published from 2010 to 2022 in the field of text similarity prediction and algorithms applied in this field. In this paper, systematic scientific comparisons have been made between existing approaches for predicting text similarity to answer the question raised in this study about the most used and accurate approach. Through previous studies and the comparisons made in this paper, the Ant Colony Optimization Algorithm (ACO) approach was found to be the most frequently used in text similarity prediction and solving scheduling problems. [ABSTRACT FROM AUTHOR]

Tweet prediction plays a crucial role in sentiment analysis, trend forecasting, and user behavior analysis on social media platforms such as X (Twitter). This study delves into optimizing Machine Learning (ML) models for precise tweet prediction by capturing intricate dependencies and contextual nuances within tweets. Four prominent ML models, i.e. Logistic Regression (LR), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) were utilized for disaster-related tweet prediction. Our models adeptly discern semantic meanings, sentiment, and pertinent context from tweets, ensuring robust predictive outcomes. The SVM model showed significantly higher performance with 82% accuracy and an F1 score of 81%, whereas LR, XGBoost, and RF achieved 79% accuracy with average F1-scores of 78%. [ABSTRACT FROM AUTHOR]

The increasing popularity of social media platforms makes it important to study user engagement, which is a crucial aspect of any marketing strategy or business model. The oversaturation of image content on social media platforms has persuaded us to identify the important factors that affect the popularity of image content in social media. This comes from the fact that only an iota of the humongous image content available online receives the attention of the target audience. A good quality image having the potential content to make the image popular, can be used in online business, to attract potential buyers. Comprehensive research has been done in the area of popularity prediction using several machine learning techniques. However, we observe that there is still significant scope for improvement in analyzing the social importance of image content. We propose the DFW-PP framework, to learn the importance of different features obtained from the image content, that vary over time. The proposed method extracts features from the image content from social media, provides scores to the image content based on studies of the dynamics of the image content, and draws inferences on the impact of the image on social media. Further, the proposed method controls the skewness of the distribution of the features by applying a log–log normalization. The proposed method is experimented with a benchmark dataset, to show promising results. Our statistical analysis shows a significant influence of a combination of image and meta features on the popularity of the image in social media. The code will be made publicly available at github. [ABSTRACT FROM AUTHOR]

Organized event is an important form of human activity. Nowadays, many digital platforms offer organized events on the Internet, allowing users to be organizers or participants. For such platforms, it is beneficial to predict potential event participants. Existing work on this problem tends to borrow recommendation techniques. However, compared to e-commerce items and purchases, events and participation are usually of a much smaller frequency, and the data may be insufficient to learn an accurate prediction model. In this paper, we propose to utilize social media retweeting activity to enhance the learning of event participant prediction models. We create a joint knowledge graph to bridge the social media and the target domain, assuming that event descriptions and tweets are written in the same language. Furthermore, we propose a learning model that utilize retweeting information for the target domain prediction more effectively. We conduct comprehensive experiments in two scenarios with real-world data. In each scenario, we set up training data of different sizes, as well as warm and cold test cases. The evaluation results show that our approach consistently outperforms several baseline models in both warm and cold tests. [ABSTRACT FROM AUTHOR]

Age is an essential piece of demographic information for social profiling, as different social and behavioral characteristics are age-related. To acquire age information, most of the previously conducted social profiling studies have predicted age information. However, age predictions in social profiling have been very limited, because it is difficult or impossible to obtain age information from social media. Moreover, age-prediction results have rarely been used to study human dynamics. In these circumstances, this study focused on naver.com, a nationwide social media website in Korea. Although the social profiles of news commenters on naver.com can be analyzed and used, the age information is incomplete (i.e., partially open to the public) owing to anonymity and privacy protection policies. Therefore, no prior research has used naver.com for age predictions or subsequent analyses based on the predicted age information. To address this research gap, this study proposes a method that uses a machine learning approach to predict the age information of anonymous commenters on unlabeled (i.e., with age information hidden) news articles on naver.com. Furthermore, the predicted age information was fused with the section information of the collected news articles, and fuzzy differences between age groups were analyzed for topics of interest, using the proposed correlation–similarity matrix and fuzzy sets of age differences. Thus, differentiated from the previous social profiling studies, this study expands the literature on social profiling and human dynamics studies. Consequently, it revealed differences between age groups from anonymous and incomplete Korean social media that can help in understanding age differences and ease related intergenerational conflicts to help reach a sustainable South Korea. [ABSTRACT FROM AUTHOR]

The goal of crime prediction is to help law enforcement authorities to prevent crimes before they happen by identifying and reducing potential future crimes. In present days, crime cases are rapidly occur so it is a challenging task to accurately predict and classify the future crimes, as criminal patterns are adaptable and constantly evolve, making it a challenging task. Sentiment analysis is essential for deciphering the feelings conveyed in text since social media has grown into a popular venue for individuals to communicate their thoughts, feelings, views, and comments. This analysis is particularly valuable for making informed decisions in business, politics and government agencies especially to identify crimes. However, it faces the difficulties like lexical diversity and dataset imbalance. In order to make better decisions and maybe lower the risk of predicting the suspects in a crime, early crime prediction has lately made use of Artificial Intelligence (AI) models like Deep Learning (DL) and Machine Learning (ML). When applied to crime data, ML and DL models may assist pinpoint possible crime hotspots and foretell when crimes may occur. Crime prediction and categorization using social media posts is covered in depth in this paper, which offers a comprehensive overview of several ML and DL frameworks. Initially, different ML and DL based crime prediction models designed by many researchers are examined in brief. The next step is to do a comparison research to learn about the shortcomings of those frameworks and provide an alternative method for effectively predicting crimes based on social media postings. [ABSTRACT FROM AUTHOR]

The simplicity to deliver an opinion about companies or institutions via social media has resulted in both positive and negative judgments. Through social media all positive and negative information will be easily found and spread. It is concerned that negative information will lead to negative public opinion. If this occurs, the company will suffer from a lack of trust, which will harm the company's reputation. Thus, to monitor uncontrolled issues, a company wants to know what topics or opinions are developing in the community. Therefore, the topic modelling using latent dirichlet allocation (LDA) is proposed to identify topics that are being discussed on social media. The findings of this study got the coherence score of 0.558 and based on the direct human judgment, the model got an average 80% correctly. The findings of this study reveal 4 topics groups that represent the corporate social media content. These findings offer information to companies about the latest topics or opinions that are currently developing in society which could provide recommendations related to decision-making on current issues thus increasing the trust and reliability towards the company. [ABSTRACT FROM AUTHOR]

This research aims to analyze the role of visuals posted on the social media of NGOs and to predict the popularity of a post based on the characteristics of the visual it contains. Two social media platforms, namely Facebook and Instagram, were selected as the empirical study environments. Specifically, all visuals posted on 12 child-related Non-Government Organizations during the period of 2020–2021 (4,144 in total) were collected and subsequently subjected to automatic characterization using visual recognition and artificial intelligence tools. Machine learning algorithms were then employed to predict the popularity of a post solely based on the visuals it contains, as well as to identify the most significant features that serve as predictors for post popularity. The Support Vector Classifier performed best with a prediction accuracy of 0.62 on Facebook and 0.81 on Instagram. For the explanation of the model, we used feature importance metrics and found that features like the presence of people and the emotions of joy and calmness are important for the prediction. Companies and organizations serve a large part of their communication strategy through social media. Given that every advertiser would like to use their funds in the most efficient way, the ability to predict the performance of a post would be a very important tool. The methodology can be used in the non-profit sector, whereby knowing what visual will perform better they could promote their mission more effectively, increase public awareness, raise funds and reduce expenses on their communication strategy. The novelty of this work regarding popularity prediction on social media lies in the fact that to make the prediction, it focused exclusively on the visual and its characteristics and achieved high accuracy scores in the case of Instagram. Additionally, it provided important information about visual characteristics and their importance in predicting popularity. [ABSTRACT FROM AUTHOR]

The relation between mental health and the language employed by social media users has been the focus of a wide range of studies in Natural Language Processing and related fields intended to predict early signs of a mental disorder. In many cases, however, text data alone is considered to be a sufficient source of learning features, and other possible sources also available from social media are often neglected. To shed light on these issues, the present work addresses the use of social media connections information — represented by the identities of Twitter friends, followers and mentions — to predict depression and anxiety disorder. Using a large multimodal dataset of over 31K unique Twitter timelines (555 million words) and associated network data, we built a number of network-based models (using network embeddings representations), and text-based alternatives (bag-of-words and LIWC baseline models) to these tasks. Results suggest that not only network connections may act as strong predictors of mental health, but that these models may actually outperform standard baseline alternatives that rely on text data alone, which may be seen as a first step towards more sophisticated architectures that may actually combine textual and non-textual information. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the predictive effect of artificial intelligence on user demand in big data social media and to provide suggestions for developing enterprise innovation frameworks and implementing marketing strategies. In response to the inconsistency between the supply of enterprise products and services and market demand, deep learning algorithms have been introduced using social media big data analysis. This algorithm has been improved to construct a user demand prediction model in social media big data based on bidirectional long short-term memory (BiLSTM) fused with Word2Vec. The model uses data acquisition and pre-processing, Word2Vec algorithm to vectorization the data information, and BiLSTM network to model and train the sequence. Finally, the model is evaluated as an example. [ABSTRACT FROM AUTHOR]

Edge intelligence (EI) is recognized by academia and industry as one of the key emerging technologies for future cyber‐physical‐social systems (CPSS), which provides the ability to analyze data at edge rather than sending it to the cloud for analysis, and will be a key enabler to realize a world of a trillion hyper‐connected smart sensing devices. As a part of future CPSS, online social networks are large‐scale complex networks that consist of a large number of network nodes and links. The dynamic discovery of communities, especially overlapping communities, is important to understand the evolution of online social networks. However, traditional community discovery algorithms cannot effectively discover overlapping communities in social networks. In order to address this challenge, an edge intelligence‐enabled dynamic overlapping community discovery and evolution prediction model (EIDEP) is proposed in this article. This model encompasses a label propagation algorithm based extension (LPAE) algorithm, which is able to efficiently discover the user community structures in online social networks. Based on the LPAE community discovery algorithm, a user interest behavior based evolution prediction (UIBEP) algorithm is incorporated in our EIDEP model in order to realize a fast yet accurate community evolution for online social networks, by considering the interest similarity of unlinked nodes in a given community. The performance of our proposed LPAE and UIBEP models is validated and evaluated against notable state‐of‐the‐art community discovery algorithms, through extensive experiments conducted based on a Twitter dataset. [ABSTRACT FROM AUTHOR]

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor. [ABSTRACT FROM AUTHOR]

Due to the great advances in information technology, an increasing number of social platforms have appeared. Friend recommendation is an important task in social media, but newly built social platforms have insufficient information to predict entity relationships. In this case, platforms with sufficient information can help newly built platforms. To address this challenge, a model of link prediction in social multiplex networks (LPSMN) is proposed in this work. Specifically, we first extract graph structure features, latent features and explicit features and then concatenate these features as link representations. Then, with the assistance of external information from a mature platform, an attention mechanism is employed to construct a multiplex and enhanced forecasting model. Additionally, we consider the problem of link prediction to be a binary classification problem. This method utilises three different kinds of features to improve link prediction performance. Finally, we use five synthetic networks with various degree distributions and two real-world social multiplex networks (Weibo–Douban and Facebook–Twitter) to build an experimental scenario for further assessment. The numerical results indicate that the proposed LPSMN model improves the prediction accuracy compared with several baseline methods. We also find that with the decline in network heterogeneity, the performance of LPSMN increases. [ABSTRACT FROM AUTHOR]

Social media popularity prediction refers to using multi-modal content to predict the popularity of a post offered by an internet user. It is an effective way to explore advanced forecasting trends and make more popularity-sensitive strategic decisions for the future. Existing methods attempt to explore various multi-model features to solve this task, which only focus on local information, lacking global understanding for the post's content. In this paper, we propose social media popularity prediction with caption (SMPC), a novel architecture that integrates the caption as the global representation into the existing multi-model-feature-based popularity prediction method. To make good use of the generated captions, we process them in word-level, sentence-level and length-level ways, obtaining three kinds of caption features. To incorporate caption features, we exploit seven variants of the architecture by concatenating features in all the possible manners, for the feature fusion and training different combinations for the CatBoost regression. Extensive experiments are conducted on Social Media Prediction Dataset (SMPD) and show that the proposed approaches can achieve competing results against state-of-the-art models. [ABSTRACT FROM AUTHOR]

The traditional method of doing business has been disrupted by social media. In order to develop the enterprise, it is essential to forecast the level of interaction that a new post would receive from social media users. It is possible for the user's interest in any one social media post to be impacted by external factors or to dwindle as a result of changes in his behaviour. The popularity detection strategies that are user-based or population-based are unable to keep up with these shifts, which leads to inaccurate forecasts. This work makes a prediction about how popular the post will be and addresses any anomalies caused by factors outside of the study. A novel improved PARAFAC (A-PARAFAC) method that is tensor factorization-based has been presented in order to cope with the user criteria that will be used in the future to rate any project. We consolidated the information on the historically popular content, and we accelerated the computation by choosing the top contents that were most like each other. The tensor is factorised with the application of the Adam optimization. It has been modified such that the bias is now included in the gradient function of A-PARAFAC, and the value of the bias is updated after each iteration. The prediction accuracy is improved by 32.25% with this strategy compared to other state of the art methods. [ABSTRACT FROM AUTHOR]

One of the major platforms today to express your emotions, expressions, feelings, and views about something or about any product is social media. People put forth what they feel, therefore their individual posts can be judged as a reflection of their personality, but how to judge a personality of a person based on social media posts and that too of say large data, say thousands, and produce analysis on it. In our studies, we specialize in using deep learning to build a classifier that can classify individuals according to their Myers-Briggs Type Index (MBTI) solely using textual content samples from their social media posts as an input. There are two reasons for developing such a classifier. First, due to the fact social media is so ubiquitous, one of these classifiers might have lots of statistics to degree character, permitting extra human beings to realize their MBTI character type, possibly extra reliably and faster. Our second incentive is the opportunity that our classifier can be more correct than the cutting-edge assessments, as evidenced with the aid of using the low retest mistakes price for character assessments executed with the aid of using expert psychologists. It may be possible to use our classification method to verify the accuracy of these first assessments, to give individuals more confidence in their results. In some cases, a text classification may be more comprehensive than a single personality assessment. [ABSTRACT FROM AUTHOR]

Historically, policymakers and practitioners relied exclusively on survey and census data to design and plan for assistive interventions; now, social media offer a timely and cost-effective way to reach out to populations otherwise unobserved. This study was designed to address the needs of a non-for-profit organisation to reach out to the young unemployed individuals in Italy with educational and job opportunities via communication channels that are more likely to appeal to younger generations. To this extend, we developed an ad-hoc Facebook application which administers questionnaires while gathering data about the Likes on Facebook Pages. Then, we developed a machine learning framework that successfully predicts the unemployment status of an unseen individual (.74 AUC). However, blindly delegating to the machine learning model the communication intervention may lead to digital discrimination on the basis of socio-demographic characteristics. Here, we propose a framework that aims to optimising both for the prediction performance as well as the most adequate fairness metric. Our framework is based on an adaptive threshold for gender, while we show that it can be expanded for other socio-demographic attributes and generalised for other interventions of assistive character. We present a doubly cross-validated setting that achieves out-of-sample stability and generalisability of results. We compare the behaviour of models that infer on different sets of data and provide an indepth discussion on the most predictive features, demonstrating that the "fairness through unawareness" approach does not suffice to achieve a fair classification since sensitive demographic information can be inferred not only via other sociodemographic attributes but also from behavioural digital patterns. Finally, we thoroughly assess the behaviour of the adaptive threshold approach and provide an in-depth discussion on the advantages but also the implications of such models offering actionable insights. Our results show that careful assessment of fairness metrics should be considered, primarily when AI models are employed for policymaking. [ABSTRACT FROM AUTHOR]

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Following the large-scale 2015–2016 migration crisis that shook Europe, deploying big data and social media harvesting methods became gradually popular in mass forced migration monitoring. These methods have focused on producing 'real-time' inferences and predictions on individual and social behavioral, preferential, and cognitive patterns of human mobility. Although the volume of such data has improved rapidly due to social media and remote sensing technologies, they have also produced biased, flawed, or otherwise invasive results that made migrants' lives more difficult in transit. This review article explores the recent debate on the use of social media data to train machine learning classifiers and modify thresholds to help algorithmic systems monitor and predict violence and forced migration. Ultimately, it identifies and dissects five prevalent explanations in the literature on limitations for the use of such data for A.I. forecasting, namely 'policy-engineering mismatch', 'accessibility/comprehensibility', 'legal/legislative legitimacy', 'poor data cleaning', and 'difficulty of troubleshooting'. From this review, the article suggests anonymization, distributed responsibility, and 'right to reasonable inferences' debates as potential solutions and next research steps to remedy these problems. [ABSTRACT FROM AUTHOR]

In this study, problematic social media use (PSU) was modeled using machine learning with artificial neural networks (ANN) and support vector machines (SVM). Fifteen predictor variables were examined in predicting PSU, including social media usage habits (frequency of daily social media use, history of social media usage, frequency of checking social media accounts, number of shares on social media, and number of social media accounts), desire for being liked, envy of the life of others, narcissistic personality traits (exhibitionism, grandiose fantasies, manipulativeness, thrill-seeking, narcissistic admiration and narcissistic rivalry), fear of missing out (FOMO), and online socialization. The present study comprised 309 (208 females and 101 males) university students. Using ANN and SVM, estimation was performed using k-folds (k = 5) cross validation. Results demonstrated a large relationship between predictors and PSU scores. Estimation rates with ANN and SVM were each.61. Then we used forward selection procedures to determine variable importance. We found that frequency of daily social media use, frequency of checking social media accounts, desire for being liked, exhibitionism and FOMO were the five most important variables in association with PSU severity. Finally, we analyzed the extent to which these five variables predicted PSU, finding that the estimate with five variables had a higher coefficient of estimation than with the fifteen variables. Prediction rates for the five variables were.62 using ANN and.63 using SVM. Results demonstrate that several psychological and social media-related variables were important in modeling PSU severity. [ABSTRACT FROM AUTHOR]

Sarcasm is a type of speech where people use positive words to convey a negative message. Recently, only a few research have been presented that focus on the entire spectrum of sarcasm in order to identify sarcastic sentiments present in both the image and the text. This work presents a novel firebug swarm optimization-based long short-term memory (FSO-LSTM) architecture to identify the sarcastic sentiments present in tweets. To identify the facial expressions of the users, the proposed FSO-based LSTM architecture is trained using the CK + dataset. The FSO algorithm is used to optimize the weighting factors of the LSTM architecture and also minimize the root-mean-square error (RMSE) and mean absolute error. The proposed method primarily attempts to address two challenging issues in sarcasm detection: the high number of false negatives and the fact that polite tweets often go undetected. The user's mood changes (sarcastic) such as rude, polite, furious, and impassive can be identified using the proposed model. Hence, the proposed classifier is capable of analyzing the behavior change of the user by collecting the past twitter account history. The efficiency of the proposed methodology is evaluated using different performance metrics such as accuracy, RMSE, confusion matrix, and loss. The proposed methodology offers an average classification accuracy of 97.25% classification accuracy when compared to the state-of-the-art approaches. [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]

Information sharing behavior and social link building behavior have shown strong correlation on social media. The aim of this article is to explore this correlation for simultaneously modeling and predicting sharing behavior in information diffusion sequences and linking behavior in social network, which correspond to information diffusion prediction and social link prediction problems. To achieve this goal, we propose a joint user representation learning model to characterize the two correlated behaviors in a shared latent space. The proposed model learns user representations via two maximum likelihood estimation objectives defined on observed information diffusion sequences and social network structure respectively and incorporates them in a unified framework. A multi-task learning algorithm is designed for efficient model optimization. Based on the learned representations, the model can be directly applied to predicting diffusion processes and inferring unobserved social links at the same time. We evaluate the proposed model on two real social media datasets with extensive experiments. The model consistently achieves significant improvements over the state-of-the-art approaches on diffusion prediction and link prediction tasks. The better robustness of our model in further ablation studies demonstrates that capturing the behavior correlation in the shared representation space is beneficial. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) techniques to uncover customer sentiment is not uncommon. However, the integration of sentiment analysis with research in design change prediction remains an untapped potential. This paper presents a method that uses social media sentiment analysis to identify opportunities for design change and the set of product components affected by the change. The method builds on natural language processing to determine change candidates from textual data and uses dependency modeling to reveal direct and indirect change propagation paths arising from the change candidates. The method was applied in a case example where 3665 YouTube comments on a diesel engine were analyzed. Based on the results, two engine components were recommended for design change with six others predicted as likely to be affected through change propagation. The findings suggest that the method can be used to aid decision quality in product planning through a better understanding of the change impact associated with the opportunities identified. [ABSTRACT FROM AUTHOR]

Today, social networks have created a wide variety of relationships between users. Friendships on Facebook and trust in the Epinions network are examples of these relationships. Most social media research has often focused on positive interpersonal relationships, such as friendships. However, in many real-world applications, there are also networks of negative relationships whose communication between users is either distrustful or hostile in nature. Such networks are called signed networks. In this work, sign prediction is made based on existing links between nodes. However, in real signed networks, links between nodes are usually sparse and sometimes absent. Therefore, existing methods are not appropriate to address the challenges of accurate sign prediction. To address the sparsity problem, this work aims to propose a method to predict the sign of positive and negative links based on clustering and collaborative filtering methods. Network clustering is done in such a way that the number of negative links between the clusters and the number of positive links within the clusters are as large as possible. As a result, the clusters are as close as possible to social balance. The main contribution of this work is using clustering and collaborative filtering methods, as well as proposing a new similarity criterion, to overcome the data sparseness problem and predict the unknown sign of links. Evaluations on the Epinions network have shown that the prediction accuracy of the proposed method has improved by 8% compared to previous studies. [ABSTRACT FROM AUTHOR]

Multimodal sentiment prediction poses a formidable challenge that necessitates a profound understanding of both visual and linguistic cues, as well as the intricate interactions between them. The current achievements of modern systems in this domain can plausibly be attributed to the development of sophisticated cross-modal fusion techniques. Nevertheless, such solutions often handle each modality equally, neglecting the discordant predictions arising from sentiment incongruity in unimodal sources, which may result in performance degradation in conventional extraction-fusion scenarios. In this work, we take a different route–introducing an extraction-estimation-fusion paradigm aimed at exploring more reliable multimodal representations under the supervision of unimodal sentiment prediction. To this end, we propose a C ross-modal I ncongrui T y p E rception NET work, named CiteNet , for multimodal sentiment detection. In CiteNet, we initially develop a cross-modal alignment module tailored to synchronize modality-specific representations through contrastive learning. Subsequently, with a refined cross-modal integration module, CiteNet can achieve a synergistic and comprehensive multimodal representation. In addition, we explore a cross-modal incongruity learning module from an information-theoretic perspective, capable of estimating inherent sentiment disparities by analyzing modal distributions. This incongruity score is then employed as a crucial factor in the adaptive fusion of unimodal and multimodal representations, culminating in enhanced accuracy in sentiment prediction. Experimental results on two datasets demonstrate that CiteNet outperforms prior methods by a significant margin of approximately 1%–11% in accuracy. [ABSTRACT FROM AUTHOR]

Social media is popular in our society right now. People are using social media platforms to purchase various products. We collected the data from various social media platforms. We analyzed the data for prediction of the consumer behavior on the social media platform. We considered the consumer data from Facebook, Twitter, Linked In and YouTube, Instagram, and Pinterest, etc. There are diverse and high-speed, high volume data which are coming from social media platform, so we used predictive big data analytics. In this paper, we have used the concept of big data technology to process data and analyze it to predict consumer behavior on social media. We have analyzed consumer behavior on social media platforms based on some parameters and criteria. We analyzed the consumer perception, attitude towards the social media platform. To get good quality of result, we pre-process data using various data pre-processing to detect outlier, noises, error, and duplicate record. We developed mathematical modeling using machine learning to predict consumer behavior on the social media platform. This model is a predictive model for predicting consumer behavior on the social media platform. 80% of data are used for training purposes and 20% for testing. [ABSTRACT FROM AUTHOR]

This study explored the chaotic properties of human emotions as expressed in social media and its implications for attainable forecasting horizons. Three human emotional states extracted from Twitter were analyzed using the nonlinear dynamics approach. The greatest positive Lyapunov exponent (LE) and 0-1 test methods were applied to a time series set consisting of over 25,000 data points reflecting the hourly recorded data of over 1.3 million tweets. The results suggest that the examined emotional time series data represent a nonlinear dynamical system with deterministic chaos properties. Therefore, by utilizing traditional linear methods of social media data analysis, one may not be able to fully understand and forecast critical transition trends over time or beyond a limited duration. It was concluded that the nonlinear dynamics approach is useful to determine a feasible forecasting horizon and to assess the prediction accuracy of social media data in general. [ABSTRACT FROM AUTHOR]

Nowadays, as an arena of politics, social media ignites political protests, so analyzing topics discussed negatively in the social media has increased in importance for detecting a nation's political risk. In this context, this paper designs and examines an automatic approach to forecast the daily outbreak of political risk from social media at a topic level. It evaluates the forecasting performances of topic features, investigated among the previous works that analyze social media data for politics, hidden Markov model (HMM)-based techniques, widely used for the anomaly detection with time-series data, and detection models, into which the topic features and the detection techniques are combined. When applied to South Korea's Web forum, Daum Agora, statistical comparisons with the constraints of false positive rate of < 0.1 and timeliness of < 0 show that, for accuracy, social network-based feature and, for sensitivity, energy-based feature give the best results but there is no single best detection technique for accuracy and sensitivity. Besides, they demonstrate that the detection model using Markov switching model with jumps (MSJ) with social-network based feature is the best combination for accuracy; there is no single best detection model for sensitivity. This paper helps make a move to prevent the national political risk, and eventually the predictive governance benefits the people. [ABSTRACT FROM AUTHOR]

• Exhibiting the relationship between users' personalities with their behavior in the interactions of social media platforms. • Predict the personality traits to explore in the social media platform using machine learning approach. • The current work comprises five stages: data collection, pre-processing, Feature extraction, and selection, using machine learning algorithm. • The proposed feature extraction method using Binary-Partitioning Transformer(BPT) with the TF-IGM approach better predicts personality traits. Predicting human behavior and personality from the social media applications like Facebook, Twitter and Instagram is achieving tremendous attention among researchers. Statistical information about the human thoughts expressed via status on social media is essential assets for research in predicting various human behaviour and personality. The current work mainly focuses on guessing user personality based on big five personality traits. An intelligent Sentence analysis model is built to extract personality features. In this article, a new Binary-Partitioning Transformer (BPT) with Term Frequency & Inverse Gravity Moment (TF-IGM) is proposed that identifies relationships among feature sets and traits from datasets. The proposed work outperforms the all feature extraction average baseline set on multiple social datasets. A maximum F1-score of 0.762 and accuracy of 78.34% on the Facebook dataset; 0.783 and 79.67%; on the Twitter dataset, 0.821; 86.84% on Instagram dataset is achieved. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract: The purpose of the study was to examine pre-service teachers'' sense of community, perception of collaborative learning, and perceived learning. Fifty pre-service teachers from two undergraduate ICT courses which incorporated blogs participated in this study. The data were obtained via three online questionnaires (Collaborative Learning scale, Sense of Community scale, and Perceived Learning scale) administered throughout Fall 2009–2010. The research questions were answered by using Pearson Product-Moment Correlation and multiple linear regressions. Results indicated that the pre-service teachers had positive feelings about the collaborative learning and perceived learning; also, they had moderate feelings related to sense of community in the classes which incorporated blogs. Additionally, to a great extent sense of community and to a much lesser extent of computer knowledge level were the predictors of explaining their learning perceptions. [Copyright &y& Elsevier]

This work proposes a multimodal approach with which to predict the regional Gross Domestic Product (GDP) by combining historical GDP values with the embodied information in Twitter messages concerning the current economic condition. This proposal is of great interest, since it delivers forecasts at higher frequencies than both the official statistics (published only annually at the regional level in Spain) and the existing unofficial quarterly predictions (which rely on economic indicators that are available only after months of delay). The proposed method is based on a two-stage architecture. In the first stage, a multi-task autoencoder is initially used to obtain a GDP-related representation of tweets, which are then filtered to remove outliers and to obtain the GDP prediction from the consensus of opinions. In a second stage, this result is combined with the historical GDP values of the region using a multimodal network. The method is evaluated in four different regions of Spain using the tweets written by the most relevant economists, politicians, newspapers and institutions in each one. The results show that our approach successfully learns the evolution of the GDP using only historical information and tweets, thus making it possible to provide earlier forecasts about the regional GDP. This method also makes it possible to establish which the most or least influential opinions regarding this prediction are. As an additional exercise, we have assessed how well our method predicted the effect of the COVID-19 pandemic. • A new multimodal two-stage approach for regional GDP prediction. • Forecasts for any geographic unit and at higher frequencies than official statistics. • Use of social media activity and historical information to anticipate official data. • Comprehensive experimentation is presented, including the effect of the COVID-19 crisis. • Results demonstrate that the approach is able to outperform the state of the art. [ABSTRACT FROM AUTHOR]

• Mine Twitter messages in the evening/midnight to predict next-day morning traffic. • Test the models on freeway networks in Pittsburgh. • Tweet information adds considerable prediction power to predict morning traffic. • The prediction also interprets travel demand characteristics. • Information up to midnight can be sufficient to well predict morning traffic on many roads. The effectiveness of traditional traffic prediction methods, such as autoregressive or spatio-temporal models, is often extremely limited when forecasting traffic dynamics in early morning. The reason is that traffic can break down drastically during the early morning commute, and the time and duration of this break-down vary substantially from day to day. Early morning traffic forecast is crucial to inform morning-commute traffic management, but they are generally challenging to predict in advance, particularly by midnight (called 'next-day morning traffic prediction' thereafter). In this paper, we propose to mine Twitter messages as a probing method to understand the impacts of people's work and rest patterns in the evening/midnight of the previous day to the next-day morning traffic. The model is tested on freeway networks in Pittsburgh as experiments. The resulting relationship is surprisingly simple and powerful. We find that, in general, the earlier people rest as indicated from Tweets, the more congested roads will be in the next morning. The occurrence of big events in the evening before, represented by higher or lower tweet sentiment than normal, often implies lower travel demand in the next morning than normal days. Besides, people's tweeting activities in the night before and early morning (by 5am) are statistically associated with congestion in morning peak hours. We make use of such relationships to build a predictive framework which forecasts morning commute congestion using people's tweeting profiles extracted by 5am. In most cases, the tweet information collected by the midnight before is sufficient to make good prediction for next-day morning traffic. The Pittsburgh study supports that this framework can precisely predict morning congestion, particularly for some road segments upstream of roadway bottlenecks with large day-to-day congestion variation, while its prediction performance being no worse than baseline methods on other roads. Through experiments, we demonstrate our approach considerably outperforms those existing methods without Twitter message features, and it can learn meaningful representation of demand from tweeting profiles that offer managerial insights. The proposed social media empowered framework can be a promising tool for real-time traffic management and potentially extended for traffic prediction at other times of day. [ABSTRACT FROM AUTHOR]

Sentiment Analysis is a channel by which automated feedback analysis can be processed effectively and efficiently. The reviews reachable are not only useful for customers buying a product or service but also the manufacturers and industrialists to formulate their production and marketing strategies as well as government organizations to access the views of the citizens. Recommender systems, market researches, and predictions on various social media platforms can be made more practical and comprehensive by sentiment analysis. It has evolved in numerous ways starting from the coarsely grained analysis. But when we need to find intricate details scrunched into a single statement or so, aspect-level sentiment analysis is the way. The uprising of deep learning approaches has opened doors in various applications, including Aspect-based Sentiment Analysis. These networks are computationally strong and can be trained easily. However, since they lag in recognizing semantic complexities, various Natural Language Processing techniques have been joined into the neural models. Researchers have been creative with inventing various novel models, combining a myriad of neural networks and attention mechanisms to improve aspect detection and sentiment polarity identification. The promise that this field provides because of its independent nature compels scientists to delve into the topic. In this work, fine grained analysis is not only processed for the aspect and aspect word detection but also for polarity and its intensity analysis. Our Contributions include the enriched input embedding with token, orientation, grammatical function, field and intensity components in the embedding stage, refined pattern extraction with convolutional kernels and improved performance using attention mechanism in the latter stages. Our experimental results reveal that, our procedural methodology has brought out an optimal enhanced performance compared to the near closer designs. [ABSTRACT FROM AUTHOR]

• The proposal of trend detection method from a stream of tweets. • Precise prediction & detection of real-time Twitter trends along with terms ranking. • Discusses applications of proposed research in business, industries and advertisers. Social media has recently become popular due to its vast applications. The common people all over the world uses its diverse channels to express personal views, experiences and opinions regarding diverse topics. Social media has revolutionized the way people interact and communicate with each other and overall, it has changed the methods and approaches in about all the aspects of life such as social issue, business, education, health, etc. Thus, sales and marketing departments of multinational industries are focusing on social media trends to analyze current trends and predict future trends by analyzing user generated content on Facebook, Flickr, Twitter, etc. However, the prediction process becomes challenging as the multiplicity of factors affect the popular elements in the social media content. This research paper aims to work on Twitter trend analysis and proposes a trend detection process over streams of tweets. The proposed approach detects the trending topics of the real-time Twitter trends along with ranking the top terms and hashtags. The paper further discusses the motivation for trend prediction over the social media; In addition to exploratory data analysis, the research paper explores the Term Frequency-Inverse Document Frequency (Tf-IDF), Combined Component Approach (CCA) and Biterm Topic Model (BTM) approaches for finding the topics and terms within given topics. In modern competitive world, this research provides investors, advertisers, industries and all the stakeholders. A detailed and comprehensive data analysis which may help them to focus their investment, area of work, marketing, and product. [ABSTRACT FROM AUTHOR]